Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Analysis of the Effects of Fire, 

Grazing, and the Distance to 

Wetlands on Grassland 

Bird Abundance

September, 2013

Report

Commission for Environmental Cooperation
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 2 / 189

 

Please cite as: 

CEC. 2013. Analysis of the Effects of Fire, Grazing, and the Distance to Wetlands on 

Grassland Bird Abundance. Montreal, Canada. Commission for Environmental 

Cooperation. 189pp. 

This report was prepared by Apropos Information Systems Inc. for the Secretariat of the 

Commission for Environmental Cooperation. The information contained herein is the 

responsibility of the author and does not necessarily reflect the views of the CEC, or the 

governments of Canada, Mexico or the United States of America. 

Reproduction of this document in whole or in part and in any form for educational or nonprofit purposes may be made without special permission from the CEC Secretariat, 

provided acknowledgment of the source is made. The CEC would appreciate receiving a 

copy of any publication or material that uses this document as a source. 

Except where otherwise noted, this work is protected under a Creative Commons 

Attribution Noncommercial-No Derivative Works License. 

© Commission for Environmental Cooperation, 2013 

Publication Details 

Publication type: Report 

Publication date: September, 2013 

Original language: English 

Review and quality assurance procedures: 

Final Party review: August, 2013 

QA12.01 

Disponible en français: (Sommaire de rapport) 

Disponible en español: (Resumen ejecutivo)

Cover photo: Brenda Dale 

For more information: 

Commission for Environmental Cooperation 

393, rue St-Jacques West, Suite 200 

Montreal (Quebec) 

H2Y 1N9 Canada 

t 514.350.4300 f 514.350.4372 

info@cec.org / www.cec.org 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Table of Contents

1. Introduction & Study Area ........................................................................................... 10

3. Existing research on Fire and Grazing........................................................................ 14

4. Analysis of EC Fire and Grazing Data ........................................................................ 17

4.1 Data preparation .............................................................................................. 17

4.2 Examination of data ......................................................................................... 18

4.3 Non-parametric Analysis .................................................................................. 18

4.4 Parametric Analysis ......................................................................................... 21

4.5 Results and Discussion ................................................................................... 24

4.5.1 Baird's Sparrow .................................................................................... 24

4.5.2 Brown-headed Cowbird ........................................................................ 26

4.5.3 Brewer's Sparrow ................................................................................. 28

 ...................................................................................................................... 29

4.5.4 Chestnut-collared Longspur ................................................................. 30

4.5.5 Clay-colored Sparrow ........................................................................... 32

 ...................................................................................................................... 33

4.5.6 Grasshopper Sparrow .......................................................................... 34

4.5.7 Horned Lark ......................................................................................... 36

4.5.8 Long-billed Curlew................................................................................ 38

 ...................................................................................................................... 39

4.5.9 Lark Bunting ......................................................................................... 40

4.5.10 Sprague's Pipit ................................................................................... 42

4.5.11 Upland Sandpiper ............................................................................... 44

4.5.12 Vesper Sparrow .................................................................................. 46

 ...................................................................................................................... 47

4.5.13 Western Meadowlark .......................................................................... 48

4.6 Summary ......................................................................................................... 50

5 Analysis of EC Wetland Distance Data ........................................................................ 51

5.1 Background ..................................................................................................... 51

5.2 Data preparation .............................................................................................. 52

5.2 Initial examination of data ................................................................................ 53

5.3 Non-parametric Analysis .................................................................................. 54

5.4 Parametric Analysis ......................................................................................... 55

5.5 Results & Discussion ....................................................................................... 56

5.5.1 Baird's Sparrow .................................................................................... 56

5.5.2 Brown-headed Cowbird ........................................................................ 57

5.5.3 Brewer's Sparrow ................................................................................. 58

5.5.4 Chestnut-collared Longspur ................................................................. 60

5.5.5 Clay-colored Sparrow ........................................................................... 62

5.5.6 Grasshopper Sparrow .......................................................................... 63

5.5.7 Horned Lark ......................................................................................... 65

5.5.8 Long-billed Curlew................................................................................ 67

5.5.9 Lark Bunting ......................................................................................... 69
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5.5.10 Sprague's Pipit ................................................................................... 71

5.5.11 Upland Sandpiper ............................................................................... 73

5.5.12 Vesper Sparrow .................................................................................. 75

5.5.13 Western Meadowlark .......................................................................... 77

6 Summary ..................................................................................................................... 79

7 Acknowledgments ....................................................................................................... 80

Bibliography .................................................................................................................... 81

Appendix A – Descriptive graphs of fire & grazing data .................................................. 85

A.1 Species Distributions....................................................................................... 85

A.2 Predictor Distributions ..................................................................................... 87

A.3 Species Outlier Detection Graphs (dot charts) ................................................ 89

A.4 Predictor Outlier Detection Graphs (dot charts) .............................................. 91

Appendix B – Correlograms from fire & grazing parametric analysis .............................. 92

B.1 Baird's Sparrow GLM Model Residuals ........................................................... 92

B.2 Brown-headed Cowbird GLM Model Residuals .............................................. 92

B.3 Brewer's Sparrow GLM Model Residuals ........................................................ 93

B.4 Chestnut-colored Longspur GLM Model Residuals ......................................... 93

B.5 Clay-colored Sparrow GLM Model Residuals ................................................. 94

B.6 Grasshopper Sparrow GLM Model Residuals ................................................. 94

B.7 Horned Lark GLM Model Residuals ................................................................ 95

B.8 Long-billed Curlew GLM Model Residuals ...................................................... 95

B.9 Lark Bunting GLM Model Residuals ................................................................ 96

B.10 Sprague's Pipit GLM Model Residuals .......................................................... 96

B.12 Upland Sandpiper GLM Model Residuals ..................................................... 97

B.12 Vesper Sparrow GLM Model Residuals ........................................................ 97

 .............................................................................................................................. 97

B.13 Western Meadowlark Model Residuals ......................................................... 98

B.13.1 Initial GLM .......................................................................................... 98

B.13.2 GLM with adjacency parameter ......................................................... 98

B.13.3 GLMM without adjacency parameter ................................................. 99

Appendix C – Wetland analysis variable description .................................................... 100

Appendix D – Descriptive graphs of wetland data ........................................................ 103

D.1 Species Distributions .................................................................................... 103

 ............................................................................................................................ 103

 ............................................................................................................................ 104

D.2 Predictor Distributions ................................................................................... 105

D.2.1 Wetland Area (# of 30m x 30m units) within Distances ...................... 105

D.2.2 Wetland Number within Distance Ranges ......................................... 108

D.2.3 Wetland Number within 10 km ........................................................... 110

D.2.4 Wetland Mean Distances within 10 km ............................................... 111

D.2.5 Wetland Number within Distance Ranges in Field .............................. 111

D.2.6 Wetland Number in Field ................................................................... 114

D.2.7 Wetland Distance in Field .................................................................. 115
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D.2.8 In Field Wetland Density .................................................................... 116

D.2.9 Other Measures ................................................................................. 117

D.3 Species Outlier Detection Graphs (dot charts) ............................................... 119

 ............................................................................................................................ 120

D.4 Predictor Outlier Detection Graphs (dot charts) ............................................ 121

D.4.1 Wetland Area (# of 30 x 30 pixels) within Distances .......................... 121

D.4.2 Wetland Number within Distance Ranges ......................................... 122

D.4.3 Wetland Number within 10 km ........................................................... 123

D.4.4 Mean Wetland Distances within 10km ............................................... 123

D.4.5 Wetland Number within Distance Ranges in Field ............................. 124

D.4.6 Wetland Number in Field ................................................................... 125

D.4.7 Wetland Distance in Field .................................................................. 125

D.4.8 In Field Wetland Density .................................................................... 126

D.4.9 Other Measures ................................................................................. 126

Appendix E – Wetland Non-parametric Results ............................................................ 126

Appendix F –Correlograms from Wetland Analysis....................................................... 139

F.1 Baird's Sparrow GLM Model Residuals ......................................................... 139

F.2 Brewer's Sparrow GLM Model Residuals ...................................................... 139

F.3 Chestnut-collared Longspur GLM Model Residuals ...................................... 140

F.3.1 Chestnut-collared Longspur GLM without adjacency measure .......... 140

F.3.2 Chestnut-collared Longspur GLM with adjacency measure ............... 140

F.4 Clay-colored Sparrow GLM Model Residuals ................................................ 141

F.5 Grasshopper Sparrow GLM Model Residuals ............................................... 141

F.6 Horned Lark GLM Model Residuals ............................................................... 142

F.7 Long-billed Curlew GLM Model Residuals ..................................................... 142

 ............................................................................................................................ 142

F.8 Lark Bunting GLM Model Residuals .............................................................. 143

F.9 Sprague's Pipit GLM Model Residuals .......................................................... 143

 ............................................................................................................................ 143

F.10 Upland Sandpiper GLM Model Residuals .................................................... 144

F.11 Vesper Sparrow GLM Model Residuals ....................................................... 144

F.11.1 Vesper Sparrow GLM without adjacency measure ........................... 144

F.11.2 Vesper Sparrow GLM with adjacency measure ................................ 145

F.12 Western Meadowlark Sparrow GLM Model Residuals ................................. 145

Appendix G – Correlations between wetland predictors ............................................... 146

Appendix H: Correlations between Wetland and Fire & Grazing Predictors ................. 188
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Análisis de los efectos que los incendios, el 

pastoreo y la distancia respecto a los humedales 

tienen en la abundancia de aves de pastizal 

Resumen ejecutivo 

La Comisión para la Cooperación Ambiental (CCA) contrató los servicios de Apropos para 

evaluar los efectos que los incendios, el pastoreo y la distancia de los humedales tienen en 

los niveles de abundancia de las aves de pastizal en el Área Nacional de Vida Silvestre 

(National Wildlife Area, NWA), ubicada dentro de los límites de la Base Suffield de las 

Fuerzas Armadas Canadienses (Canadian Forces Base, CFB). La información que nutrió 

este proyecto es aportación del ministerio de Medio Ambiente (Environment Canada, EC) y el 

Departamento de la Defensa Nacional (Department of National Defence, DND) de Canadá. 

En lo que respecta a los temas de incendios y pastoreo, se encontró que análisis simples 

realizados con anterioridad arrojaron resultados más claros, de hecho, que el método de 

modelos estadísticamente más complejos utilizado para los estudios encomendados por la 

CCA. Sin embargo, el trabajo efectuado para la CCA permitió abordar inquietudes respecto a 

que los factores de perturbación por incendios y pastoreo no estaban distribuidos de manera 

uniforme en todas las secciones ecológicas, por lo que cabe la posibilidad de que las 

respuestas a aspectos clave del hábitat —como los arbustos— estuviesen generando cierta 

distorsión. La repetición del análisis mediante el presente estudio tuvo el propósito de incluir 

aspectos de topografía y el tamaño de las partículas de suelo con miras a tomar en cuenta 

los efectos que ejercen en el hábitat. Al final, aunque se siguieron presentando los mismos 

problemas que entorpecieron el análisis inicial, resultó más fácil cuantificarlos y quedó claro 

que, con niveles de pastoreo e incendios moderados —similares a los patrones históricos—, 

el control de factores topográficos y de suelo en el hábitat reviste, para numerosas especies, 

mayor importancia que los factores de perturbación mismos. 

En el análisis de la distancia respecto a los humedales, la mayoría de las especies mostró 

respuestas más contundentes con relación a los arbustos, el suelo y los factores topográficos 

—que influyen en el tipo y cantidad de vegetación que crece dentro del área de estudio—, en 

comparación con las respuestas que generaron para las mediciones de los factores de 

perturbación, como conteos de materia fecal, número de incendios, años transcurridos desde 

el último incendio o distancia respecto a posibles fuentes de agua o bordes de humedales 

visibles. Esto sugiere lo siguiente: 

Es de suma importancia tomar en cuenta los factores que influyen en el crecimiento del 

hábitat incluso en un experimento de perturbaciones controladas, puesto que pocos sitios 

guardarán absoluta uniformidad en términos de suelos o topografía y, por ende, de la 

comunidad vegetal. 

En áreas semiáridas con pastoreo moderado, todo un conjunto de aves de pastizal puede 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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persistir durante periodos prolongados sin perturbaciones de incendios. 

Las respuestas de las aves respecto de los humedales varían en función del uso del suelo y 

el tipo de humedal de que se trata. 

La precipitación puede ser un importante indicador de la selección de hábitat y los niveles de 

abundancia de las aves de pastizal en el transcurso del tiempo y es muy probable que las 

medidas de grandes distancias a los humedales se hayan visto significativamente influidas 

por la dinámica de precipitaciones en general. La información sobre el tipo de humedal, per 

se, no aporta datos históricos sobre precipitaciones, y el tipo de humedal está apenas 

relacionado con el relieve y otros factores como la presencia de arbustos. El desempeño 

combinado o mixto que registran algunos de los modelos de humedal puede responder a lo 

complejo de estas dinámicas. 

Las distancias respecto a los humedales se calcularon con base en información de dos años 

similares, pero no como medidas directas de cada uno de estos años. Habría sido de gran 

utilidad limitar las mediciones de los humedales a escalas más pequeñas y, para reflejar 

mejor una parte de la compleja dinámica en juego, haber incluido algunas mediciones de la 

precipitación, como los efectos que la precipitación histórica ejerce en la profundidad de los 

depósitos de residuos vegetales sin descomponerse en la superficie del suelo. 

Los resultados con posibles implicaciones en términos de manejo son los siguientes: 

En pastizales semiáridos, los regímenes de incendios que se aproximan a los regímenes 

naturales en términos de frecuencia y alcance del fuego tal vez no ejerzan un efecto tan 

significativo al examinarse en una zona extendida o un periodo prolongado. Sus efectos 

inmediatos pueden ser catastróficos, pero no serán duraderos. 

La distancia respecto a vaguadas, fosas y ocasionalmente suelos húmedos y humedales a 

cielo abierto influyó en la abundancia de algunas especies en conformidad con su respuesta 

al pastoreo. Lo anterior sugiere que la distribución y la abundancia de las fuentes de agua 

pueden servir como herramienta de manejo de particular importancia para especies que 

requieren mayor cubierta o una cubierta irregular, con remanentes de pastizal (por ejemplo, 

el gorrión sabanero pálido [Ammodramus bairdii], la bisbita llanera [Anthus spragueii], el 

zarapito ganga [Bartramia longicauda] y, posiblemente, el gorrión zacatero coliblanco 

[Pooecetes gramineus]). 

La extensión del terreno influyó a menudo en la abundancia de la avifauna, aunque este 

resultado deberá interpretarse con cautela, puesto que los terrenos de mayor extensión se 

encontraron en zonas en su mayor parte sin arbustos, con suelos menos densos y una 

topografía poco accidentada. Un indicio más sólido que sustenta la conveniencia de los 

terrenos de gran extensión en ambientes semiáridos es el hecho de que algunas especies 

prefieren alejarse de las fuentes de agua del ganado, lo cual sólo es posible en terrenos de 

gran magnitud. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Analyse des effets du feu, du broutage, et de la distance 

observée relativement aux terres humides sur 

l’abondance des oiseaux de prairies 

Sommaire de rapport

La Commission de coopération environnementale (CCE) a mandaté Apropos pour évaluer les 

effets du feu, du pâturage et des zones humides sur l’abondance des oiseaux des prairies 

dans la réserve nationale de faune, à la Base des Forces canadiennes Suffield. Les données 

relatives au projet ont été fournies par Environnement Canada et le ministère de la Défense 

nationale. 

Dans le cas du travail relatif au feu et au pâturage, les analyses simples effectuées 

précédemment avaient en fait donné des résultats plus clairs que l’approche fondée sur un 

modèle plus statistiquement perfectionné qui a été utilisée pour les analyses exécutées sur 

demande de la CCE, mais le travail effectué par la Commission a aidé à traiter les 

préoccupations selon lesquelles les perturbations causées par le feu et le pâturage n’étaient 

pas réparties également entre les écosections (classification fondée sur l’aménagement du 

sol et la topographie), de sorte qu’il était possible que les réactions aux principales 

caractéristiques de l’habitat, telles que les arbustes, puissent créer un biais. La nouvelle 

analyse actuelle avait pour objet d’inclure la granulométrie du sol et la topographie afin d’en 

contrôler les effets liés à l’habitat. En fin de compte, les problèmes qui gênaient l’analyse 

initiale se sont encore une fois manifestés, mais ils étaient plus faciles à quantifier et il était 

évident que, lorsque le pâturage et le feu étaient modérés, et à des niveaux semblables aux 

modèles historiques, l’habitat qui tient compte du sol et de la topographie était plus important 

pour de nombreuses espèces que les facteurs de perturbation. 

Dans le cas de l’analyse de la distance des zones humides, la plupart des espèces ont réagi 

aux arbustes, au sol et à la topographie, qui influencent le type et la quantité de végétation 

qui pousse dans la zone d’étude; elles ont réagi plus fortement qu’elles ne l’ont fait aux 

mesures de la perturbation, telles que les comptes de pelotes de réjection, le nombre de feux, 

les années écoulées depuis le feu, ou la distance des sources d’eau possibles ou des bords 

de zone humide visibles. Cela semble indiquer ce qui suit : 

Il est très important de prendre en compte les facteurs qui influencent la croissance de 

l’habitat, même dans le cadre d’une expérience de perturbation contrôlée, car peu de sites 

sont parfaitement uniformes pour ce qui est du sol ou de la topographie et donc, de la 

communauté végétale. 

Dans les zones semi arides modérément pâturées, une suite complète d’oiseaux des prairies 

peut persister pendant longtemps, à condition de ne pas être perturbée par le feu. 

Les réactions des oiseaux aux zones humides doivent tenir compte de l’utilisation des terres 

et du type de zone humide. 

La précipitation peut être un important indicateur prévisionnel du choix de l’habitat et de 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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l’abondance des oiseaux des prairies au fil du temps, et il était probable que les mesures de 

grande distance des zones humides seraient fortement influencées par la dynamique 

générale des précipitations. En soi, l’information sur le type de zone humide ne fournit pas 

l’histoire des précipitations, et la corrélation entre le type de zone humide et les formes de 

relief, ainsi que d’autres facteurs tels que les arbustes, est faible. Les résultats mitigés fournis 

par certains des modèles de zone humide pourraient être le résultat de cette dynamique, qui 

est source de complications. 

Les distances de zone humide ont été calculées pour deux années semblables, mais non 

comme mesures directes pour chaque année. Il aurait peut être été avantageux de limiter les 

mesures de zone humide aux petites échelles et d’inclure certaines mesures de précipitation, 

telles que les incidences de l’histoire des précipitations sur l’épaisseur de la litière, pour 

mieux tenir compte d’une partie de la dynamique complexe qui est en cause. 

Les résultats ayant des répercussions possibles sur la gestion ont été les suivants : 

Dans les prairies semi arides, il se peut que les régimes des feux qui s’apparentent aux 

régimes naturels en ce qui a trait à la fréquence et à l’étendue du feu n’aient que peu de 

conséquences lorsqu’on les examine sur une grande aire ou au cours d’une longue période. 

Les conséquences immédiates peuvent être dramatiques, mais elles ne sont pas durables. 

La distance des cuvettes, des mares réservoirs et occasionnellement des terres humides et 

des marécages a influé sur l’abondance de certaines espèces de façon compatible avec la 

réaction de ces dernières au pâturage. Cela laisse entendre que la répartition et l’abondance 

des sources d’eau peuvent servir d’outils de gestion particulièrement importants dans le cas 

des espèces qui requièrent un abri plus important ou épars (le bruant de Baird, le pipit de 

Sprague, la maubèche des champs et peut être le bruant vespéral). 

Les dimensions du champ étaient souvent liées à l’abondance des oiseaux, mais ce résultat 

doit être considéré avec prudence, car les grands champs se trouvaient dans des aires en 

grande partie sans arbustes, au sol fin et à la topographie d’une grande douceur. Le fait que 

certaines espèces préfèrent être loin des sources d’eau destinées au bétail, ce qui ne peut 

avoir lieu que dans un grand champ, est la meilleure preuve de l’attrait des champs de 

grandes dimensions dans les environnements semi arides. 
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1. Introduction & Study Area 

The Commission for Environmental Cooperation (CEC) contracted Apropos to assess the 

effects of fire, grazing and wetlands on grassland bird abundance at Canadian Forces Base 

(CFB) Suffield. The data for this project were provided by Environment Canada (EC) and the 

Department of National Defence (DND). The original schedule for this project was to be 

completed in 2012, but unforeseen delays in acquiring the wetland data delayed the project. 

This report covers the analysis of the fire and grazing data in section 4 and the wetland data 

in section 5. The data and analysis were partitioned because not all input variables were 

available for all years. 

In 1994 and 1995 EC conducted a biophysical inventory of a portion of CBF Suffield in 

response to a plan for a proposed National Wildlife Area (NWA). The proposed NWA was off 

limits for military training but had pasture areas and until the winter of 1993/1994 had feral 

horses. This area had also experienced greater than average fires in modern time due to 

previous military training in the area. The NWA itself is an area along the eastern edge of CFB 

Suffield where the border follows the South Saskatchewan River and is treated as two parts, 

the North Block where there is more shrub and topographic variation and no cattle grazing, 

and the South Block which has ongoing grazing and more variety in terms of topographic 

profiles and habitat types. An overview map of the Suffield NWA is included as Figure 1. 

A number of research initiatives were undertaken including wetland studies (Adams et al. 

1998), vegetation studies (Adams et al. 1997), ungulate studies (Shandruk et al. 1998) and 

avifauna (Dale et al. 1999). For this project we used the bird data, grazing, fire and shrub data 

generated from those reports. 

The bird data used were gathered using 1000m spaced transects with 500m spaced locations 

to conduct 5 minute point counts (Dale et al. 1999). Birds were identified as either inside or 

outside a 100m radius up to 250m from the point count center. The original study examined 

bird abundance in relation to ecosites as well as fire and grazing using simple statistical 

methods. The authors of the 1999 study wished to revisit those data with more sophisticated 

statistical methods and this was in part the impetus for this study. In this analysis we used 

both inside and outside bird counts added together but did not use the ecosite information. 

The fire data were provided as a GIS layer (Adams et al. 1997). In the report it states that 

fires had been mapped since 1983 and it is assumed that the map data were provided by 

CFB Suffield, but no information about the methods used to create the fire maps was 

provided. 

The shrub data were collected as part of the vegetation cover mapping (Adams et al. 1997). 

The vegetation mapping was done with both detailed sampling of the entire NWA using 

regularly spaced survey locations and infrared photo interpretation. From photos, polygons 

were created and assigned to classes and on the ground visits using microplots were used to 

estimate shrub and tree cover. 

Figure 1: Location of the Suffield NWA 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Pellet counts are a rapid and economical method for identifying general habitat use by cattle 

and other ungulates (Shandruk et al. 1998). Shandruk et al. (1998) completed 925 pellet 

group transects within the NWA. These transects were spaced 500 - 1000m apart and each 

consisted of a strip plot 2m by 100m which was traversed by two observers to determine 

which pellets were within the plot and how many and what type of pellets they were. For this 

study, we choose locations where both pellet counts and bird counts co-occurred. Shandruk 

et al. (1998) note that the stocking rates within the NWA were moderate, which limited the 

range of grazing effects we could explore. It is also worth noting that older pellets were 

included in the counts so that these data represented both recent use and grazing history. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 2: South Block of NWA 

Beginning in 1999, the Suffield Grazing Advisory Committee approved an increase in grazing 

intensity in the community pasture which included part of the NWA and an adjacent area as 

shown in Figure 2. EC had concerns that an increase in grazing might have some influence 

on the bird community, so monitoring was conducted from 2000 through 2005 on a subset of 

the points used in the biophysical work done in 1994 and 1995 in the South Bock of the NWA. 

Some new points were added to cover the new areas using the same field methods as 

before. This work was able to document high year to year variability in bird numbers and 
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showed no adverse effects with the change to the minimal stocking rate. The stocking rates 

for the NWA continued to be well regulated and adjusted yearly when needed following fall 

site inspections. 

From 2006 through 2009, subsets of points in the North Block were also surveyed. The 2000 

through 2006 data were previously used to create and validate habitat models using variables 

that included remote sensing based indices, soil particle size or soil coarseness, topography 

and precipitation (Wiens et al. 2008). 

Another change to the area began in 1999 with the infill drilling of natural gas wells. This was 

initiated in 1999 prior to the designation of the area as a National Wildlife Area. The 

biophysical and grazing monitoring data were used to test for effects of gas well density and 

this information was used in hearings to consider further drilling proposed by EnCana (CEAA) 

and later published with additional information (Dale et al. 2009). Gas well density data and 

shrub data from the biophysical were used to improve the model for Sprague’s Pipit 

developed in Wiens et al. (2008) to identify Critical Habitat protected under the Canadian 

Species at Risk Act. Long term data sets are valuable and although this one has been used 

for many analyses, it can still provide more useful information. Distance to water is an 

example of that. During the biophysical analyses, the authors speculated that larger fields 

were ideal for a variety of birds because the grazing was likely to be more uneven due to 

varying distances to water sources for cattle (Dale et al. 1999). At that time, no layer with 

wetland and well location data was available. The data are now available and thus, the 

response of birds to distance to water at Suffield can be tested. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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3. Existing research on Fire and Grazing 

A variety of papers on burning and grazing were reviewed to guide the analytical approach 

take to this data set. This section provides a very brief review of that material. 

The main points from the reviewed literature are summarized in Table 1. 

Table 1: Bird specific responses to fire and grazing

Species Effect Habitat Associations Citation

Baird's Sparrow 

(Ammodramus 

bairdii) 

Fire reduced shrub 

which increased 

habitat suitability 

+ grass 

+ native grass 

- shrub 

- visual obstruction 

Madden et al. 2000 

Grazing in drought 

reduces abundance 

- shrub Dale 1983 

Heavy grazing reduced 

numbers 

+ veg height & 

thickness 

(drier site than in 

Madden et al. 2000) 

Kantrud 1981, Davis et al. 

1999 

Brewer's 

Sparrow 

(Spizella 

breweri) 

Shrub dependent so 

reduction of shrub is 

negative 

+ shrub Rotenberry et al. 1999 

Brown-headed 

Cowbird 

(Molothrus ater) 

No clear response to 

grazing 

 Kantrud 1981, Dale 1983 

No response to fire Grant et al. 2010 

Chestnutcollared 

Longspur 

(Calcarius 

ornatus) 

Early seral stage 

preferred so areas with 

recent fires or heavy 

grazing likely preferred

+ bare ground 

- forbs 

Fritcher et al. 2004 

Preferred grazed to 

ungrazed 

+ bare ground 

litter 

vegetation height and 

thickness 

Dale 1983 

Increased with grazing 

intensity 

 Kantrud 1981 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Table 1 continued 

Species Effect Habitat Associations Citation

Clay-colored 

Sparrow 

(Spizella pallida)

Fire reduced nest 

abundance year after 

fire. Recovered in 3 

years 

+ litter depth 

+ standing dead 

(did not measure 

shrub) 

Grant et al. 2011 

Grazing had no effect 

on abundance 

+ shrub, grass cover, 

litter depth 

Dale 1983 

Decreased in heavily 

grazed 

 Kantrud 1981 

Grasshopper 

Sparrow 

(Ammodramus 

savannarum) 

Lower abundance in 

recently burned plots 

+ shrub Bock and Bock 1992 

Preference for later 

seral stages, thus 

areas with more 

temporally distant fires

 Fritcher et al. 2004 

Fire effect not clear + exotic grasses 

- visual obstruction 

Madden et al. 2000 

Fire had little effect Grant et al. 2011 

Reduced abundance 

with heavy grazing 

 Kantrud 1981 

Horned Lark 

(Eremophila 

alpestris) 

Higher abundance in 

recently burned plots 

- grass 

- litter 

Bock and Bock 1992 

Early seral stage 

preferred so areas with 

recent fires or heavy 

grazing likely preferred

- litter depth Fritcher et al. 2004 

Increased with heavy 

grazing 

 Kantrud 1981 

Prefers grazed to 

ungrazed 

 Dale 1983 

Lark Bunting 

(Calamospiza 

melanocorys) 

Highest in moderate 

grazing 

 Kantrud 1981 

Heavy grazing 

detrimental 

Fire that reduces shrub 

detrimental 

 Shane 2000 
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Table 1 continued

Species Effect Habitat Associations Citation

Long-billed 

Curlew 

(Numenius 

americanus) 

Recent burn and 

heavy grazing 

associations 

+ spatial variation in 

structure suggests a 

preference for larger 

grazing areas 

Derner et al. 2009 

Sprague's Pipit 

(Anthus 

spragueii) 

Fire reduced cover 

which has a positive 

effect 

- visual obstruction Madden et al. 2000 

Preferred ungrazed to 

grazed 

 Dale 1983 

No response to 

grazing intensity 

 Kantrud 1981 

Heavy grazing reduces 

abundance 

 Davis et al. 1999 

Upland 

Sandpiper 

(Bartramia 

longicauda) 

Avoid grazing for 

nesting but forage in 

grazed areas 

+ spatial variation in 

structure suggests a 

preference for larger 

grazing areas or 

mosaic of grazed and 

ungrazed areas 

Derner et al. 2009 

Preferred heavy 

grazing 

 Kantrud 1981 

Vesper Sparrow 

(Pooecetes 

gramineus) 

Higher abundance in 

recently burned plots 

- grass 

- litter 

Bock and Bock 1992 

Highest in moderate 

but no clear response 

to grazing intensity 

 Kantrud 1981 

Western 

Meadowlark 

(Sturnella 

neglecta) 

No clear response to 

grazing 

 Kantrud 1981, Dale 1983 

Fire had little effect Grant et al. 2011 

Researchers in the field generally agree that fire and grazing are important management 

mechanisms to be employed by land managers. Fuhlendorf et al. 2006 argued that burning 

and grazing mimic natural cycles and therefore merit re-introduction in their study areas. 

However, Curtin in 2002 argued that grazing plans need to consider ongoing environmental 

effects so that areas are not over-grazed in very dry years. McGranahan et al. in 2012 argued 

that these tools need to be used together to create distinct patches. Wendtland and Dodd in 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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1990 suggest that the pre-settlement fire cycle in the USA was about 15 to 30 years long and 

that high-intensity fires have greater environment benefits. Roberts et al. in 2012 argued that 

during a fire, the species composition changes but returned to normal within a few years. 

Finally, Wakimoto et al. 2004 reviewed historical literature and suggested fire cycle repeat 

frequency ranged from 5 to 12 years. 

A largely agreed upon theme in the literature was that drier locations would have longer fire 

intervals, which is an important consideration in applying recommended intervals to areas 

outside those for a particular area in which a study was conducted. Recommended stocking 

rates vary with soil and moisture because these influence forage productivity so again 

recommendations for grazing to benefit avian management are likely to vary. 

We also reviewed the methods of analysis employed which generally involved the use of 

standard non-parametric and parametric methods. 

4. Analysis of EC Fire and Grazing Data 

4.1 Data preparation 

Data files from EC in the format of spreadsheet files and GIS files of burn, bird and pellet 

count locations were integrated as follows: 

1. Spatial data were imported into GRASS GIS 6.4 (GRASS Development Team, 20102) 

using PostgreSQL (The PostgreSQL Global Development Group, 2012) for attribute 

management. 

2. For soils AGRASID data from the region were also imported into GRASS GIS 6.4 and SQL 

queries were constructed to calculate the Mean Relative soil Particle Size (MRPS) as per 

Wiens et al.2008 and the script mrps_creation.sql found in the deliverable_2/scripts folder. 

Similarly a 30m pixel size DEM provided by EC was used to calculate a topographic 

roughness index based on the differences between plan and surface areas using the GRASS 

extension script r.roughness.window.area2 available on the GRASS GIS Wiki in the 

extensions section. Topographic roughness is equivalent to the flatness index described in 

Wiens et al. 2008 with values near 1 for flat areas and areas with increasing topographic 

variation having values greater than 1. 

3. Pellet count sites were then used with the v.sample.buffer2 script (also available on the 

GRASS GIS Wiki site) was used to sample the mean values of the topographic roughness 

and MRPS values within 250 metres of the site centroids to match the areas included in the 

point counts. 

4. Shrub data were derived from a Canadian Wildlife Service habitat classification which 

divided the Suffield NWA into a series of habitat class polygons with a unit-less mean shrub 

value ranging from 0 to 30. This layer was converted to a raster and then using the 

v.sample.buffer2 script the mean of the shrub value within 250m of the site centroids was 

used as a shrub measure. 

5. Lastly a Fire Index value as calculated using the number of years since a fire and the 

number of fires information provided by CWS. The formula used as based on the work of 

Madden et al. (1999) as follows: 
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 Fire Index = Number of Fires / Years Since Last Fire 

In cases where the fire had been in the current year prior to our sampling, the Years Since 

Last Fire was set to 0.5 as it seemed ecologically reasonable that with little or no time for 

recovery the effect of a fire on habitat structure for the species in questions would be twice as 

powerful than if the fire had occurred in the previous year. For example, if there were no fires 

the index would be 0, if there was 1 fire last year the index would be 1 and if there have been 

5 fires and the last one was in the spring of the sample year the index would be 10. 

Using SQL queries, bird survey locations were merged with bird site list using the EC bird 

point count “siteid” variable. Pellet counts were also linked in this fashion. Burn data however, 

were linked by line and plot to pellet counts which thus enabled linkage to the bird count data. 

The bird data were cross-tabulated to create a bird records table with one species per 

column. 

These final data were then linked and exported in CSV format for analysis in R (R Core Team, 

2012). Initial analysis indicated that there may be some merit in also examining the bird 

observations as presence data, so the original data was re-accessed to also create bird 

presence variables. These queries are included in the deliverable_2/scripts folder script 

create_species_tables.sql. 

4.2 Examination of data 

First, the distribution of species and predictors were assessed through the use of histograms. 

Species with less than 20 observations were removed from consideration. Remaining species 

and predictors were then assessed for outliers and collinearity. Graphs from this initial 

assessment work are included in Appendix A. Collinearity tests showed that species were 

sufficiently independent so that one species could not be used as a proxy for another. The 

relationships between the predictor variables showed independence except between the two 

fire measures had a Kendall tau of -0.76 (p < 0.001). 

An assessment of year to year differences for species and predictors using a Wilcox test to 

assess similarity in distributions was conducted. Although most predictors and species 

showed no differences, a few did so it was decided that subsequent parametric analyses 

would include year to account for the variation and provide a consistent method across all 

species. The inclusion of year also allowed for variation in precipitation other unknown 

external variations between years that could not otherwise be accounted for. 

Outlier detection noted two records in the topographic roughness measures and two records 

for species BCHO that had outliers. Removing these records resulted in the total sites across 

the two years being reduced from 413 to 409 records. 

4.3 Non-parametric Analysis 

We calculated the Kendall rank correlation coefficient to examine the relationship between fire 

and grazing patterns in relation to topography and soil coarseness. This test was chosen 

because it makes no assumptions about the distribution of the variables being compared. 

These results are summarized in Table 2. 

The disturbance vs. structural measures might suggest that horses and cattle respond 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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differently to topographic roughness and soil coarseness. The differences observed between 

horses and cattle are presented in Table 2; however differences are largely because most of 

the horses were limited to the north with greater topographic variation, coarser soil and more 

shrub and the cattle were restricted to the south block with less shrub, finer soils and fewer 

hills. There was no measurable difference in fire patterns in relation to soil or topography. 

Table 2: Kendall Rank correlations between predictors (bold for p <= 0.01)

Measure Statistic Topographic Roughness MRPS 

Shrub tau -0.062 0.298

p-value 0.061 < 0.001

Years Since Fire tau -0.032 -0.035

p-value 0.372 0.344

Total Burns tau 0.002 0.063

p-value 0.962 0.132

Fire Index tau 0.046 -0.001

p-value 0.215 0.974

All Pellets tau -0.184 0.010

p-value < 0.001 0.786

Old Cattle Pellets tau 0.024 -0.310

p-value 0.522 < 0.001

New Horse Pellets tau -0.052 0.155

p-value 0.182 < 0.001

Old Horse Pellets tau -0.155 0.289

p-value < 0.001 < 0.001

We also calculated the Kendall rank correlation coefficient for pairs of species and predictors. 

Tests against new horse pellets were restricted to 1994 data, as horses were removed from 

Suffield at the beginning of 1994. The correlation results are summarized in Table 3. 

In this simple test, topographic roughness and years since fire were in most cases weakly or 

not significantly correlated with species abundance. The fire index however, took into account 

both the years since fire and total burns and was thus deemed the most useful management 

and analytical metric and was used for subsequent analyses. In the case of topographic 

roughness, it continued to be used on the possibility that although weak in on its own, it might 

provide useful additional information in regression analyses especially given it had proved a 

useful predictor in habitat models (Wiens et al. 2008). 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Table 3: Kendall Rank Correlations between species and predictors (bold for p <= 0.01)

Species 

(values are 

tau & p-value)

Shrub All 

Pellets 

Old 

Cattle 

Pellets 

New 

Horse 

Pellets 

Old 

Horse 

Pellets 

MRPS Topo Total 

Burns 

Years 

Since 

Fire 

Fire 

Index 

Baird's 

Sparrow

-0.341 0.158 0.275 -0.120 -0.122 -0.069 -0.129 -0.122 0.100 -0.100

< 0.001 < 0.001 < 0.001 0.062 0.004 0.091 0.001 0.011 0.021 0.032

Brownheaded 

Cowbird

0.279 -0.028 -0.165 0.034 0.158 0.111 -0.035 0.168 -0.055 0.087

< 0.001 0.500 < 0.001 0.601 < 0.001 0.008 0.383 < 0.001 0.212 0.056

Brewers 

Sparrow

0.174 -0.069 -0.239 0.204 0.170 0.256 -0.014 -0.119 0.065 -0.130

< 0.001 0.091 < 0.001 0.001 < 0.001 < 0.001 0.729 0.013 0.133 0.004

Chestnutcolored 

Longspur

-0.376 0.259 0.495 -0.211 -0.264 -0.214 -0.100 0.010 -0.039 0.072

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.010 0.841 0.362 0.103

Clay-colored 

Sparrow

0.386 -0.046 -0.251 0.207 0.199 0.176 -0.070 0.065 -0.006 -0.006

< 0.001 0.262 < 0.001 0.001 < 0.001 < 0.001 0.078 0.180 0.892 0.892

Grasshopper 

Sparrow

-0.174 0.055 0.059 -0.045 0.032 0.124 -0.030 -0.275 0.275 -0.254

< 0.001 0.161 0.168 0.476 0.426 0.002 0.426 < 0.001 < 0.001 < 0.001

Horned Lark -0.392 0.092 0.264 0.173 -0.181 -0.225 0.029 -0.173 0.071 -0.085

< 0.001 0.018 < 0.001 0.004 < 0.001 < 0.001 0.437 < 0.001 0.082 0.046

Long-billed 

Curlew 

-0.172 0.022 0.146 -0.019 -0.106 -0.173 0.013 0.082 -0.163 0.144

< 0.001 0.602 0.001 0.776 0.014 < 0.001 0.750 0.094 < 0.001 0.002

Lark Bunting 0.330 -0.135 -0.271 0.030 0.104 0.237 -0.086 0.250 -0.186 0.207

< 0.001 < 0.001 < 0.001 0.635 0.014 < 0.001 0.027 < 0.001 < 0.001 < 0.001

Sprague's 

Pipit

-0.437 0.075 0.288 -0.151 -0.174 -0.211 0.012 -0.245 0.254 -0.186

< 0.001 0.056 < 0.001 0.016 < 0.001 < 0.001 0.748 < 0.001 < 0.001 < 0.001

Upland 

Sandpiper

0.225 -0.047 -0.090 0.056 0.047 0.107 0.030 0.048 -0.008 0.037

< 0.001 0.255 0.048 0.379 0.279 0.009 0.451 0.323 0.859 0.416

Vesper 

Sparrow

0.312 -0.063 -0.298 0.087 0.271 0.186 0.005 0.078 -0.049 0.030

< 0.001 0.110 < 0.001 0.163 < 0.001 < 0.001 0.886 0.092 0.237 0.493

Western 

Meadowlark 

0.032 0.023 -0.152 0.169 0.169 0.124 -0.039 -0.176 0.112 -0.172

0.388 0.540 < 0.001 0.004 < 0.001 0.001 0.284 < 0.001 0.005 < 0.001

As part of our interest is total grazing history, we also tried combining all pellets. In most 

cases bird abundances had opposite correlations to horse and cattle grazing measures so 

combining all pellets negated most detectable effects. The differences as noted above may be 

a result of the differences between the north where most of the horses had been and the 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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south with cattle. The cattle and horse differences may also be confounded by the north block 

having much more shrub. In subsequent analyses both the merged pellets and the individual 

pellet counts were assessed with the shrub measure which was generally more strongly 

correlated with bird abundance than any other measure. 

4.4 Parametric Analysis 

The non-parametric analysis results demonstrated that although relationships between the 

species abundance and most the predictors, grazing and fire in particular, existed, these were 

not strong predictors on their own. Considering that the data were not explicitly collected for 

this purpose caution was warranted so a p-value of <=0.01 was used as an indicator of 

statistical significance. For results that made ecological sense p-values 0.05 to 0.01 were 

treated as weakly significant. 

The survey data were collected along regular spaced stations on transect lines so spatial 

auto-correlation had to be taken into consideration to ensure that any patterns observed were 

not artifacts of the spatially correlated nature of landscapes. This dynamic in relation to bird 

habitat selection is usually manifest as the simple observation that individuals of one species 

tend to be found in proximity to others of that same species. In the case of transect based 

point counts, this means that if you observe species x at station y in a transect, the likelihood 

of observing species x and station y+1 is greater than if you had not observed species x at 

station y. This obvious state of affairs can create problems in modeling species habitat 

selection and introduce the possibility of Type-1 error, detecting a phenomenon that is not 

actually true. In statistical terms, this spatial correlation means that spatially correlated data 

tend to reduce variance in the data set which in turn can produce artificially low p-values and 

potentially false conclusions (Lennon 1999). 

When a model is created, one of the tests of the model is to examine the distribution of the 

model residuals; effectively the input variance not explained by the predictors. If the residuals 

show no pattern, then the modeler knows that the model was adequately defined (Zuur et al. 

2009). However, if the model residuals display spatial, temporal or other forms of correlation, 

then important predictive factors are missing from the model. Spatial or other forms of autocorrelation are not in themselves, bad and are to be expected in ecological observation data. 

The question before ecologists is can we specify models that adequately explain the variation 

observed. 

Although some methods in spatial statistics are well established, the means to include 

effective adjustment of spatial dynamics in regression models in ecology is still evolving. 

Dormann et al. (2007) provide an excellent overview of statistical methods to manage spatial 

auto-correlation. For data with a Poisson distribution, count data being the classic example, 

Generalized Linear Mixed Models, along with Generalized Estimating Equations and Spatial 

Eigenvector Mapping, are all methods that are known to perform well. 

A less mathematically sophisticated but also effective method occasionally used with point 

count data is considering the presence or absence of the same species in adjacent stations 

along a transect. The downside of this approach is that it provides no ecological 

understanding of why the birds are clustering together and it also prevents the creation of 

habitat selection maps from the resulting models. For the purposes of this project these 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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aforementioned limitations were not a problem, as the purpose here was to test and 

document the effect size of disturbances and wetland distribution on species abundance not 

to predict responses in new areas. It is worth noting that for this project, the purpose of the 

models was only examine the relative importance of various predictor variables so the data 

were not split into training and validation sets and model validation procedures, other than the 

use of stepwise AIC model simplification, were not undertaken. 

Initially, we chose to work with Generalized Linear Mixed Models, but later realized that this 

made creation of effects graphs more difficult so we switched to Generalized Linear Models 

with this simpler adjacency variable as a means to handle spatial correlation in the models. 

Species adjacency was calculated by assessing if the same species was present in the one 

or two adjacent count stations. If the same species was not present in adjacent stations, then 

the value of this variable was 0. If the same species was adjacent in an adjacent station then 

a value of 1 was assigned. 

In our first round of analysis (in our draft report) we created normalized (z-score) values for all 

predictors so that we could more easily compare the relative importance of different predictors 

in models; these variables were given the prefix of a letter s and an underscore character. 

Later, when we considered the importance of reporting effects in the units of the predictor 

variables we abandoned this approach and developed subsequent models using the original 

untransformed variables. As such, it is important that predictor variable coefficients in model 

results not be compared against each other; examination of their p-values and effect graphs 

will be more informative and reliable in assessing their relative importance. 

In our initial analysis for the draft report we lacked a shrub layer and had used a north / south 

factor variable to account for the marked difference in shrub distribution in the NWA. For the 

second round of analysis we obtained a shrub layer from EC based on their habitat 

classification work. 

Zuur et al. (2009) provide an excellent overview of mixed effect models for a variety of 

different data types including Poisson distributed data. After all initial data evaluation and 

removal of outliers for the non-parametric analysis described in section 4.3 we adapted the 

methods outlined from Zuur et al. (2009) and used the following procedure during for the draft 

report: 

1. An initial model format was developed to account for known factors and the available 

predictor variables. 

2. The initial models for each species were expressed as Poisson distributed Generalized 

Linear Models. If both the regular and squared term had the same direction, the 

models were simplified to the linear term. 

3. This initial model was then simplified using the step function in R which applies 

backward AIC selection. This procedure would then eliminate non-linear measures if 

they were not effective. 

4. The residuals from the simplified model were then assessed for spatial correlation 

using a spline correlogram as implemented in the R ncf library. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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5. If correlation was found in the residuals, the species observations were checked for a 

similar pattern, also using a spline correlogram. 

6. If the source data and residuals displayed a similar correlation pattern it was assumed 

that the correlation in the residuals was a reflection of the source data and not a result 

of miss-specified model parameters and that use of a GLMM was needed to account 

for the spatial correlation. 

7. A GLMM model, clustering site observations by transect using the R glmmML package 

was used and again residuals were checked for spatial correlation using a spline 

correlogram. 

8. If the resulting residuals showed no sign of spatial correlation the new model was then 

used to construct a series of sub-models which were assessed using AIC weights and 

evidence ratios. 

9. The selected model or models were then used to describe the observed relationships 

between species abundance and the predictors in question. 

In this final report our procedure was modified and was as follows: 

1. An initial model format was developed to account for known factors and the available 

predictor variables. During this initial formulation for those variables we thought may 

not have a linear response we included them as quadratic terms to assess if non-linear 

responses produced more effective models. 

2. The initial models for each species was expressed as a Poisson distributed 

Generalized Linear Model. If both the regular and squared term had the same 

direction, the models were simplified to the linear term. We also calculated and 

included a species adjacency predictor variable. 

3. This initial model was then simplified using the step function in R which applies 

backward AIC selection. This procedure would then eliminate non-linear measures if 

they were not effective. 

4. This residuals from the simplified model were then assessed for spatial correlation 

using a spline correlogram as implemented in the R ncf library. 

5. If correlation was found in the residuals, the species observations were checked for a 

similar pattern, also using a spline correlogram. 

6. If the source data and residuals displayed a similar correlation pattern it was assumed 

that the correlation in the residuals was a reflection of the source data and not a result 

of miss-specified model parameters and that use of a GLMM was needed to account 

for the spatial correlation. This was only necessary in the case of Western Meadowlark 

and steps 7 through 9 from the above procedure were followed. 

7. The final model was selected and effects graphs were plotted using the R “Effects” 

library version 2.2-3. Om the case of Western Meadowlark the GLM and GLMM model 

parameters and p-values were comparable so the effects from the GLM model were 

produced to assist the reader in understanding the results. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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4.5 Results and Discussion 

4.5.1 Baird's Sparrow 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models led to a common model which 

was then tested for, and found not to have any spatial auto-correlation. The final model 

showed other features were more important predictors than fire and grazing history (Table 4). 

Effects graphs are included in Figure 3. 

Table 4: Baird's Sparrow Poisson GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) 368.14269 113.08550 3.255 0.00113

shrub^2 -0.05049 0.01287 -3.922 8.79e-05

topo -369.04988 112.98548 -3.266 0.00109

adjacency 0.96655 0.21529 4.490 7.14e-06

The correlations in Table 4 are consistent with the previous simple analysis that showed a 

positive response to moderate grazing and a negative response to fire (Dale et al. 1999). 

The inclusion of shrub as a squared term in our final model and the effect graphs clearly show 

that when shrub values are in the higher two thirds of what was assessed in the model, 

habitat is no longer suitable for Baird's Sparrow. Similarly increases in topographic roughness 

reduces habitat suitability which is consistent with Wiens et al. 2008. The wider confidence 

intervals around the topography measure create some reasonable doubt as to the true 

strength of the response, but it is clearly not a positive relationship. 

Other studies have concluded that habitat suitability for Baird’s Sparrow recovers quickly 

following fire and may even improve if shrub was reduced (Madden et al. 2000) and since 

only about 10% of our study sites had been subjected to fire within the previous 3 years it is 

perhaps not surprising that the model did not include fire variables even though the species 

showed a negative response to fire variables (Table 2, a positive correlation to time since fire 

is a negative response to fire) in this analysis and a negative response in the simpler analysis 

undertaken in Dale et al. 1999. 

Grazing as a quadratic was included in the initial models because it is consistent with the 

species preference for moderate grazing. Other studies have found the species avoids heavy 

grazing or may respond negatively to grazing in drought (Kantrud 1981, Dale 1983, Davis et 

al. 1999) but given the moderate stocking rates, even the areas in the NWA with the highest 

pellet counts do not represent heavy grazing. This light to moderate grazing provides a 

reasonable explanation why grazing had little explanatory power and was removed during the 

stepwise simplification of the model. 
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Figure 3: Baird's Sparrow Fire & Grazing Model Effects 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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4.5.2 Brown-headed Cowbird 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models that were subsequently compared with a global model using AIC weights. AIC 

evidence ratios clearly indicated the one model which was then tested for and found to have 

no spatial auto-correlation. The final model is summarized in Table 5. Effects graphs are 

included in Figure 4. 

The results show very weak correlations which is clearly illustrated by the very wide 

confidence intervals seen in Figure 4. What can be reasonably concluded is that shrub has a 

positive effect on the habitat selection and abundance of this species in the NWA, but the 

strength of that relationship is unclear. Display groups prefer perches and female cowbirds 

require a perch to observe other nesting birds in order to identify a host nest to place their egg 

in. Shrubs could serve as such a perch although Davis (2004) found they preferred areas with 

dispersed shrub rather than a lot of shrub which might fit with the quadratic response in both 

the initial and final model. Neither fire nor grazing remained in the models after AIC stepwise 

selection suggesting that if they do have an effect, it is sufficiently small to be overwhelmed by 

other factors. Previous studies (Table 1) also found no clear response to grazing intensity or 

fire. 

Table 5: Brown-headed Cowbird GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) -6.38332 1.26109 -5.062 4.15e-07

shrub 0.72238 0.30690 2.354 0.0186

shrub^2 -0.02736 0.01747 -1.566 0.1173

adjacency 0.85644 0.42079 2.035 0.0418
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 4: Brown-headed Cowbird Fire & Grazing Model Effects 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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4.5.3 Brewer's Sparrow 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models that were subsequently compared with a global model using AIC weights. AIC 

evidence ratios clearly indicated the one model which was then tested for and found to have 

no spatial auto-correlation. Effects graphs are included in Figure 5. 

Table 6: Brewer's Sparrow GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) -4.221776 0.898604 -4.698 2.63e-06

cattle -0.169191 0.089811 -1.884 0.0596

cattle^2 0.003537 0.001934 1.829 0.0674

horse 0.103647 0.061007 1.699 0.0893

shrub 0.487571 0.112856 4.320 1.56e-05

shrub^2 -0.036657 0.008652 -4.237 2.27e-05

mrps 0.132892 0.070684 1.880 0.0601

fire index -2.091903 0.879970 -2.377 0.0174

adjacency 1.115637 0.230079 4.849 1.24e-06

The effects graphs in Figure 5 clearly demonstrate the non-linear nature of the response to 

shrub as positive at lower quadratic values and then flattening and becoming negative at 

higher values. Shrub is needed for both nesting and foraging (Rotenberry et al. 1999) so that 

would not seem to explain the response. However, the authors also indicate they use larger 

sage for singing posts and possibly more shrub is not necessarily higher sage at Suffield as 

shrub there is snowberry in some places and sage in others. We have no spatial data on 

shrub heights or species. The response to soil is inconclusive as clearly seen by the wide 

confidence intervals. Fire appears to have a negative effect but the magnitude of that effect 

cannot be clearly established because of wide confidence intervals but the direction of the 

response is consistent with Bock and Bock (1992) and the species need for shrub as a nest 

substrate. The species had enough data for analysis but almost all observations were in the 

north block and in a small area of vegetated sand dunes in the south block on eolian and 

morainal formations (Dale et al. 1999). 

 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 5: Brewer's Sparrow Fire & Grazing Model Effects 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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4.5.4 Chestnut-collared Longspur 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. The creation of a global model was not possible because in both cases grazing 

measures had been retained. Without some statistical means to select a single model, the 

model with the most statistically significant terms was chosen to better understand the relative 

importance of the different predictors. This final model was assessed for spatial autocorrelation and none was found. The final model is summarized in Table 7. Effects graphs are 

included in Figure 6. 

Table 7: Chestnut-collared Longspur GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) 3.564e+02 1.094e+02 3.258 0.00112

cattle 5.971e-02 2.022e-02 2.953 0.00314

cattle^2 -8.933e-04 5.219e-04 -1.712 0.08696

shrub^2 -3.764e-02 1.369e-02 -2.749 0.00597

fire index 2.803e-01 1.593e-01 1.759 0.07851

topo -3.583e+02 1.093e+02 -3.279 0.00104

adjacency 2.034e+00 2.844e-01 7.152 8.57e-13

The grazing effects suggest that grazing is generally a positive factor for this species. Shrub 

is a negative factor for this species but the extent of its negative impact at higher levels of 

shrub is unclear. Fire had no statistically significant effect. Increased topographic roughness 

is also seen to have a negative effect but due to wide confidence intervals, the steepness of 

this slope of this effect is not discernible. Chestnut-collared Longspurs are semi-colonial in 

that where there is one there are usually several and they engage in chases and display 

vigorously against each other so it is not surprising that adjacency was highly significant. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 6: Chestnut-collared Longspur Fire & Grazing Model Effects 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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4.5.5 Clay-colored Sparrow 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. In this case one of the models was the global model and the use of AIC weight 

comparison found the global model was considered better. Unfortunately this model had no 

significant results and was not useful to us so we selected the simpler and still somewhat 

supported model with significant results. The final model was assessed for spatial autocorrelation and none was found. The final model is summarized in Table 8. Effects graphs are 

included in Figure 7. 

Table 8: Clay-colored Sparrow GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) -3.15302 0.27078 -11.644 < 2e-16

shrub 0.17607 0.02724 6.463 1.03e-10

fire index -2.96472 1.35259 -2.192 0.0284

adjacency 1.47447 0.27303 5.400 6.65e-08

Both the values in Table 8 and the graphs in Figure 7 clearly demonstrate that shrub is a 

positive predictor for this species. Fire is weakly significant and clearly negative but due to 

large confidence intervals it is not possible to clearly indicate the strength of this relationship. 

Since they are a shrub obligate species that nests in shrub it is not surprising that a 

disturbance that reduces shrub or shrub foliage even if only for a year or two would have a 

negative influence. The effect graph is quite clear that sites with no fires or at least two years 

since a single fire (index of 0.5) are preferred. Because shrub cover appears in a clumped 

distribution it is not surprising that this species as well as Brewer’s returns adjacency as 

highly significant. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 7: Clay-colored Sparrow Fire & Grazing Model Effects 
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4.5.6 Grasshopper Sparrow 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated a single simpler 

solution. This final model was assessed for spatial auto-correlation and none was found. The 

results of the final model are reported in Table 9. Effects graphs are included in Figure 8. 

The results indicate a non-linear negative relationship to shrub and a positive relationship with 

coarser soils. For reasons unknown, 1995 was a better year for this species in terms of 

abundance which may not be related to anything happening in the NWA as this species is not 

resident in the NWA for the entire year. No grazing or fire effect was detected so it appears 

that if any fire or grazing effects exist for this species that they are not detectable in 

comparison to other effects like shrub or soil texture. 

Table 9: Grasshopper Sparrow GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) -2.021052 0.375582 -5.381 7.40e-08

shrub^2 -0.008871 0.001660 -5.343 9.13e-08

mrps 0.128929 0.031183 4.135 3.56e-05

fire index -0.375474 0.217167 -1.729 0.083816

Year 1995 0.409327 0.109230 3.747 0.000179

adjacency 0.590004 0.181467 3.251 0.001149
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Figure 8: Grasshopper Sparrow Fire & Grazing Model Effects 
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4.5.7 Horned Lark 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. These simplified models were compared using AIC weights with a global model and 

were found to have near equal support. We selected model with the more highly significant 

predictor variables which was tested for spatial auto-correlation and none was found. The 

results of the final model are reported in Table 10. Effects graphs are included in Figure 9. 

Table 10: Horned Lark GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) -0.5707321 0.1947943 -2.930 0.00339

cattle^2 0.0003855 0.0001243 3.102 0.00192

shrub^2 -0.0109569 0.0019686 -5.566 2.61e-08

fire index 0.2145890 0.1029537 2.084 0.03713

adjacency 0.8825514 0.1926576 4.581 4.63e-06

The effect of cattle grazing is positive, but in a non-linear fashion which is consistent with the 

species affinity for bare ground and reduced litter values. Shrub response is also non-linear 

but in this case negative which is also consistent with the species preference for an open 

environment. The model suggests the possibility of a weak positive effect to fire, but that 

cannot be determined with confidence. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 9: Horned Lark Fire & Grazing Model Effects 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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4.5.8 Long-billed Curlew 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. A reasonable global model could not be constructed as pellet measures remained in 

both models. Both models also displayed similar ecological implications so the model with 

most significant predictor variables was selected. The final model was tested for spatial autocorrelation and none was found. The results of the final model are reported in Table 11. 

Effects graphs are included in Figure 10. 

Table 11: Long-billed Curlew GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) -2.3710401 0.2578022 -9.197 < 2e-16

cattle^2 0.0005683 0.0002831 2.008 0.04469

horse 0.6308740 0.3144459 2.006 0.04482

horse^2 -0.1024746 0.0717384 -1.428 0.15316

shrub^2 -0.0364029 0.0140352 -2.594 0.00950

fire index 0.6651990 0.2334735 2.849 0.00438

adjacency 1.3935970 0.2704989 5.152 2.58e-07

Unlike models for many other species most of the quadratic variables were retained. For this 

species the results in Table 11 suggest a weak positive relationship to cattle grazing but a 

negative response to horse grazing which might be more influenced by the occurrence of 

horse grazing mainly in the shrubby portions of the study area. Shrub is clearly negative and 

there appears to be a positive relationship to fire (which would reduce shrub), but the large 

confidence intervals make it impossible to determine the magnitude of this response. 

 
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Figure 10: Long-billed Curlew Fire & Grazing Model Effects 
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4.5.9 Lark Bunting 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. One of these models was a global model and AIC weight comparison demonstrated 

greater support for the global model. The final model was tested for spatial auto-correlation 

and none was found. The results of the final model are reported in Table 12. Effects graphs 

are included in Figure 11. 

A quadratic response to shrub is found in the model suggesting a preference for moderate 

levels of shrub in the NWA and this is consistent with their use of shrubs as overhead cover 

for nests and with the use of shrubs as display perches (Shane 2000). The response to soil 

was not significant but there was a very weakly significant negative response to topography. 

The topography response is at odds with simpler analysis on a larger data-set that examined 

percent occurrence by ecosection and found Lark Bunting was most common in Eolian and 

Morainal with Eolian features which were areas with high topographic variation (Dale et al. 

1999). Lark Buntings show virtually no site fidelity (Shane 2000) and there is large variability 

in year to year occurrence of the species in Canada. In 1994 the species occurred on 23.3% 

of counts but in 1995 it was present on only 11.5% (Dale et al. 1999). This certainly 

complicates understanding their relationships with habitat or disturbance factors. As with other 

species, if there is a grazing or fire effect it is clearly subtle compared to other predictors. 

Table 12: Lark Bunting GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) 1.832e+02 9.286e+01 1.973 0.04846 

horse_new^2 -1.434e-01 7.798e-02 -1.839 0.06593 

shrub 4.878e-01 1.066e-01 4.576 4.73e-06 

shrub^2 -2.733e-02 6.758e-03 -4.045 5.24e-05 

mrps -1.414e-01 8.803e-02 -1.607 0.10812 

topo -1.854e+02 9.234e+01 -2.007 0.04472 

Year 1995 -4.782e-01 1.727e-01 -2.768 0.00564 

adjacency 2.803e+00 3.104e-01 9.029 < 2e-16
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Figure 11: Lark Bunting Fire & Grazing Model Effects 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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4.5.10 Sprague's Pipit 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. These two models were then compared against a global model using AIC weights 

and both models, along with the global model had strong support. We selected the model with 

the strongest support and a grazing rather than a year measure. This model was then tested 

for spatial auto-correlation and none was found. The results of the final model are reported in 

Table 13. Effects graphs are included in Figure 12.

Table 13: Sprague's Pipit GLM Model Results

coef se(coef) z Pr(>|z|)

(Intercept) -0.88711 0.21773 -4.074 4.62e-05

horse_new -0.14777 0.07714 -1.916 0.0554

shrub -0.16378 0.02687 -6.095 1.09e-09

adjacency 1.41806 0.20797 6.819 9.20e-12

The quadratic effect of grazing in the initial model would be consistent with some grazing 

being positive but too much being negative as has been consistently found (Kantrud 1981, 

Dale 1983, Davis et al. 1999) but in the final model grazing effect for new horse pellets was 

not significant. Because of large confidence intervals it is not possible to determine if the 

observed grazing effect is artefactual or real. It appears that other grazing or fire effects, if 

any, were too subtle to be undetected. Shrub was strongly negative which is again consistent 

with previous studies. In a moister location in North Dakota where ungrazed cover was tall 

and thick and shrubs were common, fire had a positive effect (Madden et al. 2000) but fire 

was not included in our final model and the correlation tests (Table 2) show pipits responding 

negatively to fire. 
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Figure 12: Sprague's Pipit Fire & Grazing Model Effects 
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4.5.11 Upland Sandpiper 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. A global model was then constructed and used to compare with AIC weights the 

relative support for these different models. No model came out clearly more supported than 

the others, so we selected the model with both fire and grazing variables. This model was 

then assessed for spatial auto-correlation and found to have none. The results of the final 

model are reported in Table 14. Effects graphs are included in Figure 13. 

Table 14: Upland Sandpiper GLM Model Results

coef se(coef) t-value p-value

(Intercept) -1.7681602 0.2214830 -7.983 1.42e-15

all_pellets^2 -0.0011389 0.0007763 -1.467 0.14234

shrub 0.0926650 0.0219035 4.231 2.33e-05

fire index -0.6325326 0.4763787 -1.328 0.18425

Year 1995 -0.4059104 0.1994539 -2.035 0.04184

adjacency 0.6282932 0.2066134 3.041 0.00236

A positive response to shrub is the only effect of interest for this model. A negative response 

for both grazing and fire are suggested by the data but the confidence intervals are too large 

to determine if these effects are real. If they are real this response would be counter to the 

studies summarized in Table 1. However, those studies may be in moister locations where 

grazing and fire may be needed to create heterogeneity such that foraging and nesting areas 

can have very different structure. 
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Figure 13: Upland Sandpiper Fire & Grazing Model Effects 

 
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4.5.12 Vesper Sparrow 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models. These models were then compared against a global model with AIC weights to 

determine which model to use. In this case there was strong support for one of the models so 

it was tested for spatial auto-correlation and none was found. The results of the final model 

are reported in Table 15. Effects graphs are included in Figure 14. 

Table 15: Vesper Sparrow GLM Model Results

coef se(coef) t-value p-value

(Intercept) -1.92959 0.44819 -4.305 1.67e-05

cattle -0.05137 0.01839 -2.794 0.00521

shrub 0.03382 0.01481 2.283 0.02242

mrps 0.07388 0.03852 1.918 0.05513

adjacency 0.85556 0.17664 4.844 1.28e-06

Grazing had negative effect but the slope or strength of this effect is difficult to determine 

because of the wide confidence intervals which is consistent with Kantrud (1981) who found 

higher counts in areas of moderate grazing but no really clear directional response to grazing. 

They are an edge species that needs at least a little shrub so it may not be too important how 

the grass mixed with shrub is managed so long as that management does not result in total 

removal of shrub. This habitat preference is consistent with the finding that shrub and soil 

coarseness had weakly significant positive results. Any fire effects that might exist for this 

species could not be detected. Shrub recovers from fire in just a few years unless fires are 

severe or repeated frequently so the majority of the Suffield sampling sites would not be 

burned enough to eliminate shrub altogether. After one fire at Suffield Vesper Sparrows and 

other shrub associated species were heard singing in blackened shrubs with few, if any 

leaves (Brenda Dale, Personal Communication). 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 14: Vesper Sparrow Model Fire & Grazing Effects 
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4.5.13 Western Meadowlark 

Following the procedure outlined in section 4.4, two models were specified for this species 

with common factors except in one case including all pellets and in another including horse 

and cattle pellets separately. In both initial models grazing and shrub measures were included 

as quadratic terms. AIC stepwise selection of these two models generated two simpler 

models with the species adjacency parameter was removed. These models were then 

compared against a global model using AIC weights and the simplest model had the strongest 

support. The residuals of the final GLM showed spatial correlation but adding the species 

adjacency parameter back into the model did not resolve the spatial correlation so a GLMM 

model was created. The resulting GLMM model had reduced spatial correlation but some still 

remained. The scale and significance of the predictors was similar to the GLM model so, the 

GLM model is displayed here to examine the effect sizes, but caution is needed in the 

interpretation of these results. The results of the final model are reported in Table 16. Effects 

graphs are included in Figure 15. 

Table 16 Western Meadowlark GLM Model Results

coef se(coef) t-value p-value

(Intercept) 1.220417 0.043157 28.278 < 2e-16 

cattle -0.010935 0.004125 -2.651 0.00802 

fire index -0.221207 0.090441 -2.446 0.01445 

Year 1995 -0.105929 0.057147 -1.854 0.06379

The results indicate that cattle grazing is negative. The spatial correlation and wide 

confidence intervals however suggest that although statistically significant, the strength of this 

relationship is uncertain. Similarly for fire the negative response is weakly significant and has 

wide confidence margins (due to paucity of samples with high fire or grazing values) 

suggesting that we don't have sufficient signal to assert this as a real relationship. The 

negative response to grazing should be viewed with caution as the negative relationship to 

cattle pellets is offset by a positive relationship to horse pellets (Table 3) indicating it might be 

more about a preference for the north block which also shows in a positive response to 

coarser soils. A negative response to grazing is the opposite of previous studies and to the 

simpler analysis of Dale et al. (1999) which found they occurred more frequently in grazed 

than ungrazed sites. They showed more variation in numbers per point than any of the other 

species analyzed which may have resulted in different results utilizing abundance than were 

found with frequency of occurrence studies. 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

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Figure 15: Western Meadowlark Fire & Grazing Model Effects 
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4.6 Summary 

The original simple analyses for fire and grazing (Dale et al. 1999) actually had clearer results 

than the more statistically sophisticated model approach taken here, but the authors were 

concerned because they recognized that fire and grazing disturbances were not equally 

distributed across ecosections (classification based on soil development and topography) so 

that it was possible that responses to key habitat features such as shrub, might be creating 

bias. For example, grazing by cattle was confined to the south block of the proposed NWA 

which was topographically and vegetatively different from the north block with its vegetated 

dunes and higher shrub cover and patchy grazing by feral horses. The purpose of the current 

re-analysis was to include soil particle size and topography in order to control for their habitat 

related effects. Broad confidence intervals often diminished our certainty of fire and grazing 

effects but were not surprising given both heavy grazing and frequent or recent fire occurred 

at relatively few of our samples. This is one of the drawbacks of a mensurative study, but 

given how rare it is to find an area with semi-regular fire it was worth exploring. However, 

nothing can replace a controlled study where a range of disturbance levels are applied to 

sites that are otherwise similar. In the end, the same issues that hampered the initial analysis 

still occurred, but we were better able to quantify them and it was clear that with moderate 

grazing and fire at levels similar to historic patterns the soil and topographic controlled habitat 

was more important for many species than the disturbance factors. 
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5 Analysis of EC Wetland Distance Data 

5.1 Background 

The influence of distance to wetlands and wetlands in the landscape has been little studied 

with only two published and two as yet unpublished studies. Cattle water sources in particular 

have the potential to be management tools and responses to wetland edges may need to be 

considered in area management. 

Fontaine et al. (2004) examined the abundance of grassland birds up to 800m from cattle 

water sources (a mix of permanent wetlands, dugouts, and water tanks). They found 

vegetation height and thickness increased with distance from water and two species that 

prefer shorter cover were more abundant near the water source. They observed that effects 

for additional species might have been detectable if they had been able to sample farther 

from water. 

A southern Alberta study did multiple linear analyses of abundance and productivity in relation 

to edges including water (Koper and Schmiegelow 2006 a and b) and later nonlinear analyses 

(Champagne et al., Sliwinski and Koper 2012). Their wetlands were a mix of types including 

dugouts and not all were used as a water source by grazers and a few fields were not grazed 

at all. Given the mix of wetland types and management, it is unsurprising they found 

vegetation height had little relationship to distance to wetland. Most of their study species 

responded negatively to wetland edge even at substantial distances including Horned Lark 

which Fontaine et al. (2004) had found more abundant near water. 

A study conducted in two years in both southern Saskatchewan and Alberta (Fisher et al. in 

Review) found vegetation decreased with increasing distance to water, but most of their 

wetlands were grassy non-permanent wetlands (Ryan Fisher Pers. Comm. with Brenda Dale). 

They found Western Meadowlark and Savannah Sparrow less common within 100 m of 

wetlands and the negative effect extended to 400 m for Sprague’s Pipit. They also found no 

consistent responses to water between study areas by any species. They questioned the 

possibility espoused by Sliwinski and Koper (2012) that an edge process could have influence 

at a scale of multiple kilometers. 

These three studies produced contrasting results,, but either involved a mix of natural and 

artificial water sources or did not take wetland type or use into account. A study conducted in 

four years (every other year during a 7 year period) at the Agriculture and Agri-food Research 

sub-station at Onefour, AB was able to separate distance to cattle water source from distance 

to wetland (Dale and Wiens In Preparation). Each of 6 fields had one single cattle water 

source and all wetlands were fenced to prevent cattle use. Similar to Fontaine et al. (2004) 

they found vegetation height increased with distance to cattle water source. No species 

responded to distance to wetland but they found both abundance responses for 4 species (2 

negative and 2 positive) at distances up to 1800 m from cattle water sources. They concluded 

that limiting the number of cattle water sources and provision of large fields in the dry-mixed 

grasslands was necessary to provide habitat suitable for the full suite of grassland birds. 

It seems birds may respond differently to different types of wetlands whether because some 

are more perceptible as edge or because some are more attractive or accessible to cattle. 
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The long-term data set from Suffield provides an opportunity to further examine the potentially 

important habitat factors of naturally occurring wetlands and artificial water sources. 

5.2 Data preparation 

A subset of the data provided by EC as described in section 4.1 were used together with 

additional information on bird observations from 2001 and 2005. See Dale et al. (1999) for a 

full description of the sampling strategy and avian point count methods employed in 1994 and 

1995. Monitoring using the same methods as in 2001 and 2005 was limited to a subset of the 

south block lines so we used only south block observations from the biophysical study (Dale 

et al. 199) for the water related analysis. The initial plan had been to use 2006 data, but only 

the ungrazed north block was surveyed in 2006 which would have complicated analyses if 

different areas and treatments were used in different years. Again GRASS GIS 6.4 and 

GRASS GIS 6.5 were used for processing the spatial data along with PostGIS 1.5 (PostGIS 

Core Team 2009). 

The Department of Defense (DND) and Brent Smith from Canadian Forces Base Suffield 

provided imagery which contained the spatial and temporal state of wetlands, dugouts and 

watering troughs for 1997, 2001 and 2011. The wetland data were classified into four groups: 

1. Saline wetland / barren ground 

2. Moist (no standing water); dominated by low growing perennial forbs and grasses 

3. Herbaceous / shrub / deciduous; dominated by highly productive vegetation including 

terrestrial and emergent vegetation including rushes, shrubs, and tall trees 

4. Open water; dominated by open water with nil to sparse emergent vegetation 

The numbering does not bear any relationship to standard wetland classification such as used 

in Adams et al. (1998) and there is no implication that the type number is related to 

permanency. Using recommendations from Brent Smith, we used the 1997 wetland data for 

the 1994 and 1995 bird data; these were considered normal precipitation years. The 2001 

wetland data were used for the 2001 and 2005 bird data which were considered drought 

years. 

DND also provided a shapefile of fence lines which were used to create a polygon layer to 

calculate field size and constrain some wetland distance measures to within the boundaries of 

the field where the bird observation was made. 

Pellet and fire data were not available for 2001 and 2005, but shrub, mean relative particle 

size and topographic roughness remained valid and potentially useful variables. The value of 

the last two variables as surrogates for habitat measures such as vegetation height and 

thickness, litter, and shrub has recently been confirmed (Dale and Wiens ,in preparation). 

We wished to look at how wetlands in the landscape as well as cattle water sources within 

fields, might influence bird occurrence. 

For each bird count location the distance to all wetlands within 10km by type (four classes of 

wetlands, dugouts and troughs) was calculated in PostgreSQL / PostGIS using the 

recalc_wetland_distances.sql and recalc_wetland_distances_infield.sql scripts which both 
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depend on the functions defined in calc_nearest_neighbors_by_type.sql script all found in the 

deliverable 3 scripts folder. In this actual distance was used unlike Fisher et al (in review) 

which used the log of distances because review of the distribution of this variable did not 

indicate that transformation of this variable was necessary. 

It is important to note that for many wetlands, if they had standing water, they would have 

been fenced and were thus unavailable to cattle. The one exception is the South 

Saskatchewan River which was not fenced to prevent cattle use at the time of our surveys. 

Because of cattle use, dugouts and troughs are features of importance within a field and were 

thus only measured within a field, not across fence lines. For the other four types of wetlands 

however, both in-field and field independent distances and counts were calculated. Count was 

calculated in two ways. First, all wetlands at any distance up to 10km by type were counted 

for each bird count location. Second, wetlands within 500m, wetlands from 500 to 1000m, 

from 1000 to 2500m and from 2500 to 5000m were counted. The reason for this method was 

to estimate both the broad scale effect of wetland presence close to a bird count location, but 

also to determine if certain distances more closely correlated to bird abundances than others; 

in other words to assess the scale of the effect. 

After the initial analysis of the full data set using parametric and non-parametric methods, we 

extracted the centroids of each of the original pixels to create a wetland point map. From this 

layer we used the GRASS GIS v.neighbors function to generate surfaces of point density 

layer at 500, 1000, 2500 and 5000m radii. The resulting measures indicated not only the 

overall distribution of wetlands within the specified distance bands, but also the amount of the 

landscape within those distance bands covered by wetlands of a particular type. These data 

were sampled for each bird count location centroid using the v.what.rast module in GRASS 

GIS. 

A summary of variable names and their definitions is included in Table 45 in Appendix C. 

As noted earlier, it was not possible to include both fire and grazing along with wetlands 

variables in a single analysis because not all variables were available for all years. Still it was 

deemed worthwhile to generate that limited data set to determine what, if any, relationships 

could be established between wetland measures and grazing and disturbance measures. 

Using the script pull_wetland_n_fire.sql found in the deliverable_3/scripts folder we pulled 132 

records all from 1994 and 1995 into R to conduct a simple correlation anlysis. 

The final merging of data and extraction of the main data set for import into R was done using 

the pull_wetland_data.sql and repull_wetland_data.sql script also found in 

deliverable_3/scripts. The number of records used for this analysis was 1271. 

5.2 Initial examination of data 

The distribution of species records and predictor variables was assessed through the use of 

histograms and the same species list used in the fire and grazing analysis was used for this 

phase. Histograms of the wetland variables are found in Appendix D. 

An examination of potential collinearity in models was conducted by testing the correlation 

between different predictor variables via a Kendall rank correlation test. The expected 

correlations between moist and herbaceous wetlands was confirmed as well as correlations 
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within wetland types at similar spatial scales. Also of note is that all wetland types except 

herbaceous wetlands were positively related to soil particle size so that the number of such 

wetlands and the area occupied increases with coarse soils while the abundance and extent 

of herbaceous wetlands is negatively related to topography because they are most likely to 

occur in flatter area with only slight undulations. Further troughs and dugouts were 

significantly more prevalent in finer soils. These associations definitely need to be considered 

in interpreting model results where the species has previously shown a response to soil or 

topography. These tables were referenced in the creation of possible models. The full results 

of this analysis can be found in Appendix G. 

The correlation between the wetland and fire and grazing measures is included in Appendix 

H. No strong correlations were found. The correlations tests show positive correlations 

between field size and cattle pellets and numbers of dugouts and water troughs as expected. 

Barren / saline wetlands are shown to be negatively correlated with cattle pellets as well as 

shrub. Total burns and the fire index were positively correlated to moist and herbaceous 

wetlands from 2.5 to 10km. Troughs and dugouts were negatively correlated with total burns 

and the fire index. Overall these results are not surprising but do lend mild support for the use 

of wetlands as a partial surrogate for grazing information when and where no grazing data are 

available. 

In past work (Wiens et al. 2008) annual precipitation had proven to be a valuable predictor 

variable. Were were interested in understanding how this would be reflected in the wetland 

data obtained from DND. The total area classified as wetlands remained the same between 

years but we examined the change in composition of this area over time. These results are 

presented in Table 17. The results, as would be expected, show a shift from moist wetlands to 

open water from drought to normal and on to wet conditions. The change in barren / saline 

wetland is not consistent across years nor does it show a meaningful trend; the reason for this 

is unknown. The largest shifts however are, as expected, between moist and herbaceous 

wetlands. 

Table 17: Wetland Composition by Year 

Wetland Type / Year 2001 (drought) 1997 (normal precipitation) 2011 (wet) 

Type 1 – Barren / Saline Wetland 6.4% 9.0% 5.2%

Type 2 – Moist Wetland 57.5% 32.4% 10.9%

Type 3 – Herbaceous Wetland 30.0% 44.4% 67.3%

Type 4 – Open Water Wetland 6.1% 14.2% 16.6%

5.3 Non-parametric Analysis 

As in section 4.3, the Kendall rank correlation coefficient was used to compare the 

relationship between species abundance and the different wetland measures. We used this 

same approach with the variety of wetland variables to assess the nature of the relationships 

between individual predictors and species abundance. The results are presented in section 

5.5 by species. 
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5.4 Parametric Analysis 

Poisson Generalized Linear Models were employed a similar method as described in section 

4.4. Again we used the regression analysis as a means to assess the relative predictive 

power of different measures. As found in Appendix G, many variables were correlated to each 

other. To avoid collinearity in the model we followed the conservative approach and did not 

include predictor variables in the same model if the tau value, which is functionally equivalent 

to a Pearson's r, was equal to 0.7 or higher (Mason and Perrault, 1991). These model results 

are presented together with the non-parametric results by species in section 5.5. 
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5.5 Results & Discussion 

In this section the combination of the non-parametric and parametric analysis results are 

presented together by species. The non-parametric tests were used to determine the relative 

strength of the different predictors and if the data were sufficient to adequately model species 

abundance. Relevant results of the non-parametric results are mentioned in the text and the 

details of the non-parametric analysis, which were extensive, are found in tables in Appendix 

E. All correlograms used for testing spatial auto-correlation in the model residuals are 

included in Appendix F. 

5.5.1 Baird's Sparrow 

In section 4.5.1 we found that shrub had a powerful negative relationship to the abundance of 

this species along with topographic roughness. The wetland non-parametric analysis found 

similar relationships as well as negative relationships to barren / saline wetlands, a positive 

relationship to the amount or number herbaceous wetlands at distances greater of 500m to 

2.5km and a positive relationship to water trough distance. 

Using this information and the predictor correlations found in Appendix G, a Poisson 

generalized linear model (GLM) was constructed and using AIC stepwise selection a 

simplified model as created and is presented in Table 18. This was then tested for spatial 

auto-correlation of residuals to test if other factors were missing from the model. Minimal 

spatial auto-correlation was detected and thus it was suitable for our needs. The most 

influential predictor was herbaceous wetlands and topography was also highly significant. 

Table 18: Baird's Sparrow Wetland Predictors Poisson GLM 

Estimate Std. Error z value Pr(>|z|)

(Intercept) 3.600e+02 8.813e+01 4.085 4.4e-05

topo -3.618e+02 8.808e+01 -4.108 4.0e-05

wet1_1000_cnt -2.163e-01 1.411e-01 -1.532 0.125

wet3_all_cnt 1.782e-03 2.628e-04 6.780 1.2e-11

 

The positive relationship to the number of herbaceous wetlands (type 3) may be surprising 

given negative distance responses observed in Sliwinski and Koper (2012) and response to 

water tanks but not wetlands in Dale and Wiens (in preparation). However, as observed in 

Table 18 and Figure 16, during drought years there was significant shift from herbaceous to 

moist wetlands, so it is likely that the areas during drought years with appropriate levels of 

cover were near but not directly adjacent to herbaceous wetlands. 

The negative relationship to topography is consistent with the results in section 4 and with the 

model presented in Wiens et al. (2008) and is not surprising given that areas of high 

topographic roughness usually have shrub which reduces habitat suitability for this species. 

These results emphasize the importance of considering the local situation in terms of climate, 

soil and topography in setting grazing management strategies to ensure that moderate cover 

is maintained for this species. The positive correlation to water trough distance was significant 

and is consistent with the increase in Baird’s Sparrow abundance at greater distances 
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observed at Onefour (Dale and Wiens In Preparation) which suggests that large fields with 

few water sources may benefit this species. 

Figure 16: Baird's Sparrow Wetland Model Effects 

5.5.2 Brown-headed Cowbird 

In section 4.5.2 this species was found to have a weak and probably positive correlation to 

shrub as expected. There were only 10 observations for this data set as this species primarily 

occurs in the northern part of the Suffield NWA. With so few observations, it was not possible 

to analyze this species for wetland effects. 
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5.5.3 Brewer's Sparrow 

In section 4.5.3 it was observed that species had a negative relationship to fire and a 

quadratic relationship to shrub and an unclear relationship to soil texture. The wetland nonparametric analysis found a simple positive relationship to shrub and a positive relationship to 

topographic roughness. Additionally a negative relationship was found for barren / saline 

wetlands at distances from 1 to 5km, a positive relationship to distance from herbaceous 

wetlands within fields and a positive relationship to open water wetlands within 10km. 

The positive relationship to shrub and topographic roughness are both congruous with this 

species preference for shrub. Similarly avoidance of barren / saline wetland areas and a 

positive relationship to distance from herbaceous wetlands are not surprising. Open water 

wetlands are rare in the NWA but include the South Saskatchewan River which is the east 

boundary of the NWA. Sage, an important habitat component for this species is prevalent in 

the river flats. 

Using this information with the predictor correlation information found in Appendix G a 

Poisson GLM was created and simplified using stepwise AIC selection. The resulting model is 

summarized in Table 19 and Figure 17. This model was evaluated for spatial auto-correlation 

in the residuals to ensure correct interpretation of the model and to determine if other 

predictors were missing. The residuals showed no significant spatial auto-correlation. 

In the model distance from herbaceous wetlands within fields was the most significant factor 

followed closely by shrub. It is interesting to note that in the non-parametric analysis the 

distance effect for herbaceous wetlands was not observed independent of field boundaries 

and within field boundaries the effect was most pronounced at 1 to 2.5km. This in-field 

dynamic may indicate some sensitivity to grazing as suggested by the cattle pellet response 

found in Table 2. 

Table 19: Brewer's Sparrow Wetland Predictors Poisson GLM 

Estimate Std. Error z value Pr(>|z|)

(Intercept) -3.655e+00 2.901e-01 -12.600 < 2e-16

cws_shrub 1.675e-01 4.180e-02 4.006 6.16e-05

wet3_all_if_xd 2.206e-04 5.173e-05 4.265 2.00e-05
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Figure 17: Brewer's Sparrow Wetland Model Effects 
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5.5.4 Chestnut-collared Longspur 

In section 4.5.4 it was found that topographic roughness and shrub were negatively correlated 

with this species abundance but fire and grazing effects were inconclusive. The wetland nonparametric analysis found strong negative correlations to shrub and topography. A negative 

response to barren / saline wetlands and open water wetlands and a positive to moist 

wetlands. A positive relationship to distance from water troughs and dugouts was also found. 

The avoidance of shrub and topographic roughness is consistent with this species habitat 

requirements. The preference for moist wetlands is also consistent with the litter depth 

requirements of this species and similarly its avoidance of barren / saline wetlands. The 

avoidance of water troughs and dugouts is consistent with a need for grazing but not 

excessive grazing as they are associated with good range conditions (Fisher et al. In Review). 

Using the non-parametric results together with the predictor correlation information found in 

Appendix G a Poisson GLM was constructed for this species. The model was simplified using 

stepwise AIC selection and it was evaluated for spatial auto-correlation. The residuals from 

the model showed significant spatial correlation so an adjacency parameter was added to the 

model and the process was repeated. The final model, summarized in Table 20 and Figure 

18, showed only minimal spatial auto-correlation in the residuals indicating that an important 

predictor was missing from the model formulation. 

Table 20: Chestnut-collared Longspur Wetland Predictors Poisson GLM 

Estimate Std. Error z value Pr(>|z|)

(Intercept) 3.293e+02 5.852e+01 5.627 1.83e-08

cclo_adj 2.460e+00 2.094e-01 11.745 < 2e-16

topo -3.306e+02 5.848e+01 -5.653 1.58e-08

wet2_5000kd 1.634e-04 5.567e-05 2.935 0.00334

wet4_all_cnt -4.147e-03 8.469e-04 -4.897 9.73e-07

Ignoring adjacency, the most significant variable was topographic roughness, followed by 

numbers of open water wetlands within 10km and the amount of moist wetland within 5km. 

For management purposes the analysis suggests that larger fields with well regulated grazing 

provide a suitable habitat for this species. 
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Figure 18: Chestnut-collared Longspur Wetland Model Effects 
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5.5.5 Clay-colored Sparrow 

In section 4.5.5 it would found that shrub was positively correlated with this species 

abundance and fire was a negative factor. The wetland non-parametric analysis also found a 

positive correlation with shrub as well as positive relationships to topographic roughness and 

the number of open water wetlands within 10km. The non-parametric results are consistent 

with the species preference for shrub and moderate cover. 

Using the results from the non-parametric analysis and the predictor correlation information 

found in Appendix G, a Poisson GLM was specified. This model was then simplified using AIC 

stepwise selection and the resulting model was assessed for spatial auto-correlation and 

none was found. This final model is presented in Table 21 and Figure 19. 

Table 21: Clay-colored Sparrow Wetland Predictors Poisson GLM 

Estimate Std. Error z value Pr(>|z|)

(Intercept) -3.98599 0.26064 -15.29 < 2e-16

cws_shrub 0.24544 0.04545 5.40 6.66e-08

The AIC selection process removed all variables except for shrub, which surprisingly was able 

to predict species distribution without any spatial correlation in the model residuals. Clearly 

this is a very strong determinant for this species in the NWA. 

Figure 19: Clay-colored Sparrow 

Wetland Model Effects 
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5.5.6 Grasshopper Sparrow 

In section 4.5.6 it was reported that this species as negatively correlated with shrub and 

positively correlated with soil coarseness. The species likes moderate height clumped 

grasses with low amounts of litter and tolerates bare ground. These later characteristics differ 

from some of the other birds that like moderate height such as Baird’s Sparrow or Sprague’s 

Pipit which prefer the finer soils which have less bare ground. The wetland non-parametric 

analysis found no relationship to shrub, but positive relationships to soil coarseness and 

topographic roughness. In addition, a negative relationship to moist wetlands was found. 

The negative relationship to moist wetlands were more pronounced at shorter distances 

indicating that proximity to wetlands is undesirable for this species which was confirmed by a 

positive relationship to distance from moist wetlands. Open water wetlands were found to 

have a positive relationship at distances greater than 2.5km. Numbers of barren / saline 

wetlands within 10km were also found to have a positive relationship to this species. Overall 

these results are not surprising given this species preference for moderate cover. 

The initial results were used together with the predictor correlation information found in 

Appendix G to specify a Poisson GLM, which was simplified using AIC stepwise selection and 

subsequently evaluated for spatial auto-correlation. The residuals from this model displayed 

only minor spatial auto-correlation and the final model is summarized in Table 22 and Figure 

20. 

Table 22: Grasshopper Sparrow Wetland Predictors Poisson GLM 

Estimate Std. Error z value Pr(>|z|)

(Intercept) -4.106e+01 2.281e+01 -1.800 0.071912

mrps 7.845e-02 2.464e-02 3.184 0.001454

topo 3.922e+01 2.269e+01 1.728 0.083952

wet2_all_if_xd 9.352e-05 2.371e-05 3.945 7.99e-05

wet4_all_cnt 3.285e-03 8.924e-04 3.681 0.000232

The most significant predictor was distance from moist wetlands followed by numbers of open 

water wetlands within 10km and soil coarseness. These results are congruous with the nonparametric analysis and the species biology but have no new management implications. 
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Figure 20: Grasshopper Sparrow Wetland Model Effects 
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5.5.7 Horned Lark 

In section 4.5.7 this species was found to have positive correlations with grazing and fire and 

negative correlations with shrub as expected from a species that prefers sparse cover with 

little woody vegetation. The wetland non-parametric analysis found negative relationships to 

shrub, barren / saline wetlands, open water wetlands and distance from dugouts. There was 

also a positive relationship found to moist wetlands at distances up to 1km. 

These findings are consistent with a strongly positive relationship to grazing activity agrees 

with section 4.5.7. The negative response to increased distance from dugouts is similar to 

what was found in North Dakota and at Onefour, Alberta (Fontaine et al. 2004, Dale and 

Wiens In Preparation). A negative correlation was also found with open water wetlands that 

had no to sparse emergent vegetation. This correlation makes sense because most of these 

wetlands are fenced off from cattle access and we can therefore reasonably assume that 

these wetlands would have more cover near them. Surprisingly however positive correlations 

were found with moist wetlands, but it is possible that with over half of the data coming from 

drought years, these slightly wetter areas were better relative to other areas available to this 

species. As the reason for this moist wetland correlation was not clear, it was not included in 

the initial model specification. 

Using the information from the non-parametric analysis and the predictor correlations found in 

Appendix G, a Poisson GLM was constructed and simplified using stepwise AIC selection. 

The module residuals were then tested for spatial auto-correlation to assess if the p-values 

could be trusted and if the model specification was sufficient or if important predictors were 

missing. No spatial auto-correlation was found and the final model is summarized in Table 23 

and Figure 21. 

Table 23: Horned Lark Wetland Predictors Poisson GLM 

Estimate Std. Error z value Pr(>|z|)

(Intercept) 8.180e-01 1.662e-01 4.922 8.57e-07

dug_all_xd -9.514e-05 3.584e-05 -2.655 0.00794

wet4_all_cnt -1.896e-03 7.041e-04 -2.692 0.00710

The results from the model were similar in significance and the effect plots found in Figure 21 

clearly support the grazing relationship found in section 4.5.7. 
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Figure 21: Horned Lark Wetland Model Effects 
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5.5.8 Long-billed Curlew 

In section 4.5.8 this species was found to be negatively correlated with shrub. The wetland 

non-parametric analysis found no relationship to shrub or topography and few other 

significant correlations. An interesting relationship was found with moist wetlands where 

independent of field boundaries a negative relationship at the scale of 5km was found. 

However within field boundaries, positive relationships were found with numbers of moist 

wetlands within 500m and the complementary negative relationship to distance within field 

boundaries. Open water wetlands, at the scale of 1 to 2.5km within field boundaries, was also 

found to be positive. Weak negative relationships were found to soil coarseness and distance 

to water troughs. 

This species is known to prefer a mixture of vegetation heights and so within the bounds of a 

single field moist wetlands might be useful, but over a larger area, too many of them is not 

suitable. 

Using the correlations found in the non-parametric analysis and the predictor correlations 

found in Appendix G a Poisson GLM was created and simplified using stepwise AIC selection. 

This model was then evaluated for spatial auto-correlation and only minimal amounts were 

found (Table 24 and Figure 22). 

Table 24: Long-billed Curlew Wetland Predictors Poisson GLM Model

Estimate Std. Error z value Pr(>|z|)

(Intercept) -1.143e-01 4.405e-01 -0.259 0.79531

mrps -7.991e-02 4.017e-02 -1.990 0.04664

wet2_all_if_xd -1.666e-04 6.169e-05 -2.701 0.00691

The model results show that distance to moist wetlands within a field are significant. These 

results have no new management implications. 
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Figure 22: Long-billed Curlew Wetland Model Effects
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5.5.9 Lark Bunting 

In section 4.5.9 this species was reported to have a quadratic relationship to shrub. In the 

wetland non-parametric analysis found a simple positive relationship to shrub as well as to 

soil coarseness and topographic roughness. Examining the distance to barren / saline 

wetlands at different distances suggested a possibly quadratic relationship. Herbaceous 

wetlands showed a negative relationship at distances from 1 to 2.5km as well as a positive 

relationship to distance within a field. These results are congruous with this species 

preference for some shrub and low to moderate cover. 

These correlations (Appendix G) were not strong enough to preclude the combination of input 

variables when selecting predictors to specify a Poisson GLM. This model was then simplified 

using stepwise AIC selection. The simplified model was then assessed for spatial autocorrelation. Minimal spatial auto-correlation was found so some caution should be employed 

in considering the model (Table 25 and Figure 23). 

Table 25: Lark Bunting Wetland Predictors Poisson GLM Model

Estimate Std. Error z value Pr(>|z|)

(Intercept) -2.527e+01 8.056e+00 -3.136 0.00171

cws_shrub 1.617e-01 5.967e-02 2.710 0.00673

mrps 3.306e-01 6.297e-02 5.250 1.52e-07

wet1_all_xd 6.415e-03 2.374e-03 2.702 0.00690

wet1_all_xd^2 -5.313e-07 1.743e-07 -3.047 0.00231

wet3_all_if_xd 2.189e-04 4.557e-05 4.804 1.55e-06

The most significant variable was soil coarseness (mrps) followed by distance to herbaceous 

wetlands (type 3) within a field. Shrub and the barren / saline variables included in quadratic 

form were also significant. The tolerance for some bare ground along with some cover and 

shrub is in agreement with the non-parametric analysis results and the species documented 

habitat preferences. There are no new management implications to these results. 
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Figure 23: Lark Bunting Wetland Model Effects 
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5.5.10 Sprague's Pipit 

In section 4.5.10, shrub was reported as negatively correlated with this species abundance. 

The wetland non-parametric analysis also found negative correlations with shrub as well as 

negative correlations with moist wetlands (type 2) at all distance ranges. Positive relationships 

were found to the number open water wetlands (type 4) within 10km and to barren / saline 

wetlands from 2.5 to 5km. These results are congruous for a species with preferring cover of 

intermediate height and density. 

These factors were used together with the predictor correlation information found in Appendix 

G to form a Poisson GLM which was simplified using stepwise AIC selection. This model was 

then evaluated for spatial auto-correlation to assess if the resulting p-values could be trusted 

and if the model as lacking any major predictive factors. The model was found to have 

significant spatial auto-correlation and so a spatial adjacency measure was added and the 

process was repeated. The AIC stepwise selection of this adjusted model removed the 

adjacency measure which suggests that simple adjacency was insufficient to explain the 

pattern found in the data. The initial model is presented in Table 26 and Figure 24 and it must 

be interpreted with caution. 

Table 26: Sprague's Pipit Wetland Predictors Poisson GLM Model

Estimate Std. Error z value Pr(>|z|)

(Intercept) 3.059e-01 7.113e-02 4.300 1.71e-05

cws_shrub -5.014e-02 2.386e-02 -2.102 0.0356

wet2_5000kd -3.306e-04 6.827e-05 -4.843 1.28e-06

In the cautious interpretation of the model only the amount of moist wetland within 5km can 

be considered significant. The avoidance of areas with large amounts of moist wetlands and 

sparse shrub is congruous with a species that avoids shrub and seeks out intermediate cover. 

The fact that significant spatial auto-correlation was found and that AIC stepwise selection 

removed simple adjacency strongly indicates that important predictor variables are missing in 

the model specification. Precipitation in previous years, as a potential indicator of litter depth, 

was an important predictor in Wiens et al. (2008) and might have improved this model. Other 

unpublished work done by CWS on this species suggests that well density may be one of 

those missing predictors as additional wells had been drilled in some portions of the study 

area by 2001. 
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Figure24: Sprague's Pipit Wetland Model Effects 
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5.5.11 Upland Sandpiper 

In section 4.5.12 this species was found to be positively correlated with shrub. The wetland 

non-parametric analysis did not find a significant relationship with shrub, but did find positive 

relationships to topographic roughness and distances to moist (type 2) and herbaceous (type 

3) wetlands within a field. Positive relationships were also found with distance from moist and 

herbaceous wetlands within a field. Positive relationships were also found with barren / saline 

(type 1), herbaceous and open water (type 4) wetlands within 10km. These results are 

congruous with the known habitat preferences of this species for habitat heterogeneity at a 

larger scale (Derner et al. 2009). 

These correlations together with the predictor correlation information found in Appendix G 

were used to formulate a Poisson GLM which was simplified using AIC stepwise selection. 

The model was then assessed for spatial auto-correlation and none was found. The final 

model is summarized in Table 27 and Figure 25. 

Table 27: Upland Sandpiper Wetland Predictors Poisson GLM Model

Estimate Std. Error z value Pr(>|z|)

(Intercept) -3.6567290 0.3088654 -11.839 < 2e-16

wet2_all_if_xd 0.0004119 0.0001433 2.874 0.004054

wet3_all_if_xd -0.0002621 0.0001431 -1.831 0.067087

wet4_all_cnt 0.0080464 0.0023615 3.407 0.000656

The most significant predictor was number of open water wetlands within 10km followed by 

the distance to moist wetlands. Distance to herbaceous wetlands was not significant. The 

large scale of the selected predictors in the model suggests that in the existing data, the large 

scale measure of habitat heterogeneity has more predictive power than the other finer scale 

measures for this species. There are no new management implications from these results. 
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Figure 25: Upland Sandpiper Wetland Model Effects 
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5.5.12 Vesper Sparrow 

In section 4.5.13 this species was found to be negatively correlated with grazing and 

positively correlated with shrub and soil coarseness. The wetland non-parametric analysis 

found positive correlations to shrub, topographic roughness and the number open water 

wetlands (type 4) within 10km. Negative relationships were found to barren / saline (type 1), 

moist (type 2) and herbaceous (type 3) wetlands at a variety of scales. Distance from moist 

wetlands was positively correlated with species abundance. 

The results are largely consistent with this edge species preference for shrubby areas that are 

not heavily grazed. Open water wetlands at large scales are positively related to both 

topography and soil texture because of the prevalence of this type of wetland near the river. 

The river flats, valley walls and coulees are prime habitat for this species. 

Using these correlations and the predictor correlation information found in Appendix G a 

Poisson GLM was specified and processed using stepwise AIC selection. The resulting model 

was then assess for spatial auto-correlation. The resulting model has minimal spatial autocorrelation so it could be used and is summarized in Table 28 and Figure 26. 

Table 28: Vesper Sparrow Wetland Predictors Poisson GLM Model

Estimate Std. Error z value Pr(>|z|)

(Intercept) -8.604e+01 1.794e+01 -4.797 1.61e-06

vesp_adj 8.959e-01 1.428e-01 6.273 3.54e-10

cws_shrub 4.684e-02 2.856e-02 1.640 0.10093

topo 8.150e+01 1.781e+01 4.576 4.73e-06

wet2_all_xd 3.464e-04 1.288e-04 2.689 0.00717

wet4_all_cnt 3.900e-03 1.187e-03 3.285 0.00102

After adjacency, the most significant predictor was topographic roughness followed by open 

water wetlands with 10km and distance from moist wetlands. 

These results suggest that topography is a very powerful predictor for this species. It is 

interesting that although shrub is commonly associated with this species that the factors of 

topography, distance from moist wetlands and presence of open water wetlands are 

significant and together suggest shrubby areas within the NWA but that shrub itself in this 

model was not statistically significant. 
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Figure 26: Vesper Sparrow Wetland Model Effects 
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5.5.13 Western Meadowlark 

In section 4.5.14 grazing was reported as negatively correlated with this species. The wetland 

non-parametric analysis found positive correlations to shrub, topographic roughness and 

open water wetlands (type 4). Negative correlations were found with moist wetlands (type 2) 

and distance from water troughs and open water wetlands. 

This species is found throughout the Suffield NWA and expected to occur in a variety of cover 

regimes, shunning only extremely sparse or tall cover. This on the ground knowledge is 

congruous with these results. 

Using the non-parametric results and the predictor correlation information from Appendix G a 

Poisson GLM was formulated and processed using stepwise AIC selection. The resulting 

model was evaluated for spatial auto-correlation. Minimal spatial auto-correlation was found 

and the final model is summarized in Table 29 and Figure 27. 

Table 29: Western Meadowlark Wetland Predictors Poisson GLM Model

Estimate Std. Error z value Pr(>|z|)

(Intercept) 1.451e+00 1.608e-01 9.024 < 2e-16

wet2_5000kd -2.730e-04 4.661e-05 -5.858 4.69e-09

wet4_all_xd -5.420e-05 2.404e-05 -2.255 0.0241

The significant predictor was the amount of moist wetlands within 5km. As with some of the 

other species these results suggest that larger scale dynamics are at play for determining 

suitable habitat for this species which might be an important perspective from both a 

management and modeling perspective. 
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Figure 27: Western Meadowlark Wetland Model Effects
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6 Summary 

In this mensurative study, most species showed stronger responses to shrub, soil, and 

topography, which influence the type and amount of vegetation growing in the study area, 

than they did to measures of disturbance such as pellet counts, number of fires, years since 

fire, or distance to potential water sources or visible wetland edges. This suggests: 

1. It is very important to consider factors that influence habitat growth even in a controlled 

disturbance experiment as few sites will be completely uniform in terms of soil or 

topography and thus vegetative community. 

2. In moderately grazed semi-arid areas a full suite of grassland birds may persist for long 

periods of time without fire disturbance. 

3. Responses of birds to wetlands need to consider land-use and the type of wetland. 

4. Precipitation can be an important predictor of grassland bird habitat selection and 

abundances over time (Niemuth et al 2008, Wiens et al. 2008) and large distance 

measures of wetlands were likely to be strongly influenced by general precipitation 

dynamics. Wetland type information on its own does not provide precipitation history 

and wetland type is weakly correlated with landforms and other factors such as shrub. 

The mixed performance of some of wetland models could be a result of these 

complicating dynamics. 

5. The wetland distances were calculated from two similar years, but not as direct 

measures from each year. It might have been beneficial to limit wetland measures to 

smaller scales and have included some precipitation measures to better account for 

some of the complex dynamics, such as precipitation history effects on litter depth, that 

are involved. 

The results with potential management implications were: 

1. In semi-arid grasslands, fire regimes that approach natural regimes in terms of 

frequency and extent of fire may not have much effect when examined over a large 

area or long period of time. Immediate effects may be dramatic, but are not lasting. 

2. Distance to troughs, dugouts, and occasionally moist and open water wetlands 

influenced some species abundance in ways consistent with their response to grazing. 

This suggests that the distribution and abundance of water sources can be used as a 

management tool that is particularly important for species requiring more or patchy 

cover (Baird’s Sparrow, Sprague’s Pipit, Upland Sandpiper and possibly Vesper 

Sparrow). 

3. Field size was often related to bird abundance but this result should be viewed with 

caution as larger fields occurred in largely shrubless areas with finer soils and gentle 

topography. Stronger evidence for the desirability of large field sizes in semi-arid 

environments is the fact some species prefer to be far from cattle water sources which 

can only be achieved in large fields. 
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7 Acknowledgments 

I want to thank both Environment Canada and the Department of Defense and their 

respective staff who provided the data that made this study possible. The most significant 

contribution to this study however was Brenda Dale's sharing of her time and knowledge 

without reserve. Brenda provided on the ground knowledge of Suffield as well a deep 

knowledge of the grassland bird ecology necessary to guide the interpretation of results. 

Despite the contribution of others, all mistakes and errors in interpretation are the fault of the 

author alone. 
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Appendix A – Descriptive graphs of fire & grazing data 

A.1 Species Distributions 
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A.2 Predictor Distributions 
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A.3 Species Outlier Detection Graphs (dot charts) 
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A.4 Predictor Outlier Detection Graphs (dot charts)
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Appendix B – Correlograms from fire & grazing parametric analysis 

Interpretation of the correlograms merits some explanation. On the y-axis is a scale from -1 to 

1 providing a measure of correlation equivalent to a Pearson's r or Kendall's tau. Along the xaxis is distance. In the case of the correlogram for Baird's Sparrow, the central line tracks 

quite close to 0, indicating very low correlation. The confidence intervals do not cross the zero 

line, thus the weak correlation that exists, is not statistically significant. In the case of Western 

Meadowlark however there is a spike at 500m where both the central line and the lower 

confidence interval are above zero indicating spatial auto-correlation in the model residuals. 

B.1 Baird's Sparrow GLM Model Residuals 

B.2 Brown-headed Cowbird GLM Model Residuals 
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B.3 Brewer's Sparrow GLM Model Residuals 

B.4 Chestnut-colored Longspur GLM Model Residuals 
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B.5 Clay-colored Sparrow GLM Model Residuals 

B.6 Grasshopper Sparrow GLM Model Residuals 
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B.7 Horned Lark GLM Model Residuals 

B.8 Long-billed Curlew GLM Model Residuals 
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B.9 Lark Bunting GLM Model Residuals 

B.10 Sprague's Pipit GLM Model Residuals 
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B.12 Upland Sandpiper GLM Model Residuals 

B.12 Vesper Sparrow GLM Model Residuals 
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B.13 Western Meadowlark Model Residuals 

B.13.1 Initial GLM 

B.13.2 GLM with adjacency parameter 
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B.13.3 GLMM without adjacency parameter 
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Appendix C – Wetland analysis variable description 

Table 45: Wetland analysis variable definitions

Variable Description

wet1_500kd Amount of saline / barren wetland within 500m 

wet1_1000kd Amount of saline / barren wetland within 1000m 

wet1_2500kd Amount of saline / barren wetland within 2500m 

wet1_5000kd Amount of saline / barren wetland within 5000m 

wet2_500kd Amount of moist wetland within 500m 

wet2_1000kd Amount of moist wetland within 1000m 

wet2_2500kd Amount of moist wetland within 2500m 

wet2_5000kd Amount of moist wetland within 5000m 

wet3_500kd Amount of herbaceous wetland within 500m

wet3_1000kd Amount of herbaceous wetland within 1000m 

wet3_2500kd Amount of herbaceous wetland within 2500m 

wet3_5000kd Amount of herbaceous wetland within 5000m 

wet4_500kd Amount of open water within 500m 

wet4_1000kd Amount of open water within 1000m 

wet4_2500kd Amount of open water within 2500m 

wet4_5000kd Amount of open water within 5000m 

wet1_500_cnt Number of saline / barren wetlands within 500m 

wet1_1000_cnt Number of saline / barren wetlands from 500 to 1000m 

wet1_2500_cnt Number of saline / barren wetlands from 1000 to 2500m 

wet1_5000_cnt Number of saline / barren wetlands from 2500 to 5000m 

wet2_500_cnt Number of moist wetlands within 500m 

wet2_1000_cnt Number of moist wetlands from 500 to 1000m 

wet2_2500_cnt Number of moist wetlands from 1000 to 2500m 

wet2_5000_cnt Number of moist wetlands from 2500 to 5000m 

wet3_500_cnt Number of herbaceous wetlands within 500m 

wet3_1000_cnt Number of herbaceous wetlands from 500 to 1000m 

wet3_2500_cnt Number of herbaceous wetlands from 1000 to 2500m 

wet3_5000_cnt Number of herbaceous wetlands from 2500 to 5000m 

wet4_500_cnt Number of open water wetlands within 500m 

wet4_1000_cnt Number of open water wetlands from 500 to 1000m 

wet4_2500_cnt Number of open water wetlands from 1000 to 2500m 

wet4_5000_cnt Number of open water wetlands from 2500 to 5000m 
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Table 45 continued

Variable Description

wet1_all_cnt Number of saline / barren wetlands within 10,000m 

wet2_all_cnt Number of moist wetlands within 10,000m 

wet3_all_cnt Number of herbaceous wetlands within 10,000m 

wet4_all_cnt Number of open water wetlands within 10,000m 

tro_all_cnt Number of water troughs within a field 

dug_all_cnt Number of dugouts within a field 

wet1_all_xd Mean distance of saline / barren wetlands within 10,000m 

wet2_all_xd Mean distance of moist wetlands within 10,000m 

wet3_all_xd Mean distance of herbaceous wetlands within 10,000m 

wet4_all_xd Mean distance of open water wetlands within 10,000m 

tro_all_xd Mean distance of water troughs within a field 

dug_all_xd Mean distance of dugouts within a field 

wet1_500_if_cnt Number of saline / barren wetlands within a field and within 500m 

wet1_1000_if_cnt Number of saline / barren wetlands within a field and from 500 to 1000m 

wet1_2500_if_cnt Number of saline / barren wetlands within a field and from 100 to 2500m 

wet1_5000_if_cnt Number of saline / barren wetlands within a field and from 2500 to 5000m 

wet2_500_if_cnt Number of moist wetlands within a field and within 500m 

wet2_1000_if_cnt Number of moist wetlands within a field and from 500 to 1000m 

wet2_2500_if_cnt Number of moist wetlands within a field and from 1000 to 2500m 

wet2_5000_if_cnt Number of moist wetlands within a field and from 2500 to 5000m 

wet3_500_if_cnt Number of herbaceous wetlands within a field and within 500m 

wet3_1000_if_cnt Number of herbaceous wetlands within a field and from 500 to 1000m 

wet3_2500_if_cnt Number of herbaceous wetlands within a field and from 1000 to 2500m 

wet3_5000_if_cnt Number of herbaceous wetlands within a field and from 2500 to 5000m 
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Variable Description

wet4_500_if_cnt Number of open water wetlands within a field and within 500m 

wet4_1000_if_cnt Number of open water wetlands within a field and from 500 to 1000m 

wet4_2500_if_cnt Number of open water wetlands within a field and from 1000 to 2500m 

wet4_5000_if_cnt Number of open water wetlands within a field and from 2500 to 5000m 

wet1_all_if_cnt Number of saline / barren wetlands within a field 

wet2_all_if_cnt Number of moist wetlands within a field 

wet3_all_if_cnt Number of herbaceous wetlands within a field

Table 45 continued

Variable Description

wet4_all_if_cnt Number of open water wetlands within a field 

wet1_all_if_xd Mean distance of saline / barren wetlands within a field 

wet2_all_if_xd Mean distance of moist wetlands within a field 

wet3_all_if_xd Mean distance of herbaceous wetlands within a field 

wet4_all_if_xd Mean distance of open water wetlands within a field
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Appendix D – Descriptive graphs of wetland data 

D.1 Species Distributions 
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D.2 Predictor Distributions 

D.2.1 Wetland Area (# of 30m x 30m units) within Distances 
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D.2.2 Wetland Number within Distance Ranges 
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D.2.3 Wetland Number within 10 km 
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D.2.4 Wetland Mean Distances within 10 km 

D.2.5 Wetland Number 
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within Distance Ranges in Field 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 113 / 189
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 114 / 189

D.2.6 Wetland Number 

in Field 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 115 / 189

D.2.7 Wetland Distance in Field 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 116 / 189

D.2.8 In Field Wetland Density 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 117 / 189

D.2.9 Other Measures 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 118 / 189
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 119 / 189

D.3 Species Outlier Detection Graphs (dot charts) 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 120 / 189
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 121 / 189

D.4 Predictor Outlier Detection Graphs (dot charts)

D.4.1 Wetland Area (# of 30 x 30 pixels) within Distances 

 Barren / Saline Wetlands Moist Wetlands 

 Herbaceous Wetlands Open Water Wetlands 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 122 / 189

D.4.2 Wetland Number within Distance Ranges 

 Barren / Saline Wetlands Moist Wetlands 

 Herbaceous Wetlands Open Water Wetlands 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 123 / 189

D.4.3 Wetland Number within 10 km 

D.4.4 Mean Wetland Distances within 10km 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 124 / 189

D.4.5 Wetland Number within Distance Ranges in Field 

 Barren / Saline Wetlands Moist Wetlands 

 Herbaceous Wetlands Open Water Wetlands 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 125 / 189

D.4.6 Wetland Number in Field 

D.4.7 Wetland Distance in Field 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 126 / 189

D.4.8 In Field Wetland Density 

D.4.9 Other Measures 

Appendix E – Wetland Non-
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 127 / 189

parametric Results 

Table E.1: Baird's Sparrow abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub -0.145; <0.001 mrps 0.117; <0.001 topo -0.137; <0.001

wet1_500kd -0.047; 0.173 wet1_1000kd -0.108; 0.001 wet1_2500kd 0.004; 0.903

wet1_5000kd 0.039; 0.178 wet2_500kd -0.059; 0.060 wet2_1000kd -0.083; 0.005

wet2_2500kd 0.015; 0.594 wet2_5000kd 0.017; 0.554 wet3_500kd 0.019; 0.561

wet3_1000kd 0.016; 0.592 wet3_2500kd 0.050; 0.079 wet3_5000kd 0.078; 0.007

wet4_500kd -0.045; 0.185 wet4_1000kd -0.089; 0.007 wet4_2500kd 0.007; 0.813

wet4_5000kd 0.007; 0.817 wet1_500_cnt -0.056; 0.107 wet1_1000_cnt -0.116; <0.001

wet1_2500_cnt -0.000; 0.996 wet1_5000_cnt 0.020; 0.488 wet2_500_cnt -0.065; 0.043

wet2_1000_cnt -0.080; 0.008 wet2_2500_cnt 0.046; 0.107 wet2_5000_cnt 0.138; <0.001

wet3_500_cnt 0.026; 0.430 wet3_1000_cnt 0.017; 0.578 wet3_2500_cnt 0.103; <0.001

wet3_5000_cnt 0.119; <0.001 wet4_500_cnt -0.055; 0.113 wet4_1000_cnt -0.101; 0.003

wet4_2500_cnt 0.010; 0.747 wet4_5000_cnt -0.027; 0.354 wet1_all_cnt 0.021; 0.453

wet2_all_cnt 0.177; <0.001 wet3_all_cnt 0.177; <0.001 wet4_all_cnt 0.012; 0.662

field_size 0.063; 0.039 tro_all_cnt 0.097; 0.003 dug_all_cnt 0.114; <0.001

wet1_all_xd 0.083; 0.004 wet2_all_xd -0.040; 0.158 wet3_all_xd -0.029; 0.303

wet4_all_xd 0.100; <0.001 tro_all_xd 0.115; <0.001 dug_all_xd 0.042; 0.175

wet1_500_if_cnt -0.038; 0.270 wet1_1000_if_cnt -0.079; 0.022 wet1_2500_if_cnt -0.037; 0.277

wet1_5000_if_cnt 0.024; 0.460 wet2_500_if_cnt -0.079; 0.015 wet2_1000_if_cnt -0.077; 0.012

wet2_2500_if_cnt -0.005; 0.874 wet2_5000_if_cnt 0.097; <0.001 wet3_500_if_cnt 0.003; 0.925

wet3_1000_if_cnt 0.018; 0.588 wet3_2500_if_cnt 0.100; <0.001 wet3_5000_if_cnt 0.096; 0.001

wet4_500_if_cnt -0.063; 0.066 wet4_1000_if_cnt -0.069; 0.042 wet4_2500_if_cnt -0.018; 0.574

wet4_5000_if_cnt 0.127; <0.001 wet1_all_if_xd 0.010; 0.729 wet2_all_if_xd 0.082; 0.006

wet3_all_if_xd -0.009; 0.770 wet4_all_if_xd 0.010; 0.749 wet1_if_density 0.034; 0.264

wet2_if_density 0.040; 0.197 wet3_if_density 0.038; 0.209 wet4_if_density 0.006; 0.850

*Kendall's Rank Correlation Test. Bold indicates p-values <= 0.01
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 128 / 189

Table E.2: Brewer's Sparrow abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub 0.199; <0.001 mrps 0.047; 0.119 topo 0.114; <0.001

wet1_500kd 0.020; 0.571 wet1_1000kd -0.035; 0.312 wet1_2500kd -0.119; <0.001

wet1_5000kd -0.058; 0.052 wet2_500kd -0.075; 0.019 wet2_1000kd -0.042; 0.160

wet2_2500kd 0.000; 0.988 wet2_5000kd -0.025; 0.389 wet3_500kd -0.010; 0.778

wet3_1000kd 0.010; 0.754 wet3_2500kd 0.005; 0.875 wet3_5000kd 0.060; 0.042

wet4_500kd -0.024; 0.495 wet4_1000kd -0.029; 0.402 wet4_2500kd -0.066; 0.032

wet4_5000kd 0.083; 0.005 wet1_500_cnt 0.042; 0.236 wet1_1000_cnt -0.023; 0.505

wet1_2500_cnt -0.104; 0.001 wet1_5000_cnt 0.005; 0.871 wet2_500_cnt -0.076; 0.021

wet2_1000_cnt -0.039; 0.214 wet2_2500_cnt -0.069; 0.019 wet2_5000_cnt 0.022; 0.451

wet3_500_cnt -0.022; 0.510 wet3_1000_cnt -0.012; 0.707 wet3_2500_cnt -0.021; 0.480

wet3_5000_cnt 0.062; 0.034 wet4_500_cnt -0.005; 0.879 wet4_1000_cnt -0.006; 0.858

wet4_2500_cnt -0.088; 0.006 wet4_5000_cnt 0.087; 0.004 wet1_all_cnt 0.022; 0.463

wet2_all_cnt -0.014; 0.628 wet3_all_cnt 0.049; 0.097 wet4_all_cnt 0.143; <0.001

field_size -0.125; <0.001 tro_all_cnt -0.124; <0.001 dug_all_cnt -0.045; 0.153

wet1_all_xd -0.046; 0.115 wet2_all_xd -0.008; 0.783 wet3_all_xd -0.033; 0.253

wet4_all_xd 0.005; 0.856 tro_all_xd 0.093; 0.005 dug_all_xd 0.022; 0.500

wet1_500_if_cnt 0.046; 0.193 wet1_1000_if_cnt -0.013; 0.722 wet1_2500_if_cnt -0.087; 0.012

wet1_5000_if_cnt -0.091; 0.007 wet2_500_if_cnt -0.093; 0.005 wet2_1000_if_cnt -0.070; 0.028

wet2_2500_if_cnt -0.162; <0.001 wet2_5000_if_cnt -0.172; <0.001 wet3_500_if_cnt -0.021; 0.542

wet3_1000_if_cnt -0.053; 0.113 wet3_2500_if_cnt -0.118; <0.001 wet3_5000_if_cnt 0.032; 0.291

wet4_500_if_cnt 0.016; 0.657 wet4_1000_if_cnt 0.030; 0.397 wet4_2500_if_cnt -0.097; 0.004

wet4_5000_if_cnt 0.010; 0.746 wet1_all_if_xd 0.024; 0.447 wet2_all_if_xd 0.073; 0.018

wet3_all_if_xd 0.113; <0.001 wet4_all_if_xd 0.095; 0.002 wet1_if_density -0.072; 0.023

wet2_if_density -0.091; 0.004 wet3_if_density 0.133; <0.001 wet4_if_density 0.119; <0.001

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 129 / 189

Table E.3: Chestnut-collared Longspur abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub -0.170; <0.001 mrps 0.078; 0.004 topo -0.428; <0.001

wet1_500kd -0.104; 0.001 wet1_1000kd -0.119; <0.001 wet1_2500kd -0.017; 0.549

wet1_5000kd -0.088; 0.001 wet2_500kd 0.167; <0.001 wet2_1000kd 0.289; <0.001

wet2_2500kd 0.324; <0.001 wet2_5000kd 0.423; <0.001 wet3_500kd -0.066; 0.030

wet3_1000kd -0.030; 0.295 wet3_2500kd 0.002; 0.939 wet3_5000kd -0.086; 0.001

wet4_500kd -0.090; 0.005 wet4_1000kd -0.067; 0.029 wet4_2500kd -0.149; <0.001

wet4_5000kd -0.345; <0.001 wet1_500_cnt -0.120; <0.001 wet1_1000_cnt -0.135; <0.001

wet1_2500_cnt -0.123; <0.001 wet1_5000_cnt -0.239; <0.001 wet2_500_cnt 0.117; <0.001

wet2_1000_cnt 0.182; <0.001 wet2_2500_cnt 0.161; <0.001 wet2_5000_cnt 0.111; <0.001

wet3_500_cnt -0.072; 0.020 wet3_1000_cnt -0.032; 0.271 wet3_2500_cnt -0.004; 0.875

wet3_5000_cnt -0.055; 0.037 wet4_500_cnt -0.081; 0.011 wet4_1000_cnt -0.084; 0.008

wet4_2500_cnt -0.169; <0.001 wet4_5000_cnt -0.409; <0.001 wet1_all_cnt -0.214; <0.001

wet2_all_cnt 0.209; <0.001 wet3_all_cnt 0.083; 0.002 wet4_all_cnt -0.478; <0.001

field_size 0.427; <0.001 tro_all_cnt 0.323; <0.001 dug_all_cnt 0.231; <0.001

wet1_all_xd 0.152; <0.001 wet2_all_xd -0.032; 0.220 wet3_all_xd 0.146; <0.001

wet4_all_xd 0.310; <0.001 tro_all_xd 0.181; <0.001 dug_all_xd 0.109; <0.001

wet1_500_if_cnt -0.067; 0.039 wet1_1000_if_cnt -0.010; 0.767 wet1_2500_if_cnt 0.046; 0.140

wet1_5000_if_cnt 0.145; <0.001 wet2_500_if_cnt 0.206; <0.001 wet2_1000_if_cnt 0.307; <0.001

wet2_2500_if_cnt 0.354; <0.001 wet2_5000_if_cnt 0.296; <0.001 wet3_500_if_cnt -0.007; 0.816

wet3_1000_if_cnt 0.036; 0.234 wet3_2500_if_cnt 0.175; <0.001 wet3_5000_if_cnt 0.004; 0.873

wet4_500_if_cnt -0.015; 0.649 wet4_1000_if_cnt 0.002; 0.950 wet4_2500_if_cnt 0.023; 0.457

wet4_5000_if_cnt 0.009; 0.766 wet1_all_if_xd -0.113; <0.001 wet2_all_if_xd -0.166; <0.001

wet3_all_if_xd -0.227; <0.001 wet4_all_if_xd -0.130; <0.001 wet1_if_density 0.056; 0.049

wet2_if_density 0.233; <0.001 wet3_if_density -0.183; <0.001 wet4_if_density -0.153; <0.001

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 130 / 189

Table E.4: Clay-colored Sparrow abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub 0.127; <0.001 mrps 0.006; 0.855 topo 0.081; 0.006

wet1_500kd 0.034; 0.344 wet1_1000kd -0.003; 0.935 wet1_2500kd -0.019; 0.543

wet1_5000kd -0.007; 0.817 wet2_500kd -0.067; 0.040 wet2_1000kd -0.081; 0.007

wet2_2500kd -0.029; 0.329 wet2_5000kd -0.059; 0.046 wet3_500kd -0.009; 0.799

wet3_1000kd -0.001; 0.966 wet3_2500kd 0.028; 0.345 wet3_5000kd 0.038; 0.193

wet4_500kd 0.041; 0.249 wet4_1000kd -0.021; 0.533 wet4_2500kd -0.007; 0.830

wet4_5000kd 0.072; 0.015 wet1_500_cnt 0.030; 0.397 wet1_1000_cnt 0.006; 0.874

wet1_2500_cnt -0.010; 0.768 wet1_5000_cnt 0.024; 0.434 wet2_500_cnt -0.078; 0.018

wet2_1000_cnt -0.068; 0.030 wet2_2500_cnt -0.037; 0.209 wet2_5000_cnt 0.004; 0.898

wet3_500_cnt -0.023; 0.510 wet3_1000_cnt -0.029; 0.375 wet3_2500_cnt 0.006; 0.841

wet3_5000_cnt 0.032; 0.272 wet4_500_cnt 0.040; 0.263 wet4_1000_cnt -0.005; 0.883

wet4_2500_cnt 0.001; 0.968 wet4_5000_cnt 0.079; 0.009 wet1_all_cnt 0.061; 0.038

wet2_all_cnt -0.013; 0.666 wet3_all_cnt 0.013; 0.666 wet4_all_cnt 0.121; <0.001

field_size -0.117; <0.001 tro_all_cnt -0.103; 0.002 dug_all_cnt -0.087; 0.006

wet1_all_xd -0.011; 0.710 wet2_all_xd 0.022; 0.454 wet3_all_xd -0.012; 0.684

wet4_all_xd -0.005; 0.873 tro_all_xd 0.057; 0.085 dug_all_xd -0.003; 0.923

wet1_500_if_cnt 0.055; 0.125 wet1_1000_if_cnt 0.006; 0.875 wet1_2500_if_cnt -0.057; 0.104

wet1_5000_if_cnt -0.091; 0.007 wet2_500_if_cnt -0.071; 0.033 wet2_1000_if_cnt -0.076; 0.017

wet2_2500_if_cnt -0.121; <0.001 wet2_5000_if_cnt -0.131; <0.001 wet3_500_if_cnt -0.033; 0.342

wet3_1000_if_cnt -0.043; 0.200 wet3_2500_if_cnt -0.076; 0.015 wet3_5000_if_cnt -0.031; 0.308

wet4_500_if_cnt 0.027; 0.442 wet4_1000_if_cnt -0.006; 0.873 wet4_2500_if_cnt -0.074; 0.029

wet4_5000_if_cnt -0.008; 0.817 wet1_all_if_xd 0.056; 0.072 wet2_all_if_xd 0.091; 0.003

wet3_all_if_xd 0.095; 0.002 wet4_all_if_xd 0.093; 0.003 wet1_if_density 0.003; 0.926

wet2_if_density -0.047; 0.138 wet3_if_density 0.081; 0.011 wet4_if_density 0.099; 0.002

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 131 / 189

Table E.5: Grasshopper Sparrow abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub -0.036; 0.199 mrps 0.104; <0.001 topo 0.118; <0.001

wet1_500kd -0.049; 0.145 wet1_1000kd -0.050; 0.125 wet1_2500kd -0.008; 0.782

wet1_5000kd 0.032; 0.265 wet2_500kd -0.238; <0.001 wet2_1000kd -0.233; <0.001

wet2_2500kd -0.171; <0.001 wet2_5000kd -0.194; <0.001 wet3_500kd -0.067; 0.034

wet3_1000kd -0.031; 0.299 wet3_2500kd -0.030; 0.279 wet3_5000kd 0.006; 0.818

wet4_500kd -0.050; 0.133 wet4_1000kd -0.083; 0.011 wet4_2500kd 0.007; 0.814

wet4_5000kd 0.068; 0.014 wet1_500_cnt -0.043; 0.198 wet1_1000_cnt -0.048; 0.148

wet1_2500_cnt 0.042; 0.171 wet1_5000_cnt 0.085; 0.003 wet2_500_cnt -0.223; <0.001

wet2_1000_cnt -0.184; <0.001 wet2_2500_cnt -0.138; <0.001 wet2_5000_cnt -0.036; 0.200

wet3_500_cnt -0.071; 0.028 wet3_1000_cnt -0.024; 0.426 wet3_2500_cnt 0.002; 0.944

wet3_5000_cnt -0.014; 0.618 wet4_500_cnt -0.051; 0.131 wet4_1000_cnt -0.079; 0.017

wet4_2500_cnt 0.024; 0.428 wet4_5000_cnt 0.088; 0.002 wet1_all_cnt 0.098; <0.001

wet2_all_cnt -0.017; 0.535 wet3_all_cnt 0.031; 0.270 wet4_all_cnt 0.216; <0.001

field_size -0.046; 0.126 tro_all_cnt -0.053; 0.095 dug_all_cnt 0.091; 0.003

wet1_all_xd -0.062; 0.025 wet2_all_xd 0.098; <0.001 wet3_all_xd 0.014; 0.618

wet4_all_xd -0.013; 0.636 tro_all_xd -0.026; 0.395 dug_all_xd 0.020; 0.511

wet1_500_if_cnt -0.012; 0.727 wet1_1000_if_cnt -0.058; 0.085 wet1_2500_if_cnt -0.048; 0.145

wet1_5000_if_cnt -0.115; <0.001 wet2_500_if_cnt -0.250; <0.001 wet2_1000_if_cnt -0.214; <0.001

wet2_2500_if_cnt -0.255; <0.001 wet2_5000_if_cnt -0.118; <0.001 wet3_500_if_cnt -0.098; 0.003

wet3_1000_if_cnt -0.069; 0.028 wet3_2500_if_cnt -0.064; 0.031 wet3_5000_if_cnt -0.030; 0.299

wet4_500_if_cnt -0.050; 0.134 wet4_1000_if_cnt -0.096; 0.004 wet4_2500_if_cnt -0.066; 0.038

wet4_5000_if_cnt -0.042; 0.170 wet1_all_if_xd 0.044; 0.135 wet2_all_if_xd 0.232; <0.001

wet3_all_if_xd 0.075; 0.011 wet4_all_if_xd -0.007; 0.824 wet1_if_density -0.047; 0.115

wet2_if_density -0.154; <0.001 wet3_if_density 0.014; 0.634 wet4_if_density -0.053; 0.076

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 132 / 189

Table E.6: Horned Lark abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub -0.086; 0.002 mrps -0.036; 0.200 topo -0.062; 0.023

wet1_500kd -0.086; 0.009 wet1_1000kd -0.096; 0.003 wet1_2500kd 0.013; 0.648

wet1_5000kd 0.003; 0.907 wet2_500kd 0.101; <0.001 wet2_1000kd 0.135; <0.001

wet2_2500kd 0.070; 0.011 wet2_5000kd 0.093; <0.001 wet3_500kd -0.014; 0.665

wet3_1000kd 0.004; 0.890 wet3_2500kd -0.016; 0.568 wet3_5000kd -0.002; 0.939

wet4_500kd -0.048; 0.147 wet4_1000kd -0.014; 0.666 wet4_2500kd -0.024; 0.410

wet4_5000kd -0.078; 0.005 wet1_500_cnt -0.095; 0.004 wet1_1000_cnt -0.116; <0.001

wet1_2500_cnt 0.014; 0.655 wet1_5000_cnt -0.029; 0.299 wet2_500_cnt 0.108; <0.001

wet2_1000_cnt 0.141; <0.001 wet2_2500_cnt 0.069; 0.012 wet2_5000_cnt 0.046; 0.090

wet3_500_cnt -0.005; 0.871 wet3_1000_cnt 0.035; 0.239 wet3_2500_cnt 0.013; 0.637

wet3_5000_cnt 0.012; 0.671 wet4_500_cnt -0.057; 0.082 wet4_1000_cnt -0.037; 0.251

wet4_2500_cnt -0.016; 0.579 wet4_5000_cnt -0.078; 0.005 wet1_all_cnt -0.008; 0.762

wet2_all_cnt 0.100; <0.001 wet3_all_cnt 0.054; 0.050 wet4_all_cnt -0.127; <0.001

field_size 0.097; <0.001 tro_all_cnt 0.099; 0.001 dug_all_cnt 0.072; 0.015

wet1_all_xd 0.078; 0.004 wet2_all_xd -0.022; 0.413 wet3_all_xd 0.035; 0.205

wet4_all_xd 0.042; 0.123 tro_all_xd -0.031; 0.310 dug_all_xd -0.097; 0.001

wet1_500_if_cnt -0.065; 0.051 wet1_1000_if_cnt -0.080; 0.015 wet1_2500_if_cnt 0.075; 0.020

wet1_5000_if_cnt 0.038; 0.222 wet2_500_if_cnt 0.147; <0.001 wet2_1000_if_cnt 0.188; <0.001

wet2_2500_if_cnt 0.211; <0.001 wet2_5000_if_cnt 0.094; <0.001 wet3_500_if_cnt 0.032; 0.318

wet3_1000_if_cnt 0.071; 0.023 wet3_2500_if_cnt 0.126; <0.001 wet3_5000_if_cnt 0.023; 0.415

wet4_500_if_cnt -0.022; 0.498 wet4_1000_if_cnt -0.018; 0.578 wet4_2500_if_cnt 0.090; 0.004

wet4_5000_if_cnt -0.018; 0.548 wet1_all_if_xd -0.011; 0.711 wet2_all_if_xd -0.191; <0.001

wet3_all_if_xd -0.136; <0.001 wet4_all_if_xd -0.074; 0.011 wet1_if_density 0.031; 0.289

wet2_if_density 0.055; 0.061 wet3_if_density -0.062; 0.033 wet4_if_density -0.091; 0.002

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 133 / 189

Table E.7: Long-billed Curlew abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub -0.037; 0.210 mrps -0.064; 0.031 topo -0.037; 0.208

wet1_500kd -0.043; 0.223 wet1_1000kd -0.035; 0.306 wet1_2500kd -0.026; 0.401

wet1_5000kd -0.061; 0.042 wet2_500kd 0.056; 0.080 wet2_1000kd 0.055; 0.067

wet2_2500kd -0.012; 0.680 wet2_5000kd -0.041; 0.162 wet3_500kd -0.019; 0.574

wet3_1000kd -0.011; 0.717 wet3_2500kd -0.073; 0.013 wet3_5000kd -0.073; 0.013

wet4_500kd 0.010; 0.769 wet4_1000kd 0.039; 0.251 wet4_2500kd 0.033; 0.289

wet4_5000kd -0.015; 0.601 wet1_500_cnt -0.049; 0.166 wet1_1000_cnt -0.019; 0.587

wet1_2500_cnt -0.021; 0.507 wet1_5000_cnt -0.039; 0.197 wet2_500_cnt 0.079; 0.016

wet2_1000_cnt 0.061; 0.049 wet2_2500_cnt -0.003; 0.932 wet2_5000_cnt -0.094; 0.001

wet3_500_cnt -0.011; 0.748 wet3_1000_cnt 0.011; 0.726 wet3_2500_cnt -0.050; 0.090

wet3_5000_cnt -0.030; 0.306 wet4_500_cnt 0.003; 0.923 wet4_1000_cnt 0.032; 0.354

wet4_2500_cnt 0.011; 0.732 wet4_5000_cnt -0.031; 0.293 wet1_all_cnt -0.053; 0.072

wet2_all_cnt -0.067; 0.023 wet3_all_cnt -0.072; 0.014 wet4_all_cnt -0.027; 0.349

field_size -0.011; 0.731 tro_all_cnt 0.003; 0.933 dug_all_cnt -0.048; 0.131

wet1_all_xd 0.091; 0.002 wet2_all_xd 0.024; 0.412 wet3_all_xd -0.021; 0.471

wet4_all_xd -0.035; 0.231 tro_all_xd -0.065; 0.046 dug_all_xd 0.013; 0.675

wet1_500_if_cnt -0.029; 0.407 wet1_1000_if_cnt 0.011; 0.760 wet1_2500_if_cnt 0.025; 0.461

wet1_5000_if_cnt 0.025; 0.460 wet2_500_if_cnt 0.126; <0.001 wet2_1000_if_cnt 0.123; <0.001

wet2_2500_if_cnt 0.091; 0.002 wet2_5000_if_cnt -0.028; 0.348 wet3_500_if_cnt 0.030; 0.386

wet3_1000_if_cnt 0.068; 0.041 wet3_2500_if_cnt 0.016; 0.613 wet3_5000_if_cnt -0.018; 0.543

wet4_500_if_cnt 0.031; 0.386 wet4_1000_if_cnt 0.075; 0.033 wet4_2500_if_cnt 0.093; 0.006

wet4_5000_if_cnt -0.019; 0.550 wet1_all_if_xd 0.001; 0.987 wet2_all_if_xd -0.083; 0.007

wet3_all_if_xd -0.020; 0.521 wet4_all_if_xd -0.052; 0.090 wet1_if_density 0.030; 0.333

wet2_if_density 0.041; 0.185 wet3_if_density -0.026; 0.410 wet4_if_density 0.007; 0.828

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 134 / 189

Table E.8: Lark Bunting abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub 0.088; 0.003 mrps 0.104; <0.001 topo 0.062; 0.034

wet1_500kd -0.049; 0.169 wet1_1000kd -0.115; <0.001 wet1_2500kd -0.148; <0.001

wet1_5000kd -0.126; <0.001 wet2_500kd -0.111; <0.001 wet2_1000kd -0.104; <0.001

wet2_2500kd -0.037; 0.203 wet2_5000kd 0.043; 0.142 wet3_500kd -0.057; 0.086

wet3_1000kd -0.078; 0.013 wet3_2500kd -0.093; 0.002 wet3_5000kd 0.006; 0.828

wet4_500kd -0.067; 0.056 wet4_1000kd -0.113; <0.001 wet4_2500kd -0.154; <0.001

wet4_5000kd -0.039; 0.182 wet1_500_cnt -0.052; 0.140 wet1_1000_cnt -0.117; <0.001

wet1_2500_cnt -0.134; <0.001 wet1_5000_cnt -0.093; 0.002 wet2_500_cnt -0.113; <0.001

wet2_1000_cnt -0.128; <0.001 wet2_2500_cnt -0.114; <0.001 wet2_5000_cnt -0.053; 0.067

wet3_500_cnt -0.061; 0.072 wet3_1000_cnt -0.065; 0.042 wet3_2500_cnt -0.106; <0.001

wet3_5000_cnt -0.023; 0.422 wet4_500_cnt -0.070; 0.048 wet4_1000_cnt -0.103; 0.003

wet4_2500_cnt -0.150; <0.001 wet4_5000_cnt -0.019; 0.532 wet1_all_cnt -0.103; <0.001

wet2_all_cnt -0.061; 0.039 wet3_all_cnt 0.010; 0.742 wet4_all_cnt 0.026; 0.372

field_size -0.005; 0.882 tro_all_cnt -0.069; 0.036 dug_all_cnt 0.025; 0.434

wet1_all_xd -0.077; 0.008 wet2_all_xd 0.040; 0.169 wet3_all_xd 0.017; 0.553

wet4_all_xd 0.037; 0.207 tro_all_xd 0.027; 0.417 dug_all_xd 0.023; 0.480

wet1_500_if_cnt -0.032; 0.371 wet1_1000_if_cnt -0.093; 0.008 wet1_2500_if_cnt -0.128; <0.001

wet1_5000_if_cnt -0.111; <0.001 wet2_500_if_cnt -0.094; 0.004 wet2_1000_if_cnt -0.121; <0.001

wet2_2500_if_cnt -0.140; <0.001 wet2_5000_if_cnt -0.132; <0.001 wet3_500_if_cnt -0.072; 0.035

wet3_1000_if_cnt -0.117; <0.001 wet3_2500_if_cnt -0.129; <0.001 wet3_5000_if_cnt 0.028; 0.358

wet4_500_if_cnt -0.053; 0.132 wet4_1000_if_cnt -0.088; 0.012 wet4_2500_if_cnt -0.121; <0.001

wet4_5000_if_cnt -0.055; 0.087 wet1_all_if_xd 0.064; 0.040 wet2_all_if_xd 0.080; 0.009

wet3_all_if_xd 0.140; <0.001 wet4_all_if_xd 0.093; 0.002 wet1_if_density -0.185; <0.001

wet2_if_density -0.171; <0.001 wet3_if_density 0.058; 0.065 wet4_if_density -0.003; 0.927

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 135 / 189

Table E.9: Sprague's Pipit abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub -0.071; 0.011 mrps 0.024; 0.382 topo -0.011; 0.678

wet1_500kd -0.013; 0.687 wet1_1000kd 0.032; 0.319 wet1_2500kd 0.027; 0.362

wet1_5000kd 0.049; 0.083 wet2_500kd -0.092; 0.002 wet2_1000kd -0.127; <0.001

wet2_2500kd -0.137; <0.001 wet2_5000kd -0.143; <0.001 wet3_500kd 0.016; 0.621

wet3_1000kd 0.008; 0.792 wet3_2500kd -0.051; 0.064 wet3_5000kd -0.007; 0.800

wet4_500kd -0.039; 0.240 wet4_1000kd -0.009; 0.775 wet4_2500kd 0.035; 0.230

wet4_5000kd 0.070; 0.012 wet1_500_cnt -0.015; 0.653 wet1_1000_cnt 0.018; 0.576

wet1_2500_cnt 0.036; 0.239 wet1_5000_cnt 0.075; 0.008 wet2_500_cnt -0.074; 0.017

wet2_1000_cnt -0.074; 0.011 wet2_2500_cnt -0.070; 0.011 wet2_5000_cnt 0.017; 0.547

wet3_500_cnt 0.016; 0.610 wet3_1000_cnt 0.016; 0.601 wet3_2500_cnt 0.010; 0.725

wet3_5000_cnt 0.017; 0.530 wet4_500_cnt -0.046; 0.167 wet4_1000_cnt -0.035; 0.290

wet4_2500_cnt 0.030; 0.317 wet4_5000_cnt 0.075; 0.007 wet1_all_cnt 0.077; 0.005

wet2_all_cnt -0.006; 0.832 wet3_all_cnt 0.016; 0.568 wet4_all_cnt 0.118; <0.001

field_size -0.051; 0.084 tro_all_cnt 0.024; 0.445 dug_all_cnt -0.015; 0.618

wet1_all_xd 0.004; 0.876 wet2_all_xd 0.017; 0.528 wet3_all_xd -0.001; 0.977

wet4_all_xd 0.006; 0.831 tro_all_xd -0.043; 0.165 dug_all_xd -0.044; 0.146

wet1_500_if_cnt 0.010; 0.771 wet1_1000_if_cnt 0.035; 0.287 wet1_2500_if_cnt 0.064; 0.051

wet1_5000_if_cnt 0.021; 0.506 wet2_500_if_cnt -0.080; 0.010 wet2_1000_if_cnt -0.098; <0.001

wet2_2500_if_cnt -0.052; 0.063 wet2_5000_if_cnt 0.050; 0.072 wet3_500_if_cnt 0.026; 0.413

wet3_1000_if_cnt 0.024; 0.451 wet3_2500_if_cnt 0.032; 0.276 wet3_5000_if_cnt 0.034; 0.235

wet4_500_if_cnt -0.044; 0.187 wet4_1000_if_cnt -0.040; 0.228 wet4_2500_if_cnt 0.041; 0.193

wet4_5000_if_cnt 0.082; 0.007 wet1_all_if_xd -0.026; 0.381 wet2_all_if_xd 0.086; 0.003

wet3_all_if_xd 0.006; 0.831 wet4_all_if_xd -0.032; 0.269 wet1_if_density 0.100; <0.001

wet2_if_density 0.022; 0.456 wet3_if_density 0.008; 0.794 wet4_if_density 0.012; 0.681

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 136 / 189

Table E.10: Upland Sandpiper abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub 0.060; 0.046 mrps 0.053; 0.079 topo 0.141; <0.001

wet1_500kd -0.020; 0.580 wet1_1000kd -0.048; 0.169 wet1_2500kd -0.030; 0.349

wet1_5000kd -0.003; 0.918 wet2_500kd -0.089; 0.006 wet2_1000kd -0.144; <0.001

wet2_2500kd -0.114; <0.001 wet2_5000kd -0.126; <0.001 wet3_500kd 0.056; 0.100

wet3_1000kd 0.014; 0.655 wet3_2500kd 0.062; 0.036 wet3_5000kd 0.077; 0.009

wet4_500kd -0.007; 0.847 wet4_1000kd -0.039; 0.260 wet4_2500kd 0.039; 0.207

wet4_5000kd 0.095; 0.001 wet1_500_cnt -0.026; 0.471 wet1_1000_cnt -0.039; 0.274

wet1_2500_cnt 0.010; 0.766 wet1_5000_cnt 0.039; 0.193 wet2_500_cnt -0.066; 0.047

wet2_1000_cnt -0.091; 0.004 wet2_2500_cnt -0.036; 0.224 wet2_5000_cnt 0.004; 0.894

wet3_500_cnt 0.054; 0.113 wet3_1000_cnt 0.012; 0.705 wet3_2500_cnt 0.051; 0.087

wet3_5000_cnt 0.053; 0.075 wet4_500_cnt -0.013; 0.707 wet4_1000_cnt -0.055; 0.115

wet4_2500_cnt 0.048; 0.128 wet4_5000_cnt 0.109; <0.001 wet1_all_cnt 0.096; 0.001

wet2_all_cnt 0.025; 0.394 wet3_all_cnt 0.079; 0.007 wet4_all_cnt 0.170; <0.001

field_size -0.077; 0.015 tro_all_cnt -0.121; <0.001 dug_all_cnt 0.022; 0.485

wet1_all_xd -0.034; 0.244 wet2_all_xd 0.052; 0.075 wet3_all_xd -0.029; 0.322

wet4_all_xd -0.046; 0.115 tro_all_xd -0.000; 0.998 dug_all_xd 0.031; 0.336

wet1_500_if_cnt -0.052; 0.143 wet1_1000_if_cnt -0.066; 0.065 wet1_2500_if_cnt -0.085; 0.015

wet1_5000_if_cnt -0.132; <0.001 wet2_500_if_cnt -0.118; <0.001 wet2_1000_if_cnt -0.158; <0.001

wet2_2500_if_cnt -0.190; <0.001 wet2_5000_if_cnt -0.131; <0.001 wet3_500_if_cnt -0.025; 0.472

wet3_1000_if_cnt -0.083; 0.013 wet3_2500_if_cnt -0.064; 0.043 wet3_5000_if_cnt 0.002; 0.957

wet4_500_if_cnt -0.045; 0.205 wet4_1000_if_cnt -0.071; 0.046 wet4_2500_if_cnt -0.092; 0.007

wet4_5000_if_cnt -0.040; 0.224 wet1_all_if_xd 0.095; 0.002 wet2_all_if_xd 0.152; <0.001

wet3_all_if_xd 0.112; <0.001 wet4_all_if_xd 0.087; 0.005 wet1_if_density -0.095; 0.003

wet2_if_density -0.135; <0.001 wet3_if_density 0.077; 0.016 wet4_if_density -0.022; 0.489

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 137 / 189

Table E.11: Vesper Sparrow abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub 0.166; <0.001 mrps 0.036; 0.223 topo 0.235; <0.001

wet1_500kd -0.020; 0.563 wet1_1000kd -0.019; 0.568 wet1_2500kd -0.116; <0.001

wet1_5000kd -0.109; <0.001 wet2_500kd -0.177; <0.001 wet2_1000kd -0.200; <0.001

wet2_2500kd -0.183; <0.001 wet2_5000kd -0.141; <0.001 wet3_500kd -0.075; 0.023

wet3_1000kd -0.084; 0.006 wet3_2500kd -0.050; 0.082 wet3_5000kd 0.017; 0.555

wet4_500kd -0.010; 0.780 wet4_1000kd -0.048; 0.148 wet4_2500kd -0.047; 0.117

wet4_5000kd 0.068; 0.018 wet1_500_cnt -0.004; 0.902 wet1_1000_cnt 0.002; 0.950

wet1_2500_cnt -0.070; 0.029 wet1_5000_cnt -0.028; 0.334 wet2_500_cnt -0.167; <0.001

wet2_1000_cnt -0.164; <0.001 wet2_2500_cnt -0.200; <0.001 wet2_5000_cnt -0.102; <0.001

wet3_500_cnt -0.076; 0.022 wet3_1000_cnt -0.090; 0.005 wet3_2500_cnt -0.084; 0.004

wet3_5000_cnt -0.004; 0.876 wet4_500_cnt -0.004; 0.917 wet4_1000_cnt -0.011; 0.753

wet4_2500_cnt -0.046; 0.141 wet4_5000_cnt 0.106; <0.001 wet1_all_cnt 0.026; 0.366

wet2_all_cnt -0.085; 0.003 wet3_all_cnt -0.002; 0.952 wet4_all_cnt 0.213; <0.001

field_size -0.131; <0.001 tro_all_cnt -0.198; <0.001 dug_all_cnt -0.055; 0.078

wet1_all_xd 0.022; 0.442 wet2_all_xd 0.144; <0.001 wet3_all_xd 0.018; 0.530

wet4_all_xd -0.018; 0.522 tro_all_xd -0.069; 0.032 dug_all_xd -0.034; 0.278

wet1_500_if_cnt 0.012; 0.724 wet1_1000_if_cnt -0.030; 0.386 wet1_2500_if_cnt -0.068; 0.044

wet1_5000_if_cnt -0.174; <0.001 wet2_500_if_cnt -0.188; <0.001 wet2_1000_if_cnt -0.197; <0.001

wet2_2500_if_cnt -0.222; <0.001 wet2_5000_if_cnt -0.189; <0.001 wet3_500_if_cnt -0.081; 0.016

wet3_1000_if_cnt -0.106; 0.001 wet3_2500_if_cnt -0.100; 0.001 wet3_5000_if_cnt 0.020; 0.505

wet4_500_if_cnt 0.008; 0.816 wet4_1000_if_cnt -0.020; 0.554 wet4_2500_if_cnt -0.056; 0.089

wet4_5000_if_cnt -0.028; 0.384 wet1_all_if_xd 0.020; 0.520 wet2_all_if_xd 0.180; <0.001

wet3_all_if_xd 0.155; <0.001 wet4_all_if_xd 0.077; 0.011 wet1_if_density -0.155; <0.001

wet2_if_density -0.161; <0.001 wet3_if_density 0.152; <0.001 wet4_if_density 0.109; <0.001

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 138 / 189

Table E.12: Western Meadowlark abundance correlation to wetland predictors*

Measure tau, p value Measure tau, p value Measure tau, p value 

cws_shrub 0.075; 0.006 mrps -0.031; 0.261 topo 0.204; <0.001

wet1_500kd 0.045; 0.170 wet1_1000kd 0.006; 0.854 wet1_2500kd 0.021; 0.467

wet1_5000kd 0.074; 0.007 wet2_500kd -0.124; <0.001 wet2_1000kd -0.177; <0.001

wet2_2500kd -0.201; <0.001 wet2_5000kd -0.294; <0.001 wet3_500kd 0.039; 0.204

wet3_1000kd 0.001; 0.977 wet3_2500kd 0.004; 0.874 wet3_5000kd 0.040; 0.136

wet4_500kd 0.017; 0.611 wet4_1000kd 0.013; 0.669 wet4_2500kd 0.121; <0.001

wet4_5000kd 0.225; <0.001 wet1_500_cnt 0.066; 0.044 wet1_1000_cnt 0.000; 0.990

wet1_2500_cnt 0.087; 0.004 wet1_5000_cnt 0.164; <0.001 wet2_500_cnt -0.093; 0.002

wet2_1000_cnt -0.113; <0.001 wet2_2500_cnt -0.117; <0.001 wet2_5000_cnt -0.056; 0.040

wet3_500_cnt 0.047; 0.136 wet3_1000_cnt -0.005; 0.865 wet3_2500_cnt 0.025; 0.354

wet3_5000_cnt 0.049; 0.070 wet4_500_cnt 0.027; 0.399 wet4_1000_cnt -0.006; 0.862

wet4_2500_cnt 0.130; <0.001 wet4_5000_cnt 0.249; <0.001 wet1_all_cnt 0.154; <0.001

wet2_all_cnt -0.100; <0.001 wet3_all_cnt -0.013; 0.629 wet4_all_cnt 0.340; <0.001

field_size -0.280; <0.001 tro_all_cnt -0.175; <0.001 dug_all_cnt -0.160; <0.001

wet1_all_xd -0.086; 0.001 wet2_all_xd 0.044; 0.099 wet3_all_xd -0.078; 0.004

wet4_all_xd -0.184; <0.001 tro_all_xd -0.132; <0.001 dug_all_xd -0.048; 0.108

wet1_500_if_cnt 0.052; 0.113 wet1_1000_if_cnt -0.018; 0.584 wet1_2500_if_cnt 0.024; 0.461

wet1_5000_if_cnt -0.055; 0.079 wet2_500_if_cnt -0.143; <0.001 wet2_1000_if_cnt -0.189; <0.001

wet2_2500_if_cnt -0.219; <0.001 wet2_5000_if_cnt -0.145; <0.001 wet3_500_if_cnt 0.007; 0.832

wet3_1000_if_cnt -0.059; 0.058 wet3_2500_if_cnt -0.070; 0.015 wet3_5000_if_cnt 0.030; 0.276

wet4_500_if_cnt 0.035; 0.286 wet4_1000_if_cnt -0.007; 0.833 wet4_2500_if_cnt 0.032; 0.304

wet4_5000_if_cnt 0.049; 0.097 wet1_all_if_xd 0.067; 0.019 wet2_all_if_xd 0.128; <0.001

wet3_all_if_xd 0.144; <0.001 wet4_all_if_xd 0.066; 0.021 wet1_if_density 0.048; 0.100

wet2_if_density -0.060; 0.039 wet3_if_density 0.146; <0.001 wet4_if_density 0.148; <0.001

*Kendall's Rank Correlation Test, Bold indicates p-values <= 0.01 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 139 / 189

Appendix F –Correlograms from Wetland Analysis 

Interpretation of the correlograms merits some explanation. On the y-axis is a scale from -1 to 

1 providing a measure of correlation equivalent to a Pearson's r or Kendall's tau. Along the xaxis is distance. In the case of the correlogram for Brewer's Sparrow, the central line tracks 

quite close to 0, indicating very low correlation. The confidence intervals do not cross the zero 

line, thus the weak correlation that exists, is not statistically significant. In the case of Baird's 

Sparrow however there is a small spike at 500m where both the central line and the lower 

confidence interval are above zero indicating minor spatial auto-correlation in the model 

residuals. 

F.1 Baird's Sparrow GLM Model Residuals 

F.2 Brewer's Sparrow GLM Model Residuals 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 140 / 189

F.3 Chestnut-collared Longspur GLM Model Residuals 

F.3.1 Chestnut-collared Longspur GLM without adjacency measure 

F.3.2 Chestnut-collared Longspur GLM with adjacency measure 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 141 / 189

F.4 Clay-colored Sparrow GLM Model Residuals 

F.5 Grasshopper Sparrow GLM Model Residuals 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 142 / 189

F.6 Horned Lark GLM Model Residuals 

F.7 Long-billed Curlew GLM Model Residuals 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 143 / 189

F.8 Lark Bunting GLM Model Residuals 

F.9 Sprague's Pipit GLM Model Residuals 
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 144 / 189

F.10 Upland Sandpiper GLM Model Residuals 

F.11 Vesper Sparrow GLM Model Residuals 

F.11.1 Vesper Sparrow GLM without adjacency measure
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 145 / 189

F.11.2 Vesper Sparrow GLM with adjacency measure 

F.12 Western Meadowlark Sparrow GLM Model Residuals
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 146 / 189

Appendix G – Correlations between wetland predictors 

Tau values > 0.7 are in bold. 

Table G.1: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors cws_shrub mrps topo wet1_500kd 

cws_shrub 1.000;<0.001 -0.122;<0.001 0.152;<0.001 0.053; 0.074

mrps -0.122;<0.001 1.000;<0.001 -0.292;<0.001 -0.086; 0.004

topo 0.152;<0.001 -0.292;<0.001 1.000;<0.001 0.122;<0.001

wet1_500kd 0.053; 0.074 -0.086; 0.004 0.122;<0.001 1.000;<0.001

wet1_1000kd 0.021; 0.466 -0.119;<0.001 0.186;<0.001 0.543;<0.001

wet1_2500kd -0.138;<0.001 -0.069; 0.010 0.127;<0.001 0.253;<0.001

wet1_5000kd -0.054; 0.033 0.045; 0.076 0.028; 0.250 0.171;<0.001

wet2_500kd -0.020; 0.459 -0.128;<0.001 -0.059; 0.026 0.190;<0.001

wet2_1000kd -0.009; 0.717 -0.167;<0.001 -0.081; 0.001 0.103;<0.001

wet2_2500kd 0.030; 0.230 -0.118;<0.001 -0.130;<0.001 0.026; 0.382

wet2_5000kd 0.010; 0.678 0.041; 0.096 -0.247;<0.001 -0.116;<0.001

wet3_500kd -0.061; 0.032 -0.057; 0.045 0.146;<0.001 0.273;<0.001

wet3_1000kd -0.106;<0.001 -0.004; 0.891 0.126;<0.001 0.209;<0.001

wet3_2500kd -0.121;<0.001 0.058; 0.018 0.044; 0.073 0.120;<0.001

wet3_5000kd -0.023; 0.344 0.128;<0.001 0.059; 0.015 0.022; 0.451

wet4_500kd 0.080; 0.007 -0.161;<0.001 0.135;<0.001 0.554;<0.001

wet4_1000kd 0.091; 0.001 -0.206;<0.001 0.171;<0.001 0.320;<0.001

wet4_2500kd -0.022; 0.390 -0.126;<0.001 0.157;<0.001 0.210;<0.001

wet4_5000kd 0.072; 0.004 -0.069; 0.005 0.260;<0.001 0.151;<0.001

wet1_500_cnt 0.063; 0.035 -0.104;<0.001 0.140;<0.001 0.955;<0.001

wet1_1000_cnt 0.042; 0.153 -0.133;<0.001 0.184;<0.001 0.301;<0.001

wet1_2500_cnt -0.125;<0.001 -0.084; 0.002 0.133;<0.001 0.186;<0.001

wet1_5000_cnt 0.022; 0.376 0.005; 0.843 0.133;<0.001 0.152;<0.001

wet2_500_cnt -0.019; 0.500 -0.147;<0.001 -0.025; 0.350 0.207;<0.001

wet2_1000_cnt -0.038; 0.150 -0.175;<0.001 -0.012; 0.631 0.072; 0.020

wet2_2500_cnt -0.120;<0.001 -0.113;<0.001 -0.005; 0.828 0.075; 0.011

wet2_5000_cnt -0.020; 0.409 0.065; 0.008 -0.066; 0.006 -0.027; 0.357

wet3_500_cnt -0.067; 0.019 -0.071; 0.013 0.155;<0.001 0.277;<0.001

wet3_1000_cnt -0.125;<0.001 -0.019; 0.489 0.132;<0.001 0.127;<0.001

wet3_2500_cnt -0.099;<0.001 0.029; 0.248 0.035; 0.148 0.058; 0.052

wet3_5000_cnt 0.007; 0.788 0.023; 0.342 0.023; 0.343 -0.054; 0.066
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 147 / 189

Table G.1 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors cws_shrub mrps topo wet1_500kd 

wet4_500_cnt 0.097; 0.001 -0.170;<0.001 0.133;<0.001 0.569;<0.001

wet4_1000_cnt 0.093; 0.002 -0.202;<0.001 0.167;<0.001 0.187;<0.001

wet4_2500_cnt -0.037; 0.164 -0.115;<0.001 0.185;<0.001 0.206;<0.001

wet4_5000_cnt 0.078; 0.002 -0.026; 0.300 0.274;<0.001 0.146;<0.001

wet1_all_cnt -0.059; 0.017 -0.013; 0.599 0.195;<0.001 0.125;<0.001

wet2_all_cnt -0.172;<0.001 0.140;<0.001 -0.126;<0.001 -0.110;<0.001

wet3_all_cnt -0.109;<0.001 0.104;<0.001 -0.035; 0.148 -0.127;<0.001

wet4_all_cnt 0.091;<0.001 -0.021; 0.397 0.294;<0.001 0.119;<0.001

field_size -0.200;<0.001 0.166;<0.001 -0.306;<0.001 -0.166;<0.001

tro_all_cnt 0.044; 0.114 -0.031; 0.262 -0.239;<0.001 -0.105; 0.002

dug_all_cnt -0.205;<0.001 0.400;<0.001 -0.276;<0.001 -0.110;<0.001

wet1_all_xd -0.056; 0.024 -0.043; 0.083 -0.040; 0.094 -0.165;<0.001

wet2_all_xd -0.007; 0.766 0.062; 0.012 0.010; 0.685 -0.128;<0.001

wet3_all_xd -0.007; 0.763 0.029; 0.242 -0.082;<0.001 -0.075; 0.010

wet4_all_xd -0.048; 0.049 0.087;<0.001 -0.215;<0.001 -0.159;<0.001

tro_all_xd -0.090; 0.001 0.306;<0.001 -0.317;<0.001 -0.035; 0.286

dug_all_xd -0.096;<0.001 -0.049; 0.075 -0.161;<0.001 -0.129;<0.001

wet1_500_if_cnt 0.021; 0.489 -0.080; 0.008 0.072; 0.014 0.747;<0.001

wet1_1000_if_cnt -0.035; 0.247 -0.117;<0.001 0.077; 0.009 0.155;<0.001

wet1_2500_if_cnt -0.046; 0.114 -0.246;<0.001 0.082; 0.004 0.042; 0.225

wet1_5000_if_cnt -0.030; 0.283 -0.236;<0.001 -0.018; 0.511 -0.069; 0.039

wet2_500_if_cnt -0.043; 0.129 -0.147;<0.001 -0.091;<0.001 0.127;<0.001

wet2_1000_if_cnt -0.049; 0.067 -0.187;<0.001 -0.095;<0.001 -0.008; 0.791

wet2_2500_if_cnt -0.083;<0.001 -0.184;<0.001 -0.136;<0.001 -0.033; 0.272

wet2_5000_if_cnt -0.145;<0.001 -0.164;<0.001 -0.108;<0.001 -0.089; 0.003

wet3_500_if_cnt -0.088; 0.002 -0.081; 0.005 0.094;<0.001 0.239;<0.001

wet3_1000_if_cnt -0.144;<0.001 -0.064; 0.024 0.083; 0.003 0.115;<0.001

wet3_2500_if_cnt -0.156;<0.001 -0.004; 0.871 -0.028; 0.284 -0.006; 0.856

wet3_5000_if_cnt -0.076; 0.003 -0.037; 0.141 0.016; 0.507 -0.101;<0.001

wet4_500_if_cnt 0.064; 0.033 -0.154;<0.001 0.082; 0.005 0.369;<0.001

wet4_1000_if_cnt 0.034; 0.243 -0.196;<0.001 0.106;<0.001 0.133;<0.001

wet4_2500_if_cnt -0.042; 0.138 -0.248;<0.001 0.126;<0.001 0.091; 0.006

wet4_5000_if_cnt -0.087; 0.001 -0.109;<0.001 0.060; 0.025 -0.051; 0.112
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 148 / 189

Table G.1 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors cws_shrub mrps topo wet1_500kd 

wet1_all_if_xd 0.124;<0.001 -0.060; 0.021 0.100;<0.001 -0.094; 0.003

wet2_all_if_xd 0.044; 0.089 0.053; 0.041 0.060; 0.018 -0.136;<0.001

wet3_all_if_xd 0.103;<0.001 -0.036; 0.168 0.062; 0.015 -0.143;<0.001

wet4_all_if_xd 0.093;<0.001 -0.045; 0.084 0.079; 0.002 -0.121;<0.001

wet1_if_density 0.051; 0.056 -0.334;<0.001 0.074; 0.004 0.103; 0.001

wet2_if_density -0.001; 0.976 -0.320;<0.001 -0.034; 0.194 0.026; 0.411

wet3_if_density -0.029; 0.267 -0.022; 0.415 0.156;<0.001 0.012; 0.713

wet4_if_density 0.132;<0.001 -0.227;<0.001 0.191;<0.001 0.065; 0.038

Table G.2: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_1000kd wet1_2500kd wet1_5000kd wet2_500kd

cws_shrub 0.021; 0.466 -0.138;<0.001 -0.054; 0.033 -0.020; 0.459

mrps -0.119;<0.001 -0.069; 0.010 0.045; 0.076 -0.128;<0.001

topo 0.186;<0.001 0.127;<0.001 0.028; 0.250 -0.059; 0.026

wet1_500kd 0.543;<0.001 0.253;<0.001 0.171;<0.001 0.190;<0.001

wet1_1000kd 1.000;<0.001 0.367;<0.001 0.209;<0.001 0.139;<0.001

wet1_2500kd 0.367;<0.001 1.000;<0.001 0.429;<0.001 0.059; 0.039

wet1_5000kd 0.209;<0.001 0.429;<0.001 1.000;<0.001 0.037; 0.178

wet2_500kd 0.139;<0.001 0.059; 0.039 0.037; 0.178 1.000;<0.001

wet2_1000kd 0.161;<0.001 0.051; 0.057 0.042; 0.101 0.546;<0.001

wet2_2500kd 0.007; 0.806 0.087;<0.001 0.034; 0.167 0.341;<0.001

wet2_5000kd -0.181;<0.001 -0.086;<0.001 -0.045; 0.066 0.194;<0.001

wet3_500kd 0.269;<0.001 0.270;<0.001 0.227;<0.001 0.376;<0.001

wet3_1000kd 0.319;<0.001 0.285;<0.001 0.299;<0.001 0.200;<0.001

wet3_2500kd 0.154;<0.001 0.343;<0.001 0.413;<0.001 0.049; 0.067

wet3_5000kd -0.007; 0.815 0.136;<0.001 0.387;<0.001 -0.027; 0.312

wet4_500kd 0.354;<0.001 0.181;<0.001 0.155;<0.001 0.199;<0.001

wet4_1000kd 0.551;<0.001 0.263;<0.001 0.197;<0.001 0.132;<0.001

wet4_2500kd 0.289;<0.001 0.466;<0.001 0.365;<0.001 0.049; 0.080

wet4_5000kd 0.190;<0.001 0.244;<0.001 0.474;<0.001 -0.036; 0.182

wet1_500_cnt 0.565;<0.001 0.268;<0.001 0.187;<0.001 0.200;<0.001

wet1_1000_cnt 0.896;<0.001 0.340;<0.001 0.215;<0.001 0.081; 0.010

wet1_2500_cnt 0.199;<0.001 0.829;<0.001 0.499;<0.001 0.021; 0.484
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 149 / 189

Table G.2 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_1000kd wet1_2500kd wet1_5000kd wet2_500kd

wet1_5000_cnt 0.184;<0.001 0.271;<0.001 0.763;<0.001 0.004; 0.884

wet2_500_cnt 0.163;<0.001 0.107;<0.001 0.097;<0.001 0.830;<0.001

wet2_1000_cnt 0.183;<0.001 0.116;<0.001 0.141;<0.001 0.352;<0.001

wet2_2500_cnt 0.098;<0.001 0.300;<0.001 0.373;<0.001 0.214;<0.001

wet2_5000_cnt -0.060; 0.037 0.128;<0.001 0.352;<0.001 0.039; 0.139

wet3_500_cnt 0.267;<0.001 0.271;<0.001 0.237;<0.001 0.390;<0.001

wet3_1000_cnt 0.278;<0.001 0.261;<0.001 0.303;<0.001 0.124;<0.001

wet3_2500_cnt 0.053; 0.070 0.279;<0.001 0.424;<0.001 0.043; 0.113

wet3_5000_cnt -0.100;<0.001 -0.003; 0.906 0.244;<0.001 0.002; 0.927

wet4_500_cnt 0.361;<0.001 0.179;<0.001 0.159;<0.001 0.206;<0.001

wet4_1000_cnt 0.522;<0.001 0.260;<0.001 0.183;<0.001 0.079; 0.012

wet4_2500_cnt 0.249;<0.001 0.500;<0.001 0.402;<0.001 0.032; 0.259

wet4_5000_cnt 0.187;<0.001 0.218;<0.001 0.471;<0.001 -0.074; 0.006

wet1_all_cnt 0.183;<0.001 0.305;<0.001 0.482;<0.001 -0.036; 0.181

wet2_all_cnt -0.154;<0.001 0.031; 0.237 0.206;<0.001 0.017; 0.532

wet3_all_cnt -0.186;<0.001 -0.040; 0.123 0.112;<0.001 -0.039; 0.144

wet4_all_cnt 0.151;<0.001 0.116;<0.001 0.176;<0.001 -0.221;<0.001

field_size -0.222;<0.001 -0.174;<0.001 -0.232;<0.001 0.022; 0.446

tro_all_cnt -0.156;<0.001 -0.153;<0.001 -0.246;<0.001 0.099;<0.001

dug_all_cnt -0.187;<0.001 -0.138;<0.001 -0.095;<0.001 -0.129;<0.001

wet1_all_xd -0.171;<0.001 -0.249;<0.001 -0.486;<0.001 0.013; 0.622

wet2_all_xd -0.129;<0.001 -0.269;<0.001 -0.426;<0.001 -0.155;<0.001

wet3_all_xd -0.061; 0.033 -0.124;<0.001 -0.331;<0.001 -0.037; 0.168

wet4_all_xd -0.172;<0.001 -0.220;<0.001 -0.448;<0.001 0.001; 0.965

tro_all_xd -0.109;<0.001 0.004; 0.883 0.345;<0.001 0.007; 0.825

dug_all_xd -0.196;<0.001 -0.164;<0.001 -0.065; 0.017 -0.034; 0.244

wet1_500_if_cnt 0.441;<0.001 0.176;<0.001 0.094; 0.002 0.180;<0.001

wet1_1000_if_cnt 0.678;<0.001 0.194;<0.001 0.050; 0.091 0.095; 0.003

wet1_2500_if_cnt 0.027; 0.420 0.386;<0.001 0.018; 0.547 0.123;<0.001

wet1_5000_if_cnt -0.041; 0.209 -0.100;<0.001 0.065; 0.022 0.104;<0.001

wet2_500_if_cnt 0.088; 0.007 0.041; 0.165 -0.008; 0.786 0.749;<0.001

wet2_1000_if_cnt 0.077; 0.013 0.026; 0.365 -0.021; 0.428 0.315;<0.001

wet2_2500_if_cnt 0.012; 0.694 0.051; 0.055 -0.059; 0.019 0.224;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 150 / 189

Table G.2 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_1000kd wet1_2500kd wet1_5000kd wet2_500kd

wet2_5000_if_cnt -0.043; 0.145 0.135;<0.001 -0.009; 0.720 0.058; 0.031

wet3_500_if_cnt 0.237;<0.001 0.205;<0.001 0.147;<0.001 0.355;<0.001

wet3_1000_if_cnt 0.262;<0.001 0.183;<0.001 0.151;<0.001 0.120;<0.001

wet3_2500_if_cnt 0.018; 0.559 0.162;<0.001 0.092;<0.001 0.032; 0.258

wet3_5000_if_cnt -0.109;<0.001 -0.101;<0.001 -0.112;<0.001 -0.068; 0.013

wet4_500_if_cnt 0.250;<0.001 0.111;<0.001 0.093; 0.002 0.195;<0.001

wet4_1000_if_cnt 0.372;<0.001 0.161;<0.001 0.065; 0.027 0.093; 0.004

wet4_2500_if_cnt 0.156;<0.001 0.221;<0.001 0.021; 0.455 0.112;<0.001

wet4_5000_if_cnt -0.013; 0.670 -0.005; 0.853 -0.012; 0.667 -0.013; 0.651

wet1_all_if_xd -0.181;<0.001 -0.010; 0.716 0.322;<0.001 -0.037; 0.186

wet2_all_if_xd -0.205;<0.001 -0.147;<0.001 -0.033; 0.205 -0.242;<0.001

wet3_all_if_xd -0.204;<0.001 -0.205;<0.001 -0.078; 0.003 -0.135;<0.001

wet4_all_if_xd -0.183;<0.001 -0.068; 0.014 0.144;<0.001 -0.027; 0.331

wet1_if_density 0.157;<0.001 0.226;<0.001 0.217;<0.001 0.159;<0.001

wet2_if_density 0.059; 0.056 0.090; 0.001 0.012; 0.655 0.196;<0.001

wet3_if_density -0.005; 0.881 -0.036; 0.204 0.016; 0.558 -0.061; 0.033

wet4_if_density 0.096; 0.002 0.018; 0.527 -0.036; 0.172 0.042; 0.142

Table G.3: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_1000kd wet2_2500kd wet2_5000kd wet3_500kd

cws_shrub -0.009; 0.717 0.030; 0.230 0.010; 0.678 -0.061; 0.032

mrps -0.167;<0.001 -0.118;<0.001 0.041; 0.096 -0.057; 0.045

topo -0.081; 0.001 -0.130;<0.001 -0.247;<0.001 0.146;<0.001

wet1_500kd 0.103;<0.001 0.026; 0.382 -0.116;<0.001 0.273;<0.001

wet1_1000kd 0.161;<0.001 0.007; 0.806 -0.181;<0.001 0.269;<0.001

wet1_2500kd 0.051; 0.057 0.087;<0.001 -0.086;<0.001 0.270;<0.001

wet1_5000kd 0.042; 0.101 0.034; 0.167 -0.045; 0.066 0.227;<0.001

wet2_500kd 0.546;<0.001 0.341;<0.001 0.194;<0.001 0.376;<0.001

wet2_1000kd 1.000;<0.001 0.490;<0.001 0.319;<0.001 0.185;<0.001

wet2_2500kd 0.490;<0.001 1.000;<0.001 0.562;<0.001 0.041; 0.137

wet2_5000kd 0.319;<0.001 0.562;<0.001 1.000;<0.001 -0.099;<0.001

wet3_500kd 0.185;<0.001 0.041; 0.137 -0.099;<0.001 1.000;<0.001

wet3_1000kd 0.274;<0.001 0.091;<0.001 -0.074; 0.004 0.604;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 151 / 189

Table G.3 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_1000kd wet2_2500kd wet2_5000kd wet3_500kd

wet3_2500kd 0.077; 0.002 0.144;<0.001 0.032; 0.190 0.325;<0.001

wet3_5000kd -0.038; 0.130 0.033; 0.167 0.097;<0.001 0.224;<0.001

wet4_500kd 0.100;<0.001 0.024; 0.407 -0.127;<0.001 0.312;<0.001

wet4_1000kd 0.143;<0.001 0.023; 0.419 -0.194;<0.001 0.338;<0.001

wet4_2500kd 0.001; 0.957 -0.039; 0.127 -0.261;<0.001 0.328;<0.001

wet4_5000kd -0.078; 0.002 -0.142;<0.001 -0.299;<0.001 0.243;<0.001

wet1_500_cnt 0.113;<0.001 0.029; 0.326 -0.121;<0.001 0.291;<0.001

wet1_1000_cnt 0.141;<0.001 -0.000; 0.995 -0.187;<0.001 0.209;<0.001

wet1_2500_cnt -0.018; 0.517 0.013; 0.636 -0.164;<0.001 0.264;<0.001

wet1_5000_cnt -0.007; 0.778 -0.076; 0.002 -0.193;<0.001 0.237;<0.001

wet2_500_cnt 0.479;<0.001 0.272;<0.001 0.112;<0.001 0.477;<0.001

wet2_1000_cnt 0.618;<0.001 0.309;<0.001 0.118;<0.001 0.258;<0.001

wet2_2500_cnt 0.285;<0.001 0.435;<0.001 0.240;<0.001 0.246;<0.001

wet2_5000_cnt 0.120;<0.001 0.205;<0.001 0.271;<0.001 0.187;<0.001

wet3_500_cnt 0.202;<0.001 0.039; 0.161 -0.104;<0.001 0.923;<0.001

wet3_1000_cnt 0.275;<0.001 0.093;<0.001 -0.057; 0.032 0.436;<0.001

wet3_2500_cnt 0.070; 0.005 0.162;<0.001 0.038; 0.119 0.274;<0.001

wet3_5000_cnt 0.006; 0.817 0.029; 0.229 0.064; 0.008 0.169;<0.001

wet4_500_cnt 0.109;<0.001 0.023; 0.438 -0.126;<0.001 0.321;<0.001

wet4_1000_cnt 0.147;<0.001 0.030; 0.300 -0.188;<0.001 0.259;<0.001

wet4_2500_cnt -0.032; 0.234 -0.048; 0.065 -0.264;<0.001 0.322;<0.001

wet4_5000_cnt -0.155;<0.001 -0.236;<0.001 -0.336;<0.001 0.192;<0.001

wet1_all_cnt -0.054; 0.032 -0.131;<0.001 -0.231;<0.001 0.288;<0.001

wet2_all_cnt 0.063; 0.013 0.120;<0.001 0.212;<0.001 0.156;<0.001

wet3_all_cnt -0.011; 0.668 0.037; 0.128 0.109;<0.001 0.151;<0.001

wet4_all_cnt -0.312;<0.001 -0.418;<0.001 -0.588;<0.001 0.138;<0.001

field_size 0.093;<0.001 0.198;<0.001 0.416;<0.001 -0.176;<0.001

tro_all_cnt 0.158;<0.001 0.239;<0.001 0.286;<0.001 -0.277;<0.001

dug_all_cnt -0.117;<0.001 -0.013; 0.616 0.201;<0.001 -0.184;<0.001

wet1_all_xd -0.006; 0.799 -0.048; 0.049 -0.039; 0.104 -0.082; 0.003

wet2_all_xd -0.229;<0.001 -0.283;<0.001 -0.204;<0.001 -0.237;<0.001

wet3_all_xd -0.048; 0.053 -0.039; 0.108 0.042; 0.079 -0.237;<0.001

wet4_all_xd 0.057; 0.022 0.145;<0.001 0.257;<0.001 -0.214;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 152 / 189

Table G.3 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_1000kd wet2_2500kd wet2_5000kd wet3_500kd

tro_all_xd 0.081; 0.004 0.194;<0.001 0.305;<0.001 0.112;<0.001

dug_all_xd 0.036; 0.185 0.103;<0.001 0.151;<0.001 -0.122;<0.001

wet1_500_if_cnt 0.113;<0.001 0.017; 0.567 -0.087; 0.003 0.258;<0.001

wet1_1000_if_cnt 0.150;<0.001 0.010; 0.742 -0.106;<0.001 0.166;<0.001

wet1_2500_if_cnt 0.113;<0.001 0.068; 0.017 -0.059; 0.038 0.166;<0.001

wet1_5000_if_cnt 0.178;<0.001 0.136;<0.001 0.097;<0.001 0.044; 0.168

wet2_500_if_cnt 0.462;<0.001 0.297;<0.001 0.183;<0.001 0.348;<0.001

wet2_1000_if_cnt 0.520;<0.001 0.293;<0.001 0.190;<0.001 0.165;<0.001

wet2_2500_if_cnt 0.270;<0.001 0.273;<0.001 0.260;<0.001 0.077; 0.006

wet2_5000_if_cnt 0.133;<0.001 0.177;<0.001 0.266;<0.001 0.039; 0.168

wet3_500_if_cnt 0.208;<0.001 0.036; 0.199 -0.072; 0.011 0.771;<0.001

wet3_1000_if_cnt 0.204;<0.001 0.015; 0.584 -0.081; 0.003 0.359;<0.001

wet3_2500_if_cnt 0.053; 0.043 0.060; 0.020 0.035; 0.173 0.190;<0.001

wet3_5000_if_cnt -0.070; 0.006 -0.077; 0.002 0.011; 0.657 0.061; 0.031

wet4_500_if_cnt 0.107;<0.001 0.015; 0.615 -0.086; 0.003 0.294;<0.001

wet4_1000_if_cnt 0.131;<0.001 0.028; 0.340 -0.134;<0.001 0.239;<0.001

wet4_2500_if_cnt 0.094; 0.001 -0.027; 0.330 -0.159;<0.001 0.279;<0.001

wet4_5000_if_cnt 0.025; 0.363 0.028; 0.301 0.001; 0.969 0.068; 0.026

wet1_all_if_xd -0.025; 0.338 0.054; 0.034 0.080; 0.002 0.040; 0.172

wet2_all_if_xd -0.239;<0.001 -0.149;<0.001 -0.083; 0.001 -0.118;<0.001

wet3_all_if_xd -0.165;<0.001 -0.138;<0.001 -0.124;<0.001 -0.172;<0.001

wet4_all_if_xd -0.012; 0.638 0.054; 0.034 0.083; 0.001 -0.036; 0.226

wet1_if_density 0.203;<0.001 0.127;<0.001 -0.072; 0.006 0.203;<0.001

wet2_if_density 0.272;<0.001 0.235;<0.001 0.119;<0.001 0.186;<0.001

wet3_if_density -0.093;<0.001 -0.143;<0.001 -0.179;<0.001 0.232;<0.001

wet4_if_density 0.042; 0.120 -0.023; 0.383 -0.163;<0.001 0.172;<0.001

Table G.4: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_1000kd wet3_2500kd wet3_5000kd wet4_500kd

cws_shrub -0.106;<0.001 -0.121;<0.001 -0.023; 0.344 0.080; 0.007

mrps -0.004; 0.891 0.058; 0.018 0.128;<0.001 -0.161;<0.001

topo 0.126;<0.001 0.044; 0.073 0.059; 0.015 0.135;<0.001

wet1_500kd 0.209;<0.001 0.120;<0.001 0.022; 0.451 0.554;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 153 / 189

Table G.4 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_1000kd wet3_2500kd wet3_5000kd wet4_500kd

wet1_1000kd 0.319;<0.001 0.154;<0.001 -0.007; 0.815 0.354;<0.001

wet1_2500kd 0.285;<0.001 0.343;<0.001 0.136;<0.001 0.181;<0.001

wet1_5000kd 0.299;<0.001 0.413;<0.001 0.387;<0.001 0.155;<0.001

wet2_500kd 0.200;<0.001 0.049; 0.067 -0.027; 0.312 0.199;<0.001

wet2_1000kd 0.274;<0.001 0.077; 0.002 -0.038; 0.130 0.100;<0.001

wet2_2500kd 0.091;<0.001 0.144;<0.001 0.033; 0.167 0.024; 0.407

wet2_5000kd -0.074; 0.004 0.032; 0.190 0.097;<0.001 -0.127;<0.001

wet3_500kd 0.604;<0.001 0.325;<0.001 0.224;<0.001 0.312;<0.001

wet3_1000kd 1.000;<0.001 0.489;<0.001 0.319;<0.001 0.244;<0.001

wet3_2500kd 0.489;<0.001 1.000;<0.001 0.575;<0.001 0.174;<0.001

wet3_5000kd 0.319;<0.001 0.575;<0.001 1.000;<0.001 0.068; 0.019

wet4_500kd 0.244;<0.001 0.174;<0.001 0.068; 0.019 1.000;<0.001

wet4_1000kd 0.400;<0.001 0.271;<0.001 0.092; 0.001 0.566;<0.001

wet4_2500kd 0.384;<0.001 0.466;<0.001 0.227;<0.001 0.290;<0.001

wet4_5000kd 0.287;<0.001 0.361;<0.001 0.437;<0.001 0.185;<0.001

wet1_500_cnt 0.227;<0.001 0.135;<0.001 0.031; 0.282 0.554;<0.001

wet1_1000_cnt 0.291;<0.001 0.155;<0.001 0.000; 0.988 0.277;<0.001

wet1_2500_cnt 0.257;<0.001 0.355;<0.001 0.179;<0.001 0.173;<0.001

wet1_5000_cnt 0.302;<0.001 0.366;<0.001 0.385;<0.001 0.166;<0.001

wet2_500_cnt 0.293;<0.001 0.122;<0.001 0.038; 0.156 0.274;<0.001

wet2_1000_cnt 0.427;<0.001 0.231;<0.001 0.087;<0.001 0.164;<0.001

wet2_2500_cnt 0.349;<0.001 0.550;<0.001 0.371;<0.001 0.128;<0.001

wet2_5000_cnt 0.284;<0.001 0.441;<0.001 0.570;<0.001 0.015; 0.608

wet3_500_cnt 0.604;<0.001 0.337;<0.001 0.236;<0.001 0.323;<0.001

wet3_1000_cnt 0.821;<0.001 0.496;<0.001 0.359;<0.001 0.168;<0.001

wet3_2500_cnt 0.412;<0.001 0.750;<0.001 0.597;<0.001 0.135;<0.001

wet3_5000_cnt 0.242;<0.001 0.426;<0.001 0.695;<0.001 0.016; 0.590

wet4_500_cnt 0.247;<0.001 0.164;<0.001 0.054; 0.062 0.954;<0.001

wet4_1000_cnt 0.377;<0.001 0.266;<0.001 0.096;<0.001 0.284;<0.001

wet4_2500_cnt 0.341;<0.001 0.468;<0.001 0.239;<0.001 0.245;<0.001

wet4_5000_cnt 0.202;<0.001 0.263;<0.001 0.393;<0.001 0.129;<0.001

wet1_all_cnt 0.388;<0.001 0.481;<0.001 0.413;<0.001 0.148;<0.001

wet2_all_cnt 0.268;<0.001 0.431;<0.001 0.470;<0.001 -0.051; 0.083
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 154 / 189

Table G.4 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_1000kd wet3_2500kd wet3_5000kd wet4_500kd

wet3_all_cnt 0.233;<0.001 0.361;<0.001 0.437;<0.001 -0.053; 0.068

wet4_all_cnt 0.143;<0.001 0.107;<0.001 0.125;<0.001 0.126;<0.001

field_size -0.151;<0.001 -0.116;<0.001 -0.134;<0.001 -0.165;<0.001

tro_all_cnt -0.346;<0.001 -0.443;<0.001 -0.463;<0.001 -0.126;<0.001

dug_all_cnt -0.134;<0.001 -0.061; 0.021 -0.045; 0.085 -0.195;<0.001

wet1_all_xd -0.096;<0.001 -0.161;<0.001 -0.178;<0.001 -0.099;<0.001

wet2_all_xd -0.300;<0.001 -0.368;<0.001 -0.399;<0.001 -0.115;<0.001

wet3_all_xd -0.332;<0.001 -0.457;<0.001 -0.584;<0.001 -0.125;<0.001

wet4_all_xd -0.212;<0.001 -0.294;<0.001 -0.370;<0.001 -0.188;<0.001

tro_all_xd 0.237;<0.001 0.432;<0.001 0.433;<0.001 -0.015; 0.637

dug_all_xd -0.092; 0.002 -0.019; 0.486 -0.084; 0.002 -0.088; 0.006

wet1_500_if_cnt 0.204;<0.001 0.084; 0.005 0.001; 0.981 0.389;<0.001

wet1_1000_if_cnt 0.239;<0.001 0.067; 0.022 -0.047; 0.104 0.154;<0.001

wet1_2500_if_cnt 0.109;<0.001 0.058; 0.042 -0.096;<0.001 0.111; 0.001

wet1_5000_if_cnt 0.065; 0.030 0.034; 0.219 0.020; 0.473 0.076; 0.023

wet2_500_if_cnt 0.210;<0.001 0.065; 0.018 0.003; 0.909 0.186;<0.001

wet2_1000_if_cnt 0.241;<0.001 0.087;<0.001 -0.007; 0.789 0.081; 0.010

wet2_2500_if_cnt 0.093;<0.001 0.092;<0.001 0.007; 0.787 0.017; 0.558

wet2_5000_if_cnt 0.047; 0.076 0.066; 0.008 0.027; 0.278 -0.006; 0.852

wet3_500_if_cnt 0.517;<0.001 0.279;<0.001 0.195;<0.001 0.266;<0.001

wet3_1000_if_cnt 0.597;<0.001 0.345;<0.001 0.244;<0.001 0.145;<0.001

wet3_2500_if_cnt 0.252;<0.001 0.370;<0.001 0.224;<0.001 0.068; 0.028

wet3_5000_if_cnt 0.083; 0.002 0.140;<0.001 0.220;<0.001 -0.020; 0.499

wet4_500_if_cnt 0.215;<0.001 0.117;<0.001 0.025; 0.397 0.783;<0.001

wet4_1000_if_cnt 0.312;<0.001 0.190;<0.001 0.051; 0.076 0.230;<0.001

wet4_2500_if_cnt 0.257;<0.001 0.174;<0.001 -0.003; 0.912 0.193;<0.001

wet4_5000_if_cnt 0.068; 0.017 0.090;<0.001 0.162;<0.001 -0.010; 0.749

wet1_all_if_xd 0.049; 0.077 0.153;<0.001 0.255;<0.001 0.023; 0.453

wet2_all_if_xd -0.102;<0.001 -0.059; 0.022 -0.019; 0.451 -0.029; 0.344

wet3_all_if_xd -0.187;<0.001 -0.131;<0.001 0.013; 0.612 -0.062; 0.044

wet4_all_if_xd 0.001; 0.981 0.117;<0.001 0.288;<0.001 -0.069; 0.024

wet1_if_density 0.188;<0.001 0.076; 0.004 -0.071; 0.007 0.175;<0.001

wet2_if_density 0.169;<0.001 0.056; 0.031 -0.055; 0.034 0.091; 0.004
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 155 / 189

Table G.4 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_1000kd wet3_2500kd wet3_5000kd wet4_500kd

wet3_if_density 0.272;<0.001 0.332;<0.001 0.390;<0.001 0.068; 0.030

wet4_if_density 0.165;<0.001 0.147;<0.001 0.177;<0.001 0.128;<0.001

Table G.5: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_1000kd wet4_2500kd wet4_5000kd wet1_500_cnt

cws_shrub 0.091; 0.001 -0.022; 0.390 0.072; 0.004 0.063; 0.035

mrps -0.206;<0.001 -0.126;<0.001 -0.069; 0.005 -0.104;<0.001

topo 0.171;<0.001 0.157;<0.001 0.260;<0.001 0.140;<0.001

wet1_500kd 0.320;<0.001 0.210;<0.001 0.151;<0.001 0.955;<0.001

wet1_1000kd 0.551;<0.001 0.289;<0.001 0.190;<0.001 0.565;<0.001

wet1_2500kd 0.263;<0.001 0.466;<0.001 0.244;<0.001 0.268;<0.001

wet1_5000kd 0.197;<0.001 0.365;<0.001 0.474;<0.001 0.187;<0.001

wet2_500kd 0.132;<0.001 0.049; 0.080 -0.036; 0.182 0.200;<0.001

wet2_1000kd 0.143;<0.001 0.001; 0.957 -0.078; 0.002 0.113;<0.001

wet2_2500kd 0.023; 0.419 -0.039; 0.127 -0.142;<0.001 0.029; 0.326

wet2_5000kd -0.194;<0.001 -0.261;<0.001 -0.299;<0.001 -0.121;<0.001

wet3_500kd 0.338;<0.001 0.328;<0.001 0.243;<0.001 0.291;<0.001

wet3_1000kd 0.400;<0.001 0.384;<0.001 0.287;<0.001 0.227;<0.001

wet3_2500kd 0.271;<0.001 0.466;<0.001 0.361;<0.001 0.135;<0.001

wet3_5000kd 0.092; 0.001 0.227;<0.001 0.437;<0.001 0.031; 0.282

wet4_500kd 0.566;<0.001 0.290;<0.001 0.185;<0.001 0.554;<0.001

wet4_1000kd 1.000;<0.001 0.447;<0.001 0.246;<0.001 0.348;<0.001

wet4_2500kd 0.447;<0.001 1.000;<0.001 0.417;<0.001 0.225;<0.001

wet4_5000kd 0.246;<0.001 0.417;<0.001 1.000;<0.001 0.166;<0.001

wet1_500_cnt 0.348;<0.001 0.225;<0.001 0.166;<0.001 1.000;<0.001

wet1_1000_cnt 0.545;<0.001 0.278;<0.001 0.198;<0.001 0.322;<0.001

wet1_2500_cnt 0.223;<0.001 0.508;<0.001 0.334;<0.001 0.206;<0.001

wet1_5000_cnt 0.220;<0.001 0.367;<0.001 0.617;<0.001 0.169;<0.001

wet2_500_cnt 0.230;<0.001 0.140;<0.001 0.045; 0.098 0.224;<0.001

wet2_1000_cnt 0.326;<0.001 0.169;<0.001 0.092;<0.001 0.092; 0.003

wet2_2500_cnt 0.188;<0.001 0.346;<0.001 0.254;<0.001 0.083; 0.005

wet2_5000_cnt 0.012; 0.683 0.131;<0.001 0.277;<0.001 -0.017; 0.558

wet3_500_cnt 0.343;<0.001 0.343;<0.001 0.257;<0.001 0.299;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 156 / 189

Table G.5 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_1000kd wet4_2500kd wet4_5000kd wet1_500_cnt

wet3_1000_cnt 0.364;<0.001 0.376;<0.001 0.312;<0.001 0.141;<0.001

wet3_2500_cnt 0.206;<0.001 0.436;<0.001 0.419;<0.001 0.066; 0.026

wet3_5000_cnt 0.018; 0.521 0.153;<0.001 0.444;<0.001 -0.044; 0.132

wet4_500_cnt 0.573;<0.001 0.291;<0.001 0.177;<0.001 0.569;<0.001

wet4_1000_cnt 0.842;<0.001 0.400;<0.001 0.245;<0.001 0.223;<0.001

wet4_2500_cnt 0.344;<0.001 0.846;<0.001 0.408;<0.001 0.221;<0.001

wet4_5000_cnt 0.167;<0.001 0.285;<0.001 0.761;<0.001 0.157;<0.001

wet1_all_cnt 0.223;<0.001 0.393;<0.001 0.539;<0.001 0.142;<0.001

wet2_all_cnt -0.045; 0.112 0.030; 0.245 0.103;<0.001 -0.105;<0.001

wet3_all_cnt -0.053; 0.059 0.026; 0.301 0.178;<0.001 -0.122;<0.001

wet4_all_cnt 0.208;<0.001 0.327;<0.001 0.422;<0.001 0.124;<0.001

field_size -0.241;<0.001 -0.375;<0.001 -0.552;<0.001 -0.178;<0.001

tro_all_cnt -0.200;<0.001 -0.332;<0.001 -0.447;<0.001 -0.126;<0.001

dug_all_cnt -0.258;<0.001 -0.319;<0.001 -0.394;<0.001 -0.128;<0.001

wet1_all_xd -0.100;<0.001 -0.156;<0.001 -0.230;<0.001 -0.179;<0.001

wet2_all_xd -0.151;<0.001 -0.235;<0.001 -0.351;<0.001 -0.133;<0.001

wet3_all_xd -0.161;<0.001 -0.290;<0.001 -0.506;<0.001 -0.082; 0.005

wet4_all_xd -0.237;<0.001 -0.379;<0.001 -0.567;<0.001 -0.174;<0.001

tro_all_xd -0.022; 0.503 0.044; 0.131 0.022; 0.421 -0.035; 0.291

dug_all_xd -0.155;<0.001 -0.106;<0.001 -0.218;<0.001 -0.130;<0.001

wet1_500_if_cnt 0.249;<0.001 0.152;<0.001 0.094; 0.001 0.779;<0.001

wet1_1000_if_cnt 0.361;<0.001 0.142;<0.001 0.059; 0.047 0.168;<0.001

wet1_2500_if_cnt 0.121;<0.001 0.119;<0.001 0.004; 0.884 0.067; 0.052

wet1_5000_if_cnt 0.063; 0.051 0.049; 0.093 0.003; 0.911 -0.055; 0.102

wet2_500_if_cnt 0.142;<0.001 0.036; 0.214 -0.051; 0.066 0.147;<0.001

wet2_1000_if_cnt 0.196;<0.001 0.006; 0.822 -0.077; 0.003 0.013; 0.675

wet2_2500_if_cnt 0.059; 0.041 0.000; 0.985 -0.163;<0.001 -0.025; 0.398

wet2_5000_if_cnt -0.030; 0.301 -0.068; 0.009 -0.191;<0.001 -0.081; 0.006

wet3_500_if_cnt 0.285;<0.001 0.257;<0.001 0.189;<0.001 0.268;<0.001

wet3_1000_if_cnt 0.322;<0.001 0.244;<0.001 0.206;<0.001 0.134;<0.001

wet3_2500_if_cnt 0.140;<0.001 0.154;<0.001 0.053; 0.041 0.004; 0.909

wet3_5000_if_cnt -0.037; 0.202 -0.045; 0.086 0.097;<0.001 -0.092; 0.002

wet4_500_if_cnt 0.479;<0.001 0.220;<0.001 0.109;<0.001 0.380;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 157 / 189

Table G.5 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_1000kd wet4_2500kd wet4_5000kd wet1_500_cnt

wet4_1000_if_cnt 0.663;<0.001 0.287;<0.001 0.143;<0.001 0.181;<0.001

wet4_2500_if_cnt 0.284;<0.001 0.407;<0.001 0.077; 0.006 0.116;<0.001

wet4_5000_if_cnt -0.060; 0.055 -0.083; 0.003 0.186;<0.001 -0.039; 0.229

wet1_all_if_xd -0.002; 0.953 0.175;<0.001 0.263;<0.001 -0.103;<0.001

wet2_all_if_xd -0.131;<0.001 -0.047; 0.082 -0.047; 0.067 -0.136;<0.001

wet3_all_if_xd -0.177;<0.001 -0.075; 0.006 0.123;<0.001 -0.142;<0.001

wet4_all_if_xd -0.152;<0.001 -0.016; 0.555 0.311;<0.001 -0.129;<0.001

wet1_if_density 0.226;<0.001 0.293;<0.001 0.170;<0.001 0.125;<0.001

wet2_if_density 0.146;<0.001 0.110;<0.001 -0.041; 0.114 0.048; 0.123

wet3_if_density 0.106;<0.001 0.168;<0.001 0.353;<0.001 0.033; 0.288

wet4_if_density 0.166;<0.001 0.211;<0.001 0.356;<0.001 0.088; 0.005

Table G.6: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_1000_cnt wet1_2500_cnt wet1_5000_cnt wet2_500_cnt

cws_shrub 0.042; 0.153 -0.125;<0.001 0.022; 0.376 -0.019; 0.500

mrps -0.133;<0.001 -0.084; 0.002 0.005; 0.843 -0.147;<0.001

topo 0.184;<0.001 0.133;<0.001 0.133;<0.001 -0.025; 0.350

wet1_500kd 0.301;<0.001 0.186;<0.001 0.152;<0.001 0.207;<0.001

wet1_1000kd 0.896;<0.001 0.199;<0.001 0.184;<0.001 0.163;<0.001

wet1_2500kd 0.340;<0.001 0.829;<0.001 0.271;<0.001 0.107;<0.001

wet1_5000kd 0.215;<0.001 0.499;<0.001 0.763;<0.001 0.097;<0.001

wet2_500kd 0.081; 0.010 0.021; 0.484 0.004; 0.884 0.830;<0.001

wet2_1000kd 0.141;<0.001 -0.018; 0.517 -0.007; 0.778 0.479;<0.001

wet2_2500kd -0.000; 0.995 0.013; 0.636 -0.076; 0.002 0.272;<0.001

wet2_5000kd -0.187;<0.001 -0.164;<0.001 -0.193;<0.001 0.112;<0.001

wet3_500kd 0.209;<0.001 0.264;<0.001 0.237;<0.001 0.477;<0.001

wet3_1000kd 0.291;<0.001 0.257;<0.001 0.302;<0.001 0.293;<0.001

wet3_2500kd 0.155;<0.001 0.355;<0.001 0.366;<0.001 0.122;<0.001

wet3_5000kd 0.000; 0.988 0.179;<0.001 0.385;<0.001 0.038; 0.156

wet4_500kd 0.277;<0.001 0.173;<0.001 0.166;<0.001 0.274;<0.001

wet4_1000kd 0.545;<0.001 0.223;<0.001 0.220;<0.001 0.230;<0.001

wet4_2500kd 0.278;<0.001 0.508;<0.001 0.367;<0.001 0.140;<0.001

wet4_5000kd 0.198;<0.001 0.334;<0.001 0.617;<0.001 0.045; 0.098
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 158 / 189

Table G.6 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_1000_cnt wet1_2500_cnt wet1_5000_cnt wet2_500_cnt

wet1_500_cnt 0.322;<0.001 0.206;<0.001 0.169;<0.001 0.224;<0.001

wet1_1000_cnt 1.000;<0.001 0.208;<0.001 0.192;<0.001 0.111;<0.001

wet1_2500_cnt 0.208;<0.001 1.000;<0.001 0.368;<0.001 0.092; 0.002

wet1_5000_cnt 0.192;<0.001 0.368;<0.001 1.000;<0.001 0.075; 0.007

wet2_500_cnt 0.111;<0.001 0.092; 0.002 0.075; 0.007 1.000;<0.001

wet2_1000_cnt 0.205;<0.001 0.100;<0.001 0.127;<0.001 0.414;<0.001

wet2_2500_cnt 0.087; 0.003 0.297;<0.001 0.291;<0.001 0.240;<0.001

wet2_5000_cnt -0.070; 0.016 0.133;<0.001 0.349;<0.001 0.062; 0.022

wet3_500_cnt 0.198;<0.001 0.271;<0.001 0.252;<0.001 0.493;<0.001

wet3_1000_cnt 0.269;<0.001 0.235;<0.001 0.314;<0.001 0.210;<0.001

wet3_2500_cnt 0.055; 0.061 0.327;<0.001 0.430;<0.001 0.109;<0.001

wet3_5000_cnt -0.098;<0.001 0.051; 0.059 0.338;<0.001 0.045; 0.099

wet4_500_cnt 0.285;<0.001 0.172;<0.001 0.169;<0.001 0.288;<0.001

wet4_1000_cnt 0.580;<0.001 0.227;<0.001 0.212;<0.001 0.155;<0.001

wet4_2500_cnt 0.240;<0.001 0.572;<0.001 0.388;<0.001 0.117;<0.001

wet4_5000_cnt 0.195;<0.001 0.311;<0.001 0.624;<0.001 -0.001; 0.971

wet1_all_cnt 0.179;<0.001 0.380;<0.001 0.578;<0.001 0.047; 0.081

wet2_all_cnt -0.169;<0.001 0.028; 0.309 0.165;<0.001 0.044; 0.112

wet3_all_cnt -0.199;<0.001 -0.022; 0.402 0.160;<0.001 -0.007; 0.783

wet4_all_cnt 0.154;<0.001 0.205;<0.001 0.347;<0.001 -0.149;<0.001

field_size -0.226;<0.001 -0.242;<0.001 -0.397;<0.001 -0.027; 0.348

tro_all_cnt -0.177;<0.001 -0.240;<0.001 -0.364;<0.001 0.013; 0.673

dug_all_cnt -0.197;<0.001 -0.152;<0.001 -0.209;<0.001 -0.164;<0.001

wet1_all_xd -0.173;<0.001 -0.303;<0.001 -0.427;<0.001 -0.006; 0.814

wet2_all_xd -0.128;<0.001 -0.276;<0.001 -0.405;<0.001 -0.182;<0.001

wet3_all_xd -0.074; 0.010 -0.198;<0.001 -0.425;<0.001 -0.097;<0.001

wet4_all_xd -0.185;<0.001 -0.325;<0.001 -0.528;<0.001 -0.079; 0.003

tro_all_xd -0.092; 0.005 0.039; 0.206 0.245;<0.001 0.023; 0.459

dug_all_xd -0.163;<0.001 -0.110;<0.001 -0.092;<0.001 -0.059; 0.051

wet1_500_if_cnt 0.219;<0.001 0.095; 0.003 0.091; 0.002 0.191;<0.001

wet1_1000_if_cnt 0.758;<0.001 0.030; 0.352 0.038; 0.205 0.103; 0.002

wet1_2500_if_cnt 0.051; 0.140 0.449;<0.001 -0.054; 0.065 0.172;<0.001

wet1_5000_if_cnt -0.035; 0.286 -0.099; 0.001 0.092; 0.001 0.121;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 159 / 189

Table G.6 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_1000_cnt wet1_2500_cnt wet1_5000_cnt wet2_500_cnt

wet2_500_if_cnt 0.050; 0.127 0.006; 0.837 -0.039; 0.167 0.866;<0.001

wet2_1000_if_cnt 0.097; 0.002 -0.011; 0.708 -0.057; 0.034 0.356;<0.001

wet2_2500_if_cnt -0.006; 0.829 -0.003; 0.900 -0.149;<0.001 0.241;<0.001

wet2_5000_if_cnt -0.053; 0.074 0.060; 0.028 -0.111;<0.001 0.056; 0.043

wet3_500_if_cnt 0.163;<0.001 0.191;<0.001 0.169;<0.001 0.441;<0.001

wet3_1000_if_cnt 0.236;<0.001 0.136;<0.001 0.171;<0.001 0.194;<0.001

wet3_2500_if_cnt 0.012; 0.696 0.165;<0.001 0.066; 0.012 0.083; 0.004

wet3_5000_if_cnt -0.108;<0.001 -0.087; 0.002 -0.042; 0.101 -0.039; 0.166

wet4_500_if_cnt 0.207;<0.001 0.092; 0.005 0.106;<0.001 0.271;<0.001

wet4_1000_if_cnt 0.413;<0.001 0.113;<0.001 0.097; 0.001 0.151;<0.001

wet4_2500_if_cnt 0.141;<0.001 0.222;<0.001 0.012; 0.671 0.177;<0.001

wet4_5000_if_cnt -0.017; 0.594 -0.019; 0.530 0.045; 0.098 -0.004; 0.884

wet1_all_if_xd -0.164;<0.001 0.049; 0.087 0.374;<0.001 -0.015; 0.593

wet2_all_if_xd -0.183;<0.001 -0.093; 0.001 0.018; 0.483 -0.265;<0.001

wet3_all_if_xd -0.174;<0.001 -0.135;<0.001 0.008; 0.749 -0.140;<0.001

wet4_all_if_xd -0.166;<0.001 -0.027; 0.347 0.231;<0.001 -0.032; 0.260

wet1_if_density 0.157;<0.001 0.213;<0.001 0.225;<0.001 0.189;<0.001

wet2_if_density 0.058; 0.063 0.044; 0.129 -0.011; 0.676 0.208;<0.001

wet3_if_density 0.003; 0.921 0.008; 0.770 0.147;<0.001 -0.003; 0.908

wet4_if_density 0.105;<0.001 0.025; 0.393 0.089;<0.001 0.076; 0.009

Table G.7: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_1000_cnt wet2_2500_cnt wet2_5000_cnt wet3_500_cnt

cws_shrub -0.038; 0.150 -0.120;<0.001 -0.020; 0.409 -0.067; 0.019

mrps -0.175;<0.001 -0.113;<0.001 0.065; 0.008 -0.071; 0.013

topo -0.012; 0.631 -0.005; 0.828 -0.066; 0.006 0.155;<0.001

wet1_500kd 0.072; 0.020 0.075; 0.011 -0.027; 0.357 0.277;<0.001

wet1_1000kd 0.183;<0.001 0.098;<0.001 -0.060; 0.037 0.267;<0.001

wet1_2500kd 0.116;<0.001 0.300;<0.001 0.128;<0.001 0.271;<0.001

wet1_5000kd 0.141;<0.001 0.373;<0.001 0.352;<0.001 0.237;<0.001

wet2_500kd 0.352;<0.001 0.214;<0.001 0.039; 0.139 0.390;<0.001

wet2_1000kd 0.618;<0.001 0.285;<0.001 0.120;<0.001 0.202;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 160 / 189

wet2_2500kd 0.309;<0.001 0.435;<0.001 0.205;<0.001 0.039; 0.161

Table G.7 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_1000_cnt wet2_2500_cnt wet2_5000_cnt wet3_500_cnt

wet2_5000kd 0.118;<0.001 0.240;<0.001 0.271;<0.001 -0.104;<0.001

wet3_500kd 0.258;<0.001 0.246;<0.001 0.187;<0.001 0.923;<0.001

wet3_1000kd 0.427;<0.001 0.349;<0.001 0.284;<0.001 0.604;<0.001

wet3_2500kd 0.231;<0.001 0.550;<0.001 0.441;<0.001 0.337;<0.001

wet3_5000kd 0.087;<0.001 0.371;<0.001 0.570;<0.001 0.236;<0.001

wet4_500kd 0.164;<0.001 0.128;<0.001 0.015; 0.608 0.323;<0.001

wet4_1000kd 0.326;<0.001 0.188;<0.001 0.012; 0.683 0.343;<0.001

wet4_2500kd 0.169;<0.001 0.346;<0.001 0.131;<0.001 0.343;<0.001

wet4_5000kd 0.092;<0.001 0.254;<0.001 0.277;<0.001 0.257;<0.001

wet1_500_cnt 0.092; 0.003 0.083; 0.005 -0.017; 0.558 0.299;<0.001

wet1_1000_cnt 0.205;<0.001 0.087; 0.003 -0.070; 0.016 0.198;<0.001

wet1_2500_cnt 0.100;<0.001 0.297;<0.001 0.133;<0.001 0.271;<0.001

wet1_5000_cnt 0.127;<0.001 0.291;<0.001 0.349;<0.001 0.252;<0.001

wet2_500_cnt 0.414;<0.001 0.240;<0.001 0.062; 0.022 0.493;<0.001

wet2_1000_cnt 1.000;<0.001 0.330;<0.001 0.149;<0.001 0.273;<0.001

wet2_2500_cnt 0.330;<0.001 1.000;<0.001 0.439;<0.001 0.270;<0.001

wet2_5000_cnt 0.149;<0.001 0.439;<0.001 1.000;<0.001 0.204;<0.001

wet3_500_cnt 0.273;<0.001 0.270;<0.001 0.204;<0.001 1.000;<0.001

wet3_1000_cnt 0.463;<0.001 0.387;<0.001 0.327;<0.001 0.454;<0.001

wet3_2500_cnt 0.216;<0.001 0.603;<0.001 0.548;<0.001 0.295;<0.001

wet3_5000_cnt 0.100;<0.001 0.340;<0.001 0.627;<0.001 0.192;<0.001

wet4_500_cnt 0.169;<0.001 0.116;<0.001 0.004; 0.885 0.330;<0.001

wet4_1000_cnt 0.330;<0.001 0.179;<0.001 0.011; 0.706 0.256;<0.001

wet4_2500_cnt 0.112;<0.001 0.341;<0.001 0.122;<0.001 0.336;<0.001

wet4_5000_cnt 0.006; 0.826 0.131;<0.001 0.197;<0.001 0.203;<0.001

wet1_all_cnt 0.125;<0.001 0.290;<0.001 0.377;<0.001 0.314;<0.001

wet2_all_cnt 0.126;<0.001 0.350;<0.001 0.572;<0.001 0.170;<0.001

wet3_all_cnt 0.071; 0.005 0.266;<0.001 0.518;<0.001 0.168;<0.001

wet4_all_cnt -0.137;<0.001 -0.126;<0.001 0.008; 0.728 0.144;<0.001

field_size -0.045; 0.103 -0.031; 0.240 -0.005; 0.847 -0.189;<0.001

tro_all_cnt -0.043; 0.140 -0.158;<0.001 -0.225;<0.001 -0.295;<0.001

dug_all_cnt -0.203;<0.001 -0.138;<0.001 -0.022; 0.406 -0.204;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 161 / 189

Predictors wet2_1000_cnt wet2_2500_cnt wet2_5000_cnt wet3_500_cnt

wet1_all_xd -0.010; 0.707 -0.152;<0.001 -0.160;<0.001 -0.080; 0.005
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 162 / 189

Table G.7 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_1000_cnt wet2_2500_cnt wet2_5000_cnt wet3_500_cnt

wet2_all_xd -0.268;<0.001 -0.488;<0.001 -0.520;<0.001 -0.250;<0.001

wet3_all_xd -0.168;<0.001 -0.379;<0.001 -0.465;<0.001 -0.255;<0.001

wet4_all_xd -0.087;<0.001 -0.186;<0.001 -0.162;<0.001 -0.224;<0.001

tro_all_xd 0.097;<0.001 0.337;<0.001 0.414;<0.001 0.108;<0.001

dug_all_xd -0.029; 0.311 0.083; 0.002 0.069; 0.009 -0.136;<0.001

wet1_500_if_cnt 0.090; 0.004 0.036; 0.227 -0.004; 0.883 0.265;<0.001

wet1_1000_if_cnt 0.177;<0.001 0.011; 0.710 -0.052; 0.076 0.155;<0.001

wet1_2500_if_cnt 0.184;<0.001 0.078; 0.007 -0.030; 0.303 0.176;<0.001

wet1_5000_if_cnt 0.202;<0.001 0.217;<0.001 0.176;<0.001 0.063; 0.051

wet2_500_if_cnt 0.386;<0.001 0.207;<0.001 0.051; 0.063 0.360;<0.001

wet2_1000_if_cnt 0.734;<0.001 0.211;<0.001 0.087;<0.001 0.174;<0.001

wet2_2500_if_cnt 0.276;<0.001 0.319;<0.001 0.118;<0.001 0.096;<0.001

wet2_5000_if_cnt 0.118;<0.001 0.160;<0.001 0.226;<0.001 0.052; 0.070

wet3_500_if_cnt 0.277;<0.001 0.223;<0.001 0.188;<0.001 0.822;<0.001

wet3_1000_if_cnt 0.363;<0.001 0.225;<0.001 0.224;<0.001 0.384;<0.001

wet3_2500_if_cnt 0.169;<0.001 0.265;<0.001 0.269;<0.001 0.214;<0.001

wet3_5000_if_cnt 0.003; 0.909 0.069; 0.006 0.231;<0.001 0.073; 0.011

wet4_500_if_cnt 0.173;<0.001 0.070; 0.017 0.016; 0.580 0.303;<0.001

wet4_1000_if_cnt 0.289;<0.001 0.110;<0.001 0.010; 0.738 0.233;<0.001

wet4_2500_if_cnt 0.201;<0.001 0.148;<0.001 -0.005; 0.856 0.301;<0.001

wet4_5000_if_cnt 0.055; 0.053 0.118;<0.001 0.247;<0.001 0.087; 0.005

wet1_all_if_xd -0.000; 0.990 0.217;<0.001 0.272;<0.001 0.052; 0.082

wet2_all_if_xd -0.274;<0.001 -0.128;<0.001 0.008; 0.766 -0.123;<0.001

wet3_all_if_xd -0.171;<0.001 -0.083; 0.001 -0.035; 0.175 -0.175;<0.001

wet4_all_if_xd -0.023; 0.385 0.191;<0.001 0.297;<0.001 -0.027; 0.356

wet1_if_density 0.250;<0.001 0.226;<0.001 0.134;<0.001 0.224;<0.001

wet2_if_density 0.293;<0.001 0.214;<0.001 0.173;<0.001 0.205;<0.001

wet3_if_density 0.054; 0.048 0.127;<0.001 0.265;<0.001 0.254;<0.001

wet4_if_density 0.130;<0.001 0.113;<0.001 0.135;<0.001 0.189;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 163 / 189

Table G.8: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_1000_cnt wet3_2500_cnt wet3_5000_cnt wet4_500_cnt

cws_shrub -0.125;<0.001 -0.099;<0.001 0.007; 0.788 0.097; 0.001

mrps -0.019; 0.489 0.029; 0.248 0.023; 0.342 -0.170;<0.001

topo 0.132;<0.001 0.035; 0.148 0.023; 0.343 0.133;<0.001

wet1_500kd 0.127;<0.001 0.058; 0.052 -0.054; 0.066 0.569;<0.001

wet1_1000kd 0.278;<0.001 0.053; 0.070 -0.100;<0.001 0.361;<0.001

wet1_2500kd 0.261;<0.001 0.279;<0.001 -0.003; 0.906 0.179;<0.001

wet1_5000kd 0.303;<0.001 0.424;<0.001 0.244;<0.001 0.159;<0.001

wet2_500kd 0.124;<0.001 0.043; 0.113 0.002; 0.927 0.206;<0.001

wet2_1000kd 0.275;<0.001 0.070; 0.005 0.006; 0.817 0.109;<0.001

wet2_2500kd 0.093;<0.001 0.162;<0.001 0.029; 0.229 0.023; 0.438

wet2_5000kd -0.057; 0.032 0.038; 0.119 0.064; 0.008 -0.126;<0.001

wet3_500kd 0.436;<0.001 0.274;<0.001 0.169;<0.001 0.321;<0.001

wet3_1000kd 0.821;<0.001 0.412;<0.001 0.242;<0.001 0.247;<0.001

wet3_2500kd 0.496;<0.001 0.750;<0.001 0.426;<0.001 0.164;<0.001

wet3_5000kd 0.359;<0.001 0.597;<0.001 0.695;<0.001 0.054; 0.062

wet4_500kd 0.168;<0.001 0.135;<0.001 0.016; 0.590 0.954;<0.001

wet4_1000kd 0.364;<0.001 0.206;<0.001 0.018; 0.521 0.573;<0.001

wet4_2500kd 0.376;<0.001 0.436;<0.001 0.153;<0.001 0.291;<0.001

wet4_5000kd 0.312;<0.001 0.419;<0.001 0.444;<0.001 0.177;<0.001

wet1_500_cnt 0.141;<0.001 0.066; 0.026 -0.044; 0.132 0.569;<0.001

wet1_1000_cnt 0.269;<0.001 0.055; 0.061 -0.098;<0.001 0.285;<0.001

wet1_2500_cnt 0.235;<0.001 0.327;<0.001 0.051; 0.059 0.172;<0.001

wet1_5000_cnt 0.314;<0.001 0.430;<0.001 0.338;<0.001 0.169;<0.001

wet2_500_cnt 0.210;<0.001 0.109;<0.001 0.045; 0.099 0.288;<0.001

wet2_1000_cnt 0.463;<0.001 0.216;<0.001 0.100;<0.001 0.169;<0.001

wet2_2500_cnt 0.387;<0.001 0.603;<0.001 0.340;<0.001 0.116;<0.001

wet2_5000_cnt 0.327;<0.001 0.548;<0.001 0.627;<0.001 0.004; 0.885

wet3_500_cnt 0.454;<0.001 0.295;<0.001 0.192;<0.001 0.330;<0.001

wet3_1000_cnt 1.000;<0.001 0.446;<0.001 0.298;<0.001 0.160;<0.001

wet3_2500_cnt 0.446;<0.001 1.000;<0.001 0.535;<0.001 0.119;<0.001

wet3_5000_cnt 0.298;<0.001 0.535;<0.001 1.000;<0.001 -0.001; 0.972

wet4_500_cnt 0.160;<0.001 0.119;<0.001 -0.001; 0.972 1.000;<0.001

wet4_1000_cnt 0.362;<0.001 0.197;<0.001 0.030; 0.297 0.286;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 164 / 189

Table G.8 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_1000_cnt wet3_2500_cnt wet3_5000_cnt wet4_500_cnt

wet4_2500_cnt 0.322;<0.001 0.440;<0.001 0.140;<0.001 0.248;<0.001

wet4_5000_cnt 0.226;<0.001 0.307;<0.001 0.381;<0.001 0.123;<0.001

wet1_all_cnt 0.406;<0.001 0.517;<0.001 0.389;<0.001 0.136;<0.001

wet2_all_cnt 0.307;<0.001 0.469;<0.001 0.494;<0.001 -0.067; 0.024

wet3_all_cnt 0.277;<0.001 0.437;<0.001 0.539;<0.001 -0.070; 0.017

wet4_all_cnt 0.134;<0.001 0.118;<0.001 0.149;<0.001 0.126;<0.001

field_size -0.135;<0.001 -0.137;<0.001 -0.183;<0.001 -0.168;<0.001

tro_all_cnt -0.364;<0.001 -0.401;<0.001 -0.388;<0.001 -0.118;<0.001

dug_all_cnt -0.131;<0.001 -0.091;<0.001 -0.135;<0.001 -0.200;<0.001

wet1_all_xd -0.089;<0.001 -0.167;<0.001 -0.038; 0.117 -0.111;<0.001

wet2_all_xd -0.325;<0.001 -0.442;<0.001 -0.412;<0.001 -0.111;<0.001

wet3_all_xd -0.373;<0.001 -0.553;<0.001 -0.621;<0.001 -0.111;<0.001

wet4_all_xd -0.224;<0.001 -0.313;<0.001 -0.293;<0.001 -0.187;<0.001

tro_all_xd 0.265;<0.001 0.419;<0.001 0.314;<0.001 -0.016; 0.629

dug_all_xd -0.056; 0.060 0.023; 0.405 -0.055; 0.040 -0.092; 0.005

wet1_500_if_cnt 0.139;<0.001 0.023; 0.441 -0.024; 0.407 0.416;<0.001

wet1_1000_if_cnt 0.231;<0.001 -0.013; 0.654 -0.070; 0.016 0.169;<0.001

wet1_2500_if_cnt 0.086; 0.006 0.024; 0.404 -0.087; 0.002 0.117;<0.001

wet1_5000_if_cnt 0.120;<0.001 0.101;<0.001 0.097;<0.001 0.081; 0.016

wet2_500_if_cnt 0.150;<0.001 0.055; 0.047 0.043; 0.120 0.205;<0.001

wet2_1000_if_cnt 0.267;<0.001 0.069; 0.009 0.042; 0.108 0.092; 0.004

wet2_2500_if_cnt 0.122;<0.001 0.090;<0.001 0.071; 0.004 0.018; 0.545

wet2_5000_if_cnt 0.087; 0.001 0.089;<0.001 0.084;<0.001 -0.013; 0.663

wet3_500_if_cnt 0.392;<0.001 0.243;<0.001 0.192;<0.001 0.282;<0.001

wet3_1000_if_cnt 0.686;<0.001 0.272;<0.001 0.230;<0.001 0.143;<0.001

wet3_2500_if_cnt 0.277;<0.001 0.370;<0.001 0.219;<0.001 0.063; 0.043

wet3_5000_if_cnt 0.133;<0.001 0.159;<0.001 0.307;<0.001 -0.029; 0.327

wet4_500_if_cnt 0.142;<0.001 0.076; 0.011 0.012; 0.673 0.836;<0.001

wet4_1000_if_cnt 0.306;<0.001 0.132;<0.001 0.040; 0.170 0.239;<0.001

wet4_2500_if_cnt 0.247;<0.001 0.140;<0.001 -0.004; 0.890 0.208;<0.001

wet4_5000_if_cnt 0.121;<0.001 0.138;<0.001 0.274;<0.001 -0.024; 0.449

wet1_all_if_xd 0.089; 0.002 0.258;<0.001 0.240;<0.001 0.013; 0.667

wet2_all_if_xd -0.088; 0.002 -0.022; 0.388 -0.024; 0.349 -0.036; 0.247
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 165 / 189

Table G.8 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_1000_cnt wet3_2500_cnt wet3_5000_cnt wet4_500_cnt

wet3_all_if_xd -0.146;<0.001 -0.077; 0.003 0.083; 0.001 -0.078; 0.011

wet4_all_if_xd 0.045; 0.107 0.198;<0.001 0.358;<0.001 -0.093; 0.003

wet1_if_density 0.194;<0.001 0.133;<0.001 0.019; 0.465 0.186;<0.001

wet2_if_density 0.175;<0.001 0.086; 0.001 0.074; 0.004 0.105;<0.001

wet3_if_density 0.301;<0.001 0.315;<0.001 0.471;<0.001 0.063; 0.043

wet4_if_density 0.185;<0.001 0.151;<0.001 0.301;<0.001 0.133;<0.001

Table G.9: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_1000_cnt wet4_2500_cnt wet4_5000_cnt wet1_all_cnt

cws_shrub 0.093; 0.002 -0.037; 0.164 0.078; 0.002 -0.059; 0.017

mrps -0.202;<0.001 -0.115;<0.001 -0.026; 0.300 -0.013; 0.599

topo 0.167;<0.001 0.185;<0.001 0.274;<0.001 0.195;<0.001

wet1_500kd 0.187;<0.001 0.206;<0.001 0.146;<0.001 0.125;<0.001

wet1_1000kd 0.522;<0.001 0.249;<0.001 0.187;<0.001 0.183;<0.001

wet1_2500kd 0.260;<0.001 0.500;<0.001 0.218;<0.001 0.305;<0.001

wet1_5000kd 0.183;<0.001 0.402;<0.001 0.471;<0.001 0.482;<0.001

wet2_500kd 0.079; 0.012 0.032; 0.259 -0.074; 0.006 -0.036; 0.181

wet2_1000kd 0.147;<0.001 -0.032; 0.234 -0.155;<0.001 -0.054; 0.032

wet2_2500kd 0.030; 0.300 -0.048; 0.065 -0.236;<0.001 -0.131;<0.001

wet2_5000kd -0.188;<0.001 -0.264;<0.001 -0.336;<0.001 -0.231;<0.001

wet3_500kd 0.259;<0.001 0.322;<0.001 0.192;<0.001 0.288;<0.001

wet3_1000kd 0.377;<0.001 0.341;<0.001 0.202;<0.001 0.388;<0.001

wet3_2500kd 0.266;<0.001 0.468;<0.001 0.263;<0.001 0.481;<0.001

wet3_5000kd 0.096;<0.001 0.239;<0.001 0.393;<0.001 0.413;<0.001

wet4_500kd 0.284;<0.001 0.245;<0.001 0.129;<0.001 0.148;<0.001

wet4_1000kd 0.842;<0.001 0.344;<0.001 0.167;<0.001 0.223;<0.001

wet4_2500kd 0.400;<0.001 0.846;<0.001 0.285;<0.001 0.393;<0.001

wet4_5000kd 0.245;<0.001 0.408;<0.001 0.761;<0.001 0.539;<0.001

wet1_500_cnt 0.223;<0.001 0.221;<0.001 0.157;<0.001 0.142;<0.001

wet1_1000_cnt 0.580;<0.001 0.240;<0.001 0.195;<0.001 0.179;<0.001

wet1_2500_cnt 0.227;<0.001 0.572;<0.001 0.311;<0.001 0.380;<0.001

wet1_5000_cnt 0.212;<0.001 0.388;<0.001 0.624;<0.001 0.578;<0.001

wet2_500_cnt 0.155;<0.001 0.117;<0.001 -0.001; 0.971 0.047; 0.081
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 166 / 189

Table G.9 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_1000_cnt wet4_2500_cnt wet4_5000_cnt wet1_all_cnt

wet2_1000_cnt 0.330;<0.001 0.112;<0.001 0.006; 0.826 0.125;<0.001

wet2_2500_cnt 0.179;<0.001 0.341;<0.001 0.131;<0.001 0.290;<0.001

wet2_5000_cnt 0.011; 0.706 0.122;<0.001 0.197;<0.001 0.377;<0.001

wet3_500_cnt 0.256;<0.001 0.336;<0.001 0.203;<0.001 0.314;<0.001

wet3_1000_cnt 0.362;<0.001 0.322;<0.001 0.226;<0.001 0.406;<0.001

wet3_2500_cnt 0.197;<0.001 0.440;<0.001 0.307;<0.001 0.517;<0.001

wet3_5000_cnt 0.030; 0.297 0.140;<0.001 0.381;<0.001 0.389;<0.001

wet4_500_cnt 0.286;<0.001 0.248;<0.001 0.123;<0.001 0.136;<0.001

wet4_1000_cnt 1.000;<0.001 0.316;<0.001 0.173;<0.001 0.231;<0.001

wet4_2500_cnt 0.316;<0.001 1.000;<0.001 0.316;<0.001 0.388;<0.001

wet4_5000_cnt 0.173;<0.001 0.316;<0.001 1.000;<0.001 0.476;<0.001

wet1_all_cnt 0.231;<0.001 0.388;<0.001 0.476;<0.001 1.000;<0.001

wet2_all_cnt -0.036; 0.218 0.021; 0.431 0.026; 0.300 0.440;<0.001

wet3_all_cnt -0.043; 0.133 0.006; 0.827 0.094;<0.001 0.425;<0.001

wet4_all_cnt 0.204;<0.001 0.307;<0.001 0.448;<0.001 0.406;<0.001

field_size -0.239;<0.001 -0.358;<0.001 -0.563;<0.001 -0.302;<0.001

tro_all_cnt -0.222;<0.001 -0.322;<0.001 -0.436;<0.001 -0.503;<0.001

dug_all_cnt -0.238;<0.001 -0.283;<0.001 -0.338;<0.001 -0.179;<0.001

wet1_all_xd -0.083; 0.004 -0.190;<0.001 -0.284;<0.001 -0.184;<0.001

wet2_all_xd -0.146;<0.001 -0.237;<0.001 -0.295;<0.001 -0.245;<0.001

wet3_all_xd -0.160;<0.001 -0.294;<0.001 -0.463;<0.001 -0.343;<0.001

wet4_all_xd -0.228;<0.001 -0.428;<0.001 -0.602;<0.001 -0.359;<0.001

tro_all_xd -0.030; 0.353 0.030; 0.314 0.004; 0.875 0.166;<0.001

dug_all_xd -0.135;<0.001 -0.105;<0.001 -0.269;<0.001 -0.135;<0.001

wet1_500_if_cnt 0.176;<0.001 0.136;<0.001 0.078; 0.009 0.105;<0.001

wet1_1000_if_cnt 0.396;<0.001 0.097; 0.002 0.049; 0.097 0.092; 0.002

wet1_2500_if_cnt 0.132;<0.001 0.149;<0.001 -0.035; 0.227 0.088; 0.002

wet1_5000_if_cnt 0.033; 0.311 0.055; 0.066 -0.092; 0.001 0.005; 0.860

wet2_500_if_cnt 0.087; 0.007 0.017; 0.558 -0.107;<0.001 -0.037; 0.177

wet2_1000_if_cnt 0.196;<0.001 -0.030; 0.291 -0.155;<0.001 -0.009; 0.731

wet2_2500_if_cnt 0.046; 0.114 0.002; 0.928 -0.232;<0.001 -0.052; 0.036

wet2_5000_if_cnt -0.038; 0.197 -0.060; 0.024 -0.220;<0.001 -0.018; 0.478

wet3_500_if_cnt 0.214;<0.001 0.240;<0.001 0.122;<0.001 0.258;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 167 / 189

Table G.9 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_1000_cnt wet4_2500_cnt wet4_5000_cnt wet1_all_cnt

wet3_1000_if_cnt 0.316;<0.001 0.202;<0.001 0.137;<0.001 0.308;<0.001

wet3_2500_if_cnt 0.134;<0.001 0.165;<0.001 -0.003; 0.910 0.256;<0.001

wet3_5000_if_cnt -0.031; 0.288 -0.033; 0.222 0.069; 0.006 0.099;<0.001

wet4_500_if_cnt 0.224;<0.001 0.168;<0.001 0.054; 0.071 0.100;<0.001

wet4_1000_if_cnt 0.770;<0.001 0.201;<0.001 0.065; 0.028 0.160;<0.001

wet4_2500_if_cnt 0.250;<0.001 0.441;<0.001 -0.013; 0.652 0.141;<0.001

wet4_5000_if_cnt -0.039; 0.217 -0.078; 0.007 0.157;<0.001 0.138;<0.001

wet1_all_if_xd -0.020; 0.513 0.155;<0.001 0.243;<0.001 0.207;<0.001

wet2_all_if_xd -0.137;<0.001 -0.054; 0.049 -0.063; 0.016 -0.031; 0.231

wet3_all_if_xd -0.161;<0.001 -0.094;<0.001 0.089;<0.001 -0.053; 0.038

wet4_all_if_xd -0.122;<0.001 -0.027; 0.324 0.288;<0.001 0.175;<0.001

wet1_if_density 0.199;<0.001 0.265;<0.001 0.076; 0.004 0.239;<0.001

wet2_if_density 0.131;<0.001 0.073; 0.009 -0.162;<0.001 0.046; 0.077

wet3_if_density 0.121;<0.001 0.142;<0.001 0.285;<0.001 0.359;<0.001

wet4_if_density 0.168;<0.001 0.171;<0.001 0.273;<0.001 0.145;<0.001

Table G.10: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_all_cnt wet3_all_cnt wet4_all_cnt field_size

cws_shrub -0.172;<0.001 -0.109;<0.001 0.091;<0.001 -0.200;<0.001

mrps 0.140;<0.001 0.104;<0.001 -0.021; 0.397 0.166;<0.001

topo -0.126;<0.001 -0.035; 0.148 0.294;<0.001 -0.306;<0.001

wet1_500kd -0.110;<0.001 -0.127;<0.001 0.119;<0.001 -0.166;<0.001

wet1_1000kd -0.154;<0.001 -0.186;<0.001 0.151;<0.001 -0.222;<0.001

wet1_2500kd 0.031; 0.237 -0.040; 0.123 0.116;<0.001 -0.174;<0.001

wet1_5000kd 0.206;<0.001 0.112;<0.001 0.176;<0.001 -0.232;<0.001

wet2_500kd 0.017; 0.532 -0.039; 0.144 -0.221;<0.001 0.022; 0.446

wet2_1000kd 0.063; 0.013 -0.011; 0.668 -0.312;<0.001 0.093;<0.001

wet2_2500kd 0.120;<0.001 0.037; 0.128 -0.418;<0.001 0.198;<0.001

wet2_5000kd 0.212;<0.001 0.109;<0.001 -0.588;<0.001 0.416;<0.001

wet3_500kd 0.156;<0.001 0.151;<0.001 0.138;<0.001 -0.176;<0.001

wet3_1000kd 0.268;<0.001 0.233;<0.001 0.143;<0.001 -0.151;<0.001

wet3_2500kd 0.431;<0.001 0.361;<0.001 0.107;<0.001 -0.116;<0.001

wet3_5000kd 0.470;<0.001 0.437;<0.001 0.125;<0.001 -0.134;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 168 / 189

Table G.10 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_all_cnt wet3_all_cnt wet4_all_cnt field_size

wet4_500kd -0.051; 0.083 -0.053; 0.068 0.126;<0.001 -0.165;<0.001

wet4_1000kd -0.045; 0.112 -0.053; 0.059 0.208;<0.001 -0.241;<0.001

wet4_2500kd 0.030; 0.245 0.026; 0.301 0.327;<0.001 -0.375;<0.001

wet4_5000kd 0.103;<0.001 0.178;<0.001 0.422;<0.001 -0.552;<0.001

wet1_500_cnt -0.105;<0.001 -0.122;<0.001 0.124;<0.001 -0.178;<0.001

wet1_1000_cnt -0.169;<0.001 -0.199;<0.001 0.154;<0.001 -0.226;<0.001

wet1_2500_cnt 0.028; 0.309 -0.022; 0.402 0.205;<0.001 -0.242;<0.001

wet1_5000_cnt 0.165;<0.001 0.160;<0.001 0.347;<0.001 -0.397;<0.001

wet2_500_cnt 0.044; 0.112 -0.007; 0.783 -0.149;<0.001 -0.027; 0.348

wet2_1000_cnt 0.126;<0.001 0.071; 0.005 -0.137;<0.001 -0.045; 0.103

wet2_2500_cnt 0.350;<0.001 0.266;<0.001 -0.126;<0.001 -0.031; 0.240

wet2_5000_cnt 0.572;<0.001 0.518;<0.001 0.008; 0.728 -0.005; 0.847

wet3_500_cnt 0.170;<0.001 0.168;<0.001 0.144;<0.001 -0.189;<0.001

wet3_1000_cnt 0.307;<0.001 0.277;<0.001 0.134;<0.001 -0.135;<0.001

wet3_2500_cnt 0.469;<0.001 0.437;<0.001 0.118;<0.001 -0.137;<0.001

wet3_5000_cnt 0.494;<0.001 0.539;<0.001 0.149;<0.001 -0.183;<0.001

wet4_500_cnt -0.067; 0.024 -0.070; 0.017 0.126;<0.001 -0.168;<0.001

wet4_1000_cnt -0.036; 0.218 -0.043; 0.133 0.204;<0.001 -0.239;<0.001

wet4_2500_cnt 0.021; 0.431 0.006; 0.827 0.307;<0.001 -0.358;<0.001

wet4_5000_cnt 0.026; 0.300 0.094;<0.001 0.448;<0.001 -0.563;<0.001

wet1_all_cnt 0.440;<0.001 0.425;<0.001 0.406;<0.001 -0.302;<0.001

wet2_all_cnt 1.000;<0.001 0.786;<0.001 -0.042; 0.087 0.185;<0.001

wet3_all_cnt 0.786;<0.001 1.000;<0.001 0.102;<0.001 0.066; 0.011

wet4_all_cnt -0.042; 0.087 0.102;<0.001 1.000;<0.001 -0.504;<0.001

field_size 0.185;<0.001 0.066; 0.011 -0.504;<0.001 1.000;<0.001

tro_all_cnt -0.234;<0.001 -0.280;<0.001 -0.379;<0.001 0.288;<0.001

dug_all_cnt 0.201;<0.001 0.090;<0.001 -0.259;<0.001 0.641;<0.001

wet1_all_xd 0.077; 0.002 0.115;<0.001 -0.096;<0.001 0.143;<0.001

wet2_all_xd -0.191;<0.001 -0.164;<0.001 0.003; 0.898 0.194;<0.001

wet3_all_xd -0.219;<0.001 -0.254;<0.001 -0.174;<0.001 0.303;<0.001

wet4_all_xd 0.032; 0.188 0.023; 0.347 -0.298;<0.001 0.428;<0.001

tro_all_xd 0.443;<0.001 0.353;<0.001 -0.162;<0.001 0.290;<0.001

dug_all_xd 0.111;<0.001 0.075; 0.005 -0.155;<0.001 0.438;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 169 / 189

Table G.10 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_all_cnt wet3_all_cnt wet4_all_cnt field_size

wet1_500_if_cnt -0.053; 0.077 -0.067; 0.023 0.055; 0.063 -0.118;<0.001

wet1_1000_if_cnt -0.068; 0.022 -0.090; 0.002 0.037; 0.207 -0.094; 0.003

wet1_2500_if_cnt -0.010; 0.728 -0.022; 0.443 -0.043; 0.135 -0.013; 0.665

wet1_5000_if_cnt 0.114;<0.001 0.067; 0.017 -0.166;<0.001 0.125;<0.001

wet2_500_if_cnt 0.076; 0.006 0.021; 0.453 -0.252;<0.001 0.079; 0.007

wet2_1000_if_cnt 0.138;<0.001 0.073; 0.006 -0.278;<0.001 0.118;<0.001

wet2_2500_if_cnt 0.211;<0.001 0.144;<0.001 -0.368;<0.001 0.224;<0.001

wet2_5000_if_cnt 0.279;<0.001 0.225;<0.001 -0.284;<0.001 0.262;<0.001

wet3_500_if_cnt 0.201;<0.001 0.203;<0.001 0.080; 0.005 -0.122;<0.001

wet3_1000_if_cnt 0.279;<0.001 0.257;<0.001 0.083; 0.003 -0.071; 0.017

wet3_2500_if_cnt 0.399;<0.001 0.362;<0.001 -0.035; 0.171 0.106;<0.001

wet3_5000_if_cnt 0.329;<0.001 0.386;<0.001 0.064; 0.010 0.122;<0.001

wet4_500_if_cnt -0.024; 0.423 -0.022; 0.456 0.074; 0.011 -0.109;<0.001

wet4_1000_if_cnt 0.039; 0.189 0.040; 0.168 0.119;<0.001 -0.120;<0.001

wet4_2500_if_cnt 0.042; 0.134 0.055; 0.049 0.105;<0.001 -0.092; 0.002

wet4_5000_if_cnt 0.251;<0.001 0.295;<0.001 0.011; 0.673 -0.084; 0.003

wet1_all_if_xd 0.137;<0.001 0.159;<0.001 0.140;<0.001 -0.220;<0.001

wet2_all_if_xd -0.007; 0.785 0.036; 0.160 0.134;<0.001 0.115;<0.001

wet3_all_if_xd -0.046; 0.077 0.026; 0.308 0.129;<0.001 -0.104;<0.001

wet4_all_if_xd 0.198;<0.001 0.243;<0.001 0.088;<0.001 -0.234;<0.001

wet1_if_density 0.019; 0.468 0.023; 0.379 0.060; 0.021 -0.224;<0.001

wet2_if_density 0.149;<0.001 0.149;<0.001 -0.139;<0.001 -0.060; 0.030

wet3_if_density 0.356;<0.001 0.456;<0.001 0.314;<0.001 -0.145;<0.001

wet4_if_density 0.075; 0.004 0.178;<0.001 0.219;<0.001 -0.524;<0.001

Table G.11: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors tro_all_cnt dug_all_cnt wet1_all_xd wet2_all_xd

cws_shrub 0.044; 0.114 -0.205;<0.001 -0.056; 0.024 -0.007; 0.766

mrps -0.031; 0.262 0.400;<0.001 -0.043; 0.083 0.062; 0.012

topo -0.239;<0.001 -0.276;<0.001 -0.040; 0.094 0.010; 0.685

wet1_500kd -0.105; 0.002 -0.110;<0.001 -0.165;<0.001 -0.128;<0.001

wet1_1000kd -0.156;<0.001 -0.187;<0.001 -0.171;<0.001 -0.129;<0.001

wet1_2500kd -0.153;<0.001 -0.138;<0.001 -0.249;<0.001 -0.269;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 170 / 189

Table G.11 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors tro_all_cnt dug_all_cnt wet1_all_xd wet2_all_xd

wet1_5000kd -0.246;<0.001 -0.095;<0.001 -0.486;<0.001 -0.426;<0.001

wet2_500kd 0.099;<0.001 -0.129;<0.001 0.013; 0.622 -0.155;<0.001

wet2_1000kd 0.158;<0.001 -0.117;<0.001 -0.006; 0.799 -0.229;<0.001

wet2_2500kd 0.239;<0.001 -0.013; 0.616 -0.048; 0.049 -0.283;<0.001

wet2_5000kd 0.286;<0.001 0.201;<0.001 -0.039; 0.104 -0.204;<0.001

wet3_500kd -0.277;<0.001 -0.184;<0.001 -0.082; 0.003 -0.237;<0.001

wet3_1000kd -0.346;<0.001 -0.134;<0.001 -0.096;<0.001 -0.300;<0.001

wet3_2500kd -0.443;<0.001 -0.061; 0.021 -0.161;<0.001 -0.368;<0.001

wet3_5000kd -0.463;<0.001 -0.045; 0.085 -0.178;<0.001 -0.399;<0.001

wet4_500kd -0.126;<0.001 -0.195;<0.001 -0.099;<0.001 -0.115;<0.001

wet4_1000kd -0.200;<0.001 -0.258;<0.001 -0.100;<0.001 -0.151;<0.001

wet4_2500kd -0.332;<0.001 -0.319;<0.001 -0.156;<0.001 -0.235;<0.001

wet4_5000kd -0.447;<0.001 -0.394;<0.001 -0.230;<0.001 -0.351;<0.001

wet1_500_cnt -0.126;<0.001 -0.128;<0.001 -0.179;<0.001 -0.133;<0.001

wet1_1000_cnt -0.177;<0.001 -0.197;<0.001 -0.173;<0.001 -0.128;<0.001

wet1_2500_cnt -0.240;<0.001 -0.152;<0.001 -0.303;<0.001 -0.276;<0.001

wet1_5000_cnt -0.364;<0.001 -0.209;<0.001 -0.427;<0.001 -0.405;<0.001

wet2_500_cnt 0.013; 0.673 -0.164;<0.001 -0.006; 0.814 -0.182;<0.001

wet2_1000_cnt -0.043; 0.140 -0.203;<0.001 -0.010; 0.707 -0.268;<0.001

wet2_2500_cnt -0.158;<0.001 -0.138;<0.001 -0.152;<0.001 -0.488;<0.001

wet2_5000_cnt -0.225;<0.001 -0.022; 0.406 -0.160;<0.001 -0.520;<0.001

wet3_500_cnt -0.295;<0.001 -0.204;<0.001 -0.080; 0.005 -0.250;<0.001

wet3_1000_cnt -0.364;<0.001 -0.131;<0.001 -0.089;<0.001 -0.325;<0.001

wet3_2500_cnt -0.401;<0.001 -0.091;<0.001 -0.167;<0.001 -0.442;<0.001

wet3_5000_cnt -0.388;<0.001 -0.135;<0.001 -0.038; 0.117 -0.412;<0.001

wet4_500_cnt -0.118;<0.001 -0.200;<0.001 -0.111;<0.001 -0.111;<0.001

wet4_1000_cnt -0.222;<0.001 -0.238;<0.001 -0.083; 0.004 -0.146;<0.001

wet4_2500_cnt -0.322;<0.001 -0.283;<0.001 -0.190;<0.001 -0.237;<0.001

wet4_5000_cnt -0.436;<0.001 -0.338;<0.001 -0.284;<0.001 -0.295;<0.001

wet1_all_cnt -0.503;<0.001 -0.179;<0.001 -0.184;<0.001 -0.245;<0.001

wet2_all_cnt -0.234;<0.001 0.201;<0.001 0.077; 0.002 -0.191;<0.001

wet3_all_cnt -0.280;<0.001 0.090;<0.001 0.115;<0.001 -0.164;<0.001

wet4_all_cnt -0.379;<0.001 -0.259;<0.001 -0.096;<0.001 0.003; 0.898
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 171 / 189

Table G.11 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors tro_all_cnt dug_all_cnt wet1_all_xd wet2_all_xd

field_size 0.288;<0.001 0.641;<0.001 0.143;<0.001 0.194;<0.001

tro_all_cnt 1.000;<0.001 0.106;<0.001 0.088; 0.001 0.155;<0.001

dug_all_cnt 0.106;<0.001 1.000;<0.001 0.049; 0.061 0.214;<0.001

wet1_all_xd 0.088; 0.001 0.049; 0.061 1.000;<0.001 0.331;<0.001

wet2_all_xd 0.155;<0.001 0.214;<0.001 0.331;<0.001 1.000;<0.001

wet3_all_xd 0.415;<0.001 0.245;<0.001 0.235;<0.001 0.569;<0.001

wet4_all_xd 0.373;<0.001 0.315;<0.001 0.402;<0.001 0.345;<0.001

tro_all_xd -0.219;<0.001 0.317;<0.001 -0.217;<0.001 -0.294;<0.001

dug_all_xd 0.076; 0.014 0.344;<0.001 -0.056; 0.034 0.014; 0.596

wet1_500_if_cnt -0.147;<0.001 -0.142;<0.001 -0.093; 0.001 -0.082; 0.005

wet1_1000_if_cnt -0.151;<0.001 -0.165;<0.001 -0.015; 0.613 -0.039; 0.180

wet1_2500_if_cnt -0.097; 0.003 -0.192;<0.001 0.121;<0.001 0.006; 0.839

wet1_5000_if_cnt 0.045; 0.159 -0.113;<0.001 -0.021; 0.453 -0.163;<0.001

wet2_500_if_cnt 0.009; 0.762 -0.121;<0.001 0.077; 0.005 -0.131;<0.001

wet2_1000_if_cnt 0.010; 0.734 -0.114;<0.001 0.127;<0.001 -0.129;<0.001

wet2_2500_if_cnt 0.039; 0.168 -0.010; 0.713 0.218;<0.001 -0.082;<0.001

wet2_5000_if_cnt 0.055; 0.051 0.040; 0.137 0.126;<0.001 -0.080; 0.001

wet3_500_if_cnt -0.308;<0.001 -0.195;<0.001 0.013; 0.658 -0.178;<0.001

wet3_1000_if_cnt -0.363;<0.001 -0.122;<0.001 0.078; 0.005 -0.157;<0.001

wet3_2500_if_cnt -0.271;<0.001 0.054; 0.053 0.167;<0.001 -0.081; 0.002

wet3_5000_if_cnt -0.298;<0.001 0.083; 0.002 0.160;<0.001 -0.051; 0.040

wet4_500_if_cnt -0.152;<0.001 -0.201;<0.001 -0.053; 0.070 -0.066; 0.023

wet4_1000_if_cnt -0.228;<0.001 -0.192;<0.001 0.023; 0.436 -0.057; 0.050

wet4_2500_if_cnt -0.199;<0.001 -0.245;<0.001 0.144;<0.001 0.013; 0.628

wet4_5000_if_cnt -0.158;<0.001 -0.132;<0.001 0.107;<0.001 -0.127;<0.001

wet1_all_if_xd 0.012; 0.685 -0.174;<0.001 -0.308;<0.001 -0.245;<0.001

wet2_all_if_xd 0.052; 0.080 0.106;<0.001 -0.047; 0.062 0.130;<0.001

wet3_all_if_xd 0.002; 0.948 -0.066; 0.019 -0.024; 0.350 0.025; 0.318

wet4_all_if_xd -0.118;<0.001 -0.199;<0.001 -0.095;<0.001 -0.284;<0.001

wet1_if_density 0.170;<0.001 -0.562;<0.001 -0.045; 0.083 -0.190;<0.001

wet2_if_density 0.014; 0.644 -0.391;<0.001 0.140;<0.001 -0.154;<0.001

wet3_if_density -0.641;<0.001 -0.198;<0.001 0.164;<0.001 -0.103;<0.001

wet4_if_density -0.318;<0.001 -0.579;<0.001 0.151;<0.001 -0.154;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 172 / 189

Table G.12: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_all_xd wet4_all_xd tro_all_xd dug_all_xd

cws_shrub -0.007; 0.763 -0.048; 0.049 -0.090; 0.001 -0.096;<0.001

mrps 0.029; 0.242 0.087;<0.001 0.306;<0.001 -0.049; 0.075

topo -0.082;<0.001 -0.215;<0.001 -0.317;<0.001 -0.161;<0.001

wet1_500kd -0.075; 0.010 -0.159;<0.001 -0.035; 0.286 -0.129;<0.001

wet1_1000kd -0.061; 0.033 -0.172;<0.001 -0.109;<0.001 -0.196;<0.001

wet1_2500kd -0.124;<0.001 -0.220;<0.001 0.004; 0.883 -0.164;<0.001

wet1_5000kd -0.331;<0.001 -0.448;<0.001 0.345;<0.001 -0.065; 0.017

wet2_500kd -0.037; 0.168 0.001; 0.965 0.007; 0.825 -0.034; 0.244

wet2_1000kd -0.048; 0.053 0.057; 0.022 0.081; 0.004 0.036; 0.185

wet2_2500kd -0.039; 0.108 0.145;<0.001 0.194;<0.001 0.103;<0.001

wet2_5000kd 0.042; 0.079 0.257;<0.001 0.305;<0.001 0.151;<0.001

wet3_500kd -0.237;<0.001 -0.214;<0.001 0.112;<0.001 -0.122;<0.001

wet3_1000kd -0.332;<0.001 -0.212;<0.001 0.237;<0.001 -0.092; 0.002

wet3_2500kd -0.457;<0.001 -0.294;<0.001 0.432;<0.001 -0.019; 0.486

wet3_5000kd -0.584;<0.001 -0.370;<0.001 0.433;<0.001 -0.084; 0.002

wet4_500kd -0.125;<0.001 -0.188;<0.001 -0.015; 0.637 -0.088; 0.006

wet4_1000kd -0.161;<0.001 -0.237;<0.001 -0.022; 0.503 -0.155;<0.001

wet4_2500kd -0.290;<0.001 -0.379;<0.001 0.044; 0.131 -0.106;<0.001

wet4_5000kd -0.506;<0.001 -0.567;<0.001 0.022; 0.421 -0.218;<0.001

wet1_500_cnt -0.082; 0.005 -0.174;<0.001 -0.035; 0.291 -0.130;<0.001

wet1_1000_cnt -0.074; 0.010 -0.185;<0.001 -0.092; 0.005 -0.163;<0.001

wet1_2500_cnt -0.198;<0.001 -0.325;<0.001 0.039; 0.206 -0.110;<0.001

wet1_5000_cnt -0.425;<0.001 -0.528;<0.001 0.245;<0.001 -0.092;<0.001

wet2_500_cnt -0.097;<0.001 -0.079; 0.003 0.023; 0.459 -0.059; 0.051

wet2_1000_cnt -0.168;<0.001 -0.087;<0.001 0.097;<0.001 -0.029; 0.311

wet2_2500_cnt -0.379;<0.001 -0.186;<0.001 0.337;<0.001 0.083; 0.002

wet2_5000_cnt -0.465;<0.001 -0.162;<0.001 0.414;<0.001 0.069; 0.009

wet3_500_cnt -0.255;<0.001 -0.224;<0.001 0.108;<0.001 -0.136;<0.001

wet3_1000_cnt -0.373;<0.001 -0.224;<0.001 0.265;<0.001 -0.056; 0.060

wet3_2500_cnt -0.553;<0.001 -0.313;<0.001 0.419;<0.001 0.023; 0.405

wet3_5000_cnt -0.621;<0.001 -0.293;<0.001 0.314;<0.001 -0.055; 0.040

wet4_500_cnt -0.111;<0.001 -0.187;<0.001 -0.016; 0.629 -0.092; 0.005

wet4_1000_cnt -0.160;<0.001 -0.228;<0.001 -0.030; 0.353 -0.135;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 173 / 189

Table G.12 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_all_xd wet4_all_xd tro_all_xd dug_all_xd

wet4_2500_cnt -0.294;<0.001 -0.428;<0.001 0.030; 0.314 -0.105;<0.001

wet4_5000_cnt -0.463;<0.001 -0.602;<0.001 0.004; 0.875 -0.269;<0.001

wet1_all_cnt -0.343;<0.001 -0.359;<0.001 0.166;<0.001 -0.135;<0.001

wet2_all_cnt -0.219;<0.001 0.032; 0.188 0.443;<0.001 0.111;<0.001

wet3_all_cnt -0.254;<0.001 0.023; 0.347 0.353;<0.001 0.075; 0.005

wet4_all_cnt -0.174;<0.001 -0.298;<0.001 -0.162;<0.001 -0.155;<0.001

field_size 0.303;<0.001 0.428;<0.001 0.290;<0.001 0.438;<0.001

tro_all_cnt 0.415;<0.001 0.373;<0.001 -0.219;<0.001 0.076; 0.014

dug_all_cnt 0.245;<0.001 0.315;<0.001 0.317;<0.001 0.344;<0.001

wet1_all_xd 0.235;<0.001 0.402;<0.001 -0.217;<0.001 -0.056; 0.034

wet2_all_xd 0.569;<0.001 0.345;<0.001 -0.294;<0.001 0.014; 0.596

wet3_all_xd 1.000;<0.001 0.539;<0.001 -0.273;<0.001 0.046; 0.087

wet4_all_xd 0.539;<0.001 1.000;<0.001 -0.082; 0.002 0.077; 0.004

tro_all_xd -0.273;<0.001 -0.082; 0.002 1.000;<0.001 0.229;<0.001

dug_all_xd 0.046; 0.087 0.077; 0.004 0.229;<0.001 1.000;<0.001

wet1_500_if_cnt -0.034; 0.240 -0.098;<0.001 -0.054; 0.105 -0.144;<0.001

wet1_1000_if_cnt 0.012; 0.673 -0.032; 0.269 -0.082; 0.013 -0.117;<0.001

wet1_2500_if_cnt 0.105;<0.001 0.095;<0.001 -0.154;<0.001 -0.130;<0.001

wet1_5000_if_cnt -0.115;<0.001 -0.088; 0.001 0.131;<0.001 0.391;<0.001

wet2_500_if_cnt -0.044; 0.108 0.012; 0.666 0.061; 0.048 -0.007; 0.826

wet2_1000_if_cnt -0.028; 0.277 0.061; 0.019 0.088; 0.003 -0.021; 0.477

wet2_2500_if_cnt 0.040; 0.103 0.179;<0.001 0.096;<0.001 -0.027; 0.320

wet2_5000_if_cnt 0.046; 0.064 0.215;<0.001 0.132;<0.001 0.132;<0.001

wet3_500_if_cnt -0.195;<0.001 -0.136;<0.001 0.150;<0.001 -0.118;<0.001

wet3_1000_if_cnt -0.206;<0.001 -0.082; 0.003 0.194;<0.001 -0.157;<0.001

wet3_2500_if_cnt -0.110;<0.001 0.078; 0.002 0.274;<0.001 -0.116;<0.001

wet3_5000_if_cnt -0.180;<0.001 -0.027; 0.269 0.210;<0.001 0.188;<0.001

wet4_500_if_cnt -0.064; 0.029 -0.123;<0.001 -0.006; 0.853 -0.073; 0.025

wet4_1000_if_cnt -0.078; 0.007 -0.106;<0.001 0.005; 0.876 -0.096; 0.003

wet4_2500_if_cnt 0.015; 0.597 -0.011; 0.696 -0.046; 0.147 -0.131;<0.001

wet4_5000_if_cnt -0.168;<0.001 -0.041; 0.124 0.092; 0.002 0.096; 0.001

wet1_all_if_xd -0.275;<0.001 -0.350;<0.001 0.174;<0.001 0.158;<0.001

wet2_all_if_xd 0.032; 0.214 0.031; 0.229 0.084; 0.003 0.464;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 174 / 189

Table G.12 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_all_xd wet4_all_xd tro_all_xd dug_all_xd

wet3_all_if_xd -0.073; 0.004 -0.150;<0.001 -0.072; 0.011 0.390;<0.001

wet4_all_if_xd -0.333;<0.001 -0.262;<0.001 0.080; 0.005 0.138;<0.001

wet1_if_density -0.038; 0.143 -0.082; 0.002 -0.128;<0.001 0.021; 0.472

wet2_if_density -0.003; 0.923 0.121;<0.001 0.017; 0.579 0.167;<0.001

wet3_if_density -0.346;<0.001 -0.177;<0.001 0.223;<0.001 -0.006; 0.836

wet4_if_density -0.258;<0.001 -0.184;<0.001 -0.064; 0.034 -0.199;<0.001

Table G.13: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_500_if_cnt wet1_1000_if_cnt wet1_2500_if_cnt wet1_5000_if_cnt

cws_shrub 0.021; 0.489 -0.035; 0.247 -0.046; 0.114 -0.030; 0.283

mrps -0.080; 0.008 -0.117;<0.001 -0.246;<0.001 -0.236;<0.001

topo 0.072; 0.014 0.077; 0.009 0.082; 0.004 -0.018; 0.511

wet1_500kd 0.747;<0.001 0.155;<0.001 0.042; 0.225 -0.069; 0.039

wet1_1000kd 0.441;<0.001 0.678;<0.001 0.027; 0.420 -0.041; 0.209

wet1_2500kd 0.176;<0.001 0.194;<0.001 0.386;<0.001 -0.100;<0.001

wet1_5000kd 0.094; 0.002 0.050; 0.091 0.018; 0.547 0.065; 0.022

wet2_500kd 0.180;<0.001 0.095; 0.003 0.123;<0.001 0.104;<0.001

wet2_1000kd 0.113;<0.001 0.150;<0.001 0.113;<0.001 0.178;<0.001

wet2_2500kd 0.017; 0.567 0.010; 0.742 0.068; 0.017 0.136;<0.001

wet2_5000kd -0.087; 0.003 -0.106;<0.001 -0.059; 0.038 0.097;<0.001

wet3_500kd 0.258;<0.001 0.166;<0.001 0.166;<0.001 0.044; 0.168

wet3_1000kd 0.204;<0.001 0.239;<0.001 0.109;<0.001 0.065; 0.030

wet3_2500kd 0.084; 0.005 0.067; 0.022 0.058; 0.042 0.034; 0.219

wet3_5000kd 0.001; 0.981 -0.047; 0.104 -0.096;<0.001 0.020; 0.473

wet4_500kd 0.389;<0.001 0.154;<0.001 0.111; 0.001 0.076; 0.023

wet4_1000kd 0.249;<0.001 0.361;<0.001 0.121;<0.001 0.063; 0.051

wet4_2500kd 0.152;<0.001 0.142;<0.001 0.119;<0.001 0.049; 0.093

wet4_5000kd 0.094; 0.001 0.059; 0.047 0.004; 0.884 0.003; 0.911

wet1_500_cnt 0.779;<0.001 0.168;<0.001 0.067; 0.052 -0.055; 0.102

wet1_1000_cnt 0.219;<0.001 0.758;<0.001 0.051; 0.140 -0.035; 0.286

wet1_2500_cnt 0.095; 0.003 0.030; 0.352 0.449;<0.001 -0.099; 0.001

wet1_5000_cnt 0.091; 0.002 0.038; 0.205 -0.054; 0.065 0.092; 0.001

wet2_500_cnt 0.191;<0.001 0.103; 0.002 0.172;<0.001 0.121;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 175 / 189

Table G.13 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_500_if_cnt wet1_1000_if_cnt wet1_2500_if_cnt wet1_5000_if_cnt

wet2_1000_cnt 0.090; 0.004 0.177;<0.001 0.184;<0.001 0.202;<0.001

wet2_2500_cnt 0.036; 0.227 0.011; 0.710 0.078; 0.007 0.217;<0.001

wet2_5000_cnt -0.004; 0.883 -0.052; 0.076 -0.030; 0.303 0.176;<0.001

wet3_500_cnt 0.265;<0.001 0.155;<0.001 0.176;<0.001 0.063; 0.051

wet3_1000_cnt 0.139;<0.001 0.231;<0.001 0.086; 0.006 0.120;<0.001

wet3_2500_cnt 0.023; 0.441 -0.013; 0.654 0.024; 0.404 0.101;<0.001

wet3_5000_cnt -0.024; 0.407 -0.070; 0.016 -0.087; 0.002 0.097;<0.001

wet4_500_cnt 0.416;<0.001 0.169;<0.001 0.117;<0.001 0.081; 0.016

wet4_1000_cnt 0.176;<0.001 0.396;<0.001 0.132;<0.001 0.033; 0.311

wet4_2500_cnt 0.136;<0.001 0.097; 0.002 0.149;<0.001 0.055; 0.066

wet4_5000_cnt 0.078; 0.009 0.049; 0.097 -0.035; 0.227 -0.092; 0.001

wet1_all_cnt 0.105;<0.001 0.092; 0.002 0.088; 0.002 0.005; 0.860

wet2_all_cnt -0.053; 0.077 -0.068; 0.022 -0.010; 0.728 0.114;<0.001

wet3_all_cnt -0.067; 0.023 -0.090; 0.002 -0.022; 0.443 0.067; 0.017

wet4_all_cnt 0.055; 0.063 0.037; 0.207 -0.043; 0.135 -0.166;<0.001

field_size -0.118;<0.001 -0.094; 0.003 -0.013; 0.665 0.125;<0.001

tro_all_cnt -0.147;<0.001 -0.151;<0.001 -0.097; 0.003 0.045; 0.159

dug_all_cnt -0.142;<0.001 -0.165;<0.001 -0.192;<0.001 -0.113;<0.001

wet1_all_xd -0.093; 0.001 -0.015; 0.613 0.121;<0.001 -0.021; 0.453

wet2_all_xd -0.082; 0.005 -0.039; 0.180 0.006; 0.839 -0.163;<0.001

wet3_all_xd -0.034; 0.240 0.012; 0.673 0.105;<0.001 -0.115;<0.001

wet4_all_xd -0.098;<0.001 -0.032; 0.269 0.095;<0.001 -0.088; 0.001

tro_all_xd -0.054; 0.105 -0.082; 0.013 -0.154;<0.001 0.131;<0.001

dug_all_xd -0.144;<0.001 -0.117;<0.001 -0.130;<0.001 0.391;<0.001

wet1_500_if_cnt 1.000;<0.001 0.215;<0.001 0.140;<0.001 0.026; 0.436

wet1_1000_if_cnt 0.215;<0.001 1.000;<0.001 0.108; 0.002 0.078; 0.021

wet1_2500_if_cnt 0.140;<0.001 0.108; 0.002 1.000;<0.001 0.036; 0.279

wet1_5000_if_cnt 0.026; 0.436 0.078; 0.021 0.036; 0.279 1.000;<0.001

wet2_500_if_cnt 0.222;<0.001 0.144;<0.001 0.253;<0.001 0.210;<0.001

wet2_1000_if_cnt 0.105;<0.001 0.244;<0.001 0.314;<0.001 0.320;<0.001

wet2_2500_if_cnt 0.090; 0.003 0.157;<0.001 0.410;<0.001 0.385;<0.001

wet2_5000_if_cnt 0.012; 0.698 0.104;<0.001 0.327;<0.001 0.463;<0.001

wet3_500_if_cnt 0.331;<0.001 0.222;<0.001 0.296;<0.001 0.191;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 176 / 189

Table G.13 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_500_if_cnt wet1_1000_if_cnt wet1_2500_if_cnt wet1_5000_if_cnt

wet3_1000_if_cnt 0.217;<0.001 0.356;<0.001 0.298;<0.001 0.252;<0.001

wet3_2500_if_cnt 0.089; 0.004 0.131;<0.001 0.419;<0.001 0.271;<0.001

wet3_5000_if_cnt -0.017; 0.566 0.027; 0.366 0.113;<0.001 0.378;<0.001

wet4_500_if_cnt 0.501;<0.001 0.225;<0.001 0.195;<0.001 0.184;<0.001

wet4_1000_if_cnt 0.236;<0.001 0.532;<0.001 0.228;<0.001 0.150;<0.001

wet4_2500_if_cnt 0.200;<0.001 0.235;<0.001 0.587;<0.001 0.267;<0.001

wet4_5000_if_cnt 0.032; 0.326 0.100; 0.002 0.195;<0.001 0.495;<0.001

wet1_all_if_xd -0.185;<0.001 -0.254;<0.001 -0.353;<0.001 0.074; 0.012

wet2_all_if_xd -0.157;<0.001 -0.212;<0.001 -0.273;<0.001 0.030; 0.303

wet3_all_if_xd -0.161;<0.001 -0.201;<0.001 -0.302;<0.001 -0.005; 0.857

wet4_all_if_xd -0.164;<0.001 -0.216;<0.001 -0.297;<0.001 -0.013; 0.658

wet1_if_density 0.182;<0.001 0.237;<0.001 0.370;<0.001 0.415;<0.001

wet2_if_density 0.166;<0.001 0.227;<0.001 0.417;<0.001 0.515;<0.001

wet3_if_density 0.128;<0.001 0.138;<0.001 0.164;<0.001 0.112;<0.001

wet4_if_density 0.172;<0.001 0.198;<0.001 0.265;<0.001 0.209;<0.001

Table G.14: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_500_if_cnt wet2_1000_if_cnt wet2_2500_if_cnt wet2_5000_if_cnt

cws_shrub -0.043; 0.129 -0.049; 0.067 -0.083;<0.001 -0.145;<0.001

mrps -0.147;<0.001 -0.187;<0.001 -0.184;<0.001 -0.164;<0.001

topo -0.091;<0.001 -0.095;<0.001 -0.136;<0.001 -0.108;<0.001

wet1_500kd 0.127;<0.001 -0.008; 0.791 -0.033; 0.272 -0.089; 0.003

wet1_1000kd 0.088; 0.007 0.077; 0.013 0.012; 0.694 -0.043; 0.145

wet1_2500kd 0.041; 0.165 0.026; 0.365 0.051; 0.055 0.135;<0.001

wet1_5000kd -0.008; 0.786 -0.021; 0.428 -0.059; 0.019 -0.009; 0.720

wet2_500kd 0.749;<0.001 0.315;<0.001 0.224;<0.001 0.058; 0.031

wet2_1000kd 0.462;<0.001 0.520;<0.001 0.270;<0.001 0.133;<0.001

wet2_2500kd 0.297;<0.001 0.293;<0.001 0.273;<0.001 0.177;<0.001

wet2_5000kd 0.183;<0.001 0.190;<0.001 0.260;<0.001 0.266;<0.001

wet3_500kd 0.348;<0.001 0.165;<0.001 0.077; 0.006 0.039; 0.168

wet3_1000kd 0.210;<0.001 0.241;<0.001 0.093;<0.001 0.047; 0.076

wet3_2500kd 0.065; 0.018 0.087;<0.001 0.092;<0.001 0.066; 0.008

wet3_5000kd 0.003; 0.909 -0.007; 0.789 0.007; 0.787 0.027; 0.278
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 177 / 189

Table G.14 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_500_if_cnt wet2_1000_if_cnt wet2_2500_if_cnt wet2_5000_if_cnt

wet4_500kd 0.186;<0.001 0.081; 0.010 0.017; 0.558 -0.006; 0.852

wet4_1000kd 0.142;<0.001 0.196;<0.001 0.059; 0.041 -0.030; 0.301

wet4_2500kd 0.036; 0.214 0.006; 0.822 0.000; 0.985 -0.068; 0.009

wet4_5000kd -0.051; 0.066 -0.077; 0.003 -0.163;<0.001 -0.191;<0.001

wet1_500_cnt 0.147;<0.001 0.013; 0.675 -0.025; 0.398 -0.081; 0.006

wet1_1000_cnt 0.050; 0.127 0.097; 0.002 -0.006; 0.829 -0.053; 0.074

wet1_2500_cnt 0.006; 0.837 -0.011; 0.708 -0.003; 0.900 0.060; 0.028

wet1_5000_cnt -0.039; 0.167 -0.057; 0.034 -0.149;<0.001 -0.111;<0.001

wet2_500_cnt 0.866;<0.001 0.356;<0.001 0.241;<0.001 0.056; 0.043

wet2_1000_cnt 0.386;<0.001 0.734;<0.001 0.276;<0.001 0.118;<0.001

wet2_2500_cnt 0.207;<0.001 0.211;<0.001 0.319;<0.001 0.160;<0.001

wet2_5000_cnt 0.051; 0.063 0.087;<0.001 0.118;<0.001 0.226;<0.001

wet3_500_cnt 0.360;<0.001 0.174;<0.001 0.096;<0.001 0.052; 0.070

wet3_1000_cnt 0.150;<0.001 0.267;<0.001 0.122;<0.001 0.087; 0.001

wet3_2500_cnt 0.055; 0.047 0.069; 0.009 0.090;<0.001 0.089;<0.001

wet3_5000_cnt 0.043; 0.120 0.042; 0.108 0.071; 0.004 0.084;<0.001

wet4_500_cnt 0.205;<0.001 0.092; 0.004 0.018; 0.545 -0.013; 0.663

wet4_1000_cnt 0.087; 0.007 0.196;<0.001 0.046; 0.114 -0.038; 0.197

wet4_2500_cnt 0.017; 0.558 -0.030; 0.291 0.002; 0.928 -0.060; 0.024

wet4_5000_cnt -0.107;<0.001 -0.155;<0.001 -0.232;<0.001 -0.220;<0.001

wet1_all_cnt -0.037; 0.177 -0.009; 0.731 -0.052; 0.036 -0.018; 0.478

wet2_all_cnt 0.076; 0.006 0.138;<0.001 0.211;<0.001 0.279;<0.001

wet3_all_cnt 0.021; 0.453 0.073; 0.006 0.144;<0.001 0.225;<0.001

wet4_all_cnt -0.252;<0.001 -0.278;<0.001 -0.368;<0.001 -0.284;<0.001

field_size 0.079; 0.007 0.118;<0.001 0.224;<0.001 0.262;<0.001

tro_all_cnt 0.009; 0.762 0.010; 0.734 0.039; 0.168 0.055; 0.051

dug_all_cnt -0.121;<0.001 -0.114;<0.001 -0.010; 0.713 0.040; 0.137

wet1_all_xd 0.077; 0.005 0.127;<0.001 0.218;<0.001 0.126;<0.001

wet2_all_xd -0.131;<0.001 -0.129;<0.001 -0.082;<0.001 -0.080; 0.001

wet3_all_xd -0.044; 0.108 -0.028; 0.277 0.040; 0.103 0.046; 0.064

wet4_all_xd 0.012; 0.666 0.061; 0.019 0.179;<0.001 0.215;<0.001

tro_all_xd 0.061; 0.048 0.088; 0.003 0.096;<0.001 0.132;<0.001

dug_all_xd -0.007; 0.826 -0.021; 0.477 -0.027; 0.320 0.132;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 178 / 189

Table G.14 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet2_500_if_cnt wet2_1000_if_cnt wet2_2500_if_cnt wet2_5000_if_cnt

wet1_500_if_cnt 0.222;<0.001 0.105;<0.001 0.090; 0.003 0.012; 0.698

wet1_1000_if_cnt 0.144;<0.001 0.244;<0.001 0.157;<0.001 0.104;<0.001

wet1_2500_if_cnt 0.253;<0.001 0.314;<0.001 0.410;<0.001 0.327;<0.001

wet1_5000_if_cnt 0.210;<0.001 0.320;<0.001 0.385;<0.001 0.463;<0.001

wet2_500_if_cnt 1.000;<0.001 0.466;<0.001 0.376;<0.001 0.177;<0.001

wet2_1000_if_cnt 0.466;<0.001 1.000;<0.001 0.494;<0.001 0.300;<0.001

wet2_2500_if_cnt 0.376;<0.001 0.494;<0.001 1.000;<0.001 0.460;<0.001

wet2_5000_if_cnt 0.177;<0.001 0.300;<0.001 0.460;<0.001 1.000;<0.001

wet3_500_if_cnt 0.497;<0.001 0.314;<0.001 0.259;<0.001 0.197;<0.001

wet3_1000_if_cnt 0.264;<0.001 0.469;<0.001 0.349;<0.001 0.283;<0.001

wet3_2500_if_cnt 0.176;<0.001 0.315;<0.001 0.521;<0.001 0.441;<0.001

wet3_5000_if_cnt 0.080; 0.005 0.155;<0.001 0.291;<0.001 0.433;<0.001

wet4_500_if_cnt 0.307;<0.001 0.196;<0.001 0.130;<0.001 0.093; 0.002

wet4_1000_if_cnt 0.192;<0.001 0.335;<0.001 0.201;<0.001 0.118;<0.001

wet4_2500_if_cnt 0.256;<0.001 0.329;<0.001 0.459;<0.001 0.291;<0.001

wet4_5000_if_cnt 0.089; 0.003 0.185;<0.001 0.297;<0.001 0.488;<0.001

wet1_all_if_xd -0.129;<0.001 -0.194;<0.001 -0.248;<0.001 -0.079; 0.002

wet2_all_if_xd -0.321;<0.001 -0.405;<0.001 -0.469;<0.001 -0.073; 0.005

wet3_all_if_xd -0.183;<0.001 -0.289;<0.001 -0.341;<0.001 -0.187;<0.001

wet4_all_if_xd -0.103;<0.001 -0.179;<0.001 -0.238;<0.001 -0.066; 0.010

wet1_if_density 0.199;<0.001 0.263;<0.001 0.250;<0.001 0.286;<0.001

wet2_if_density 0.320;<0.001 0.427;<0.001 0.473;<0.001 0.510;<0.001

wet3_if_density 0.081; 0.006 0.121;<0.001 0.126;<0.001 0.129;<0.001

wet4_if_density 0.140;<0.001 0.181;<0.001 0.166;<0.001 0.153;<0.001

Table G.15: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_500_if_cnt wet3_1000_if_cnt wet3_2500_if_cnt wet3_5000_if_cnt

cws_shrub -0.088; 0.002 -0.144;<0.001 -0.156;<0.001 -0.076; 0.003

mrps -0.081; 0.005 -0.064; 0.024 -0.004; 0.871 -0.037; 0.141

topo 0.094;<0.001 0.083; 0.003 -0.028; 0.284 0.016; 0.507

wet1_500kd 0.239;<0.001 0.115;<0.001 -0.006; 0.856 -0.101;<0.001

wet1_1000kd 0.237;<0.001 0.262;<0.001 0.018; 0.559 -0.109;<0.001

wet1_2500kd 0.205;<0.001 0.183;<0.001 0.162;<0.001 -0.101;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 179 / 189

Table G.15 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_500_if_cnt wet3_1000_if_cnt wet3_2500_if_cnt wet3_5000_if_cnt

wet1_5000kd 0.147;<0.001 0.151;<0.001 0.092;<0.001 -0.112;<0.001

wet2_500kd 0.355;<0.001 0.120;<0.001 0.032; 0.258 -0.068; 0.013

wet2_1000kd 0.208;<0.001 0.204;<0.001 0.053; 0.043 -0.070; 0.006

wet2_2500kd 0.036; 0.199 0.015; 0.584 0.060; 0.020 -0.077; 0.002

wet2_5000kd -0.072; 0.011 -0.081; 0.003 0.035; 0.173 0.011; 0.657

wet3_500kd 0.771;<0.001 0.359;<0.001 0.190;<0.001 0.061; 0.031

wet3_1000kd 0.517;<0.001 0.597;<0.001 0.252;<0.001 0.083; 0.002

wet3_2500kd 0.279;<0.001 0.345;<0.001 0.370;<0.001 0.140;<0.001

wet3_5000kd 0.195;<0.001 0.244;<0.001 0.224;<0.001 0.220;<0.001

wet4_500kd 0.266;<0.001 0.145;<0.001 0.068; 0.028 -0.020; 0.499

wet4_1000kd 0.285;<0.001 0.322;<0.001 0.140;<0.001 -0.037; 0.202

wet4_2500kd 0.257;<0.001 0.244;<0.001 0.154;<0.001 -0.045; 0.086

wet4_5000kd 0.189;<0.001 0.206;<0.001 0.053; 0.041 0.097;<0.001

wet1_500_cnt 0.268;<0.001 0.134;<0.001 0.004; 0.909 -0.092; 0.002

wet1_1000_cnt 0.163;<0.001 0.236;<0.001 0.012; 0.696 -0.108;<0.001

wet1_2500_cnt 0.191;<0.001 0.136;<0.001 0.165;<0.001 -0.087; 0.002

wet1_5000_cnt 0.169;<0.001 0.171;<0.001 0.066; 0.012 -0.042; 0.101

wet2_500_cnt 0.441;<0.001 0.194;<0.001 0.083; 0.004 -0.039; 0.166

wet2_1000_cnt 0.277;<0.001 0.363;<0.001 0.169;<0.001 0.003; 0.909

wet2_2500_cnt 0.223;<0.001 0.225;<0.001 0.265;<0.001 0.069; 0.006

wet2_5000_cnt 0.188;<0.001 0.224;<0.001 0.269;<0.001 0.231;<0.001

wet3_500_cnt 0.822;<0.001 0.384;<0.001 0.214;<0.001 0.073; 0.011

wet3_1000_cnt 0.392;<0.001 0.686;<0.001 0.277;<0.001 0.133;<0.001

wet3_2500_cnt 0.243;<0.001 0.272;<0.001 0.370;<0.001 0.159;<0.001

wet3_5000_cnt 0.192;<0.001 0.230;<0.001 0.219;<0.001 0.307;<0.001

wet4_500_cnt 0.282;<0.001 0.143;<0.001 0.063; 0.043 -0.029; 0.327

wet4_1000_cnt 0.214;<0.001 0.316;<0.001 0.134;<0.001 -0.031; 0.288

wet4_2500_cnt 0.240;<0.001 0.202;<0.001 0.165;<0.001 -0.033; 0.222

wet4_5000_cnt 0.122;<0.001 0.137;<0.001 -0.003; 0.910 0.069; 0.006

wet1_all_cnt 0.258;<0.001 0.308;<0.001 0.256;<0.001 0.099;<0.001

wet2_all_cnt 0.201;<0.001 0.279;<0.001 0.399;<0.001 0.329;<0.001

wet3_all_cnt 0.203;<0.001 0.257;<0.001 0.362;<0.001 0.386;<0.001

wet4_all_cnt 0.080; 0.005 0.083; 0.003 -0.035; 0.171 0.064; 0.010
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 180 / 189

Table G.15 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet3_500_if_cnt wet3_1000_if_cnt wet3_2500_if_cnt wet3_5000_if_cnt

field_size -0.122;<0.001 -0.071; 0.017 0.106;<0.001 0.122;<0.001

tro_all_cnt -0.308;<0.001 -0.363;<0.001 -0.271;<0.001 -0.298;<0.001

dug_all_cnt -0.195;<0.001 -0.122;<0.001 0.054; 0.053 0.083; 0.002

wet1_all_xd 0.013; 0.658 0.078; 0.005 0.167;<0.001 0.160;<0.001

wet2_all_xd -0.178;<0.001 -0.157;<0.001 -0.081; 0.002 -0.051; 0.040

wet3_all_xd -0.195;<0.001 -0.206;<0.001 -0.110;<0.001 -0.180;<0.001

wet4_all_xd -0.136;<0.001 -0.082; 0.003 0.078; 0.002 -0.027; 0.269

tro_all_xd 0.150;<0.001 0.194;<0.001 0.274;<0.001 0.210;<0.001

dug_all_xd -0.118;<0.001 -0.157;<0.001 -0.116;<0.001 0.188;<0.001

wet1_500_if_cnt 0.331;<0.001 0.217;<0.001 0.089; 0.004 -0.017; 0.566

wet1_1000_if_cnt 0.222;<0.001 0.356;<0.001 0.131;<0.001 0.027; 0.366

wet1_2500_if_cnt 0.296;<0.001 0.298;<0.001 0.419;<0.001 0.113;<0.001

wet1_5000_if_cnt 0.191;<0.001 0.252;<0.001 0.271;<0.001 0.378;<0.001

wet2_500_if_cnt 0.497;<0.001 0.264;<0.001 0.176;<0.001 0.080; 0.005

wet2_1000_if_cnt 0.314;<0.001 0.469;<0.001 0.315;<0.001 0.155;<0.001

wet2_2500_if_cnt 0.259;<0.001 0.349;<0.001 0.521;<0.001 0.291;<0.001

wet2_5000_if_cnt 0.197;<0.001 0.283;<0.001 0.441;<0.001 0.433;<0.001

wet3_500_if_cnt 1.000;<0.001 0.511;<0.001 0.362;<0.001 0.219;<0.001

wet3_1000_if_cnt 0.511;<0.001 1.000;<0.001 0.511;<0.001 0.309;<0.001

wet3_2500_if_cnt 0.362;<0.001 0.511;<0.001 1.000;<0.001 0.391;<0.001

wet3_5000_if_cnt 0.219;<0.001 0.309;<0.001 0.391;<0.001 1.000;<0.001

wet4_500_if_cnt 0.374;<0.001 0.225;<0.001 0.160;<0.001 0.073; 0.015

wet4_1000_if_cnt 0.309;<0.001 0.432;<0.001 0.266;<0.001 0.126;<0.001

wet4_2500_if_cnt 0.435;<0.001 0.464;<0.001 0.520;<0.001 0.235;<0.001

wet4_5000_if_cnt 0.228;<0.001 0.331;<0.001 0.392;<0.001 0.624;<0.001

wet1_all_if_xd -0.103;<0.001 -0.185;<0.001 -0.232;<0.001 -0.103;<0.001

wet2_all_if_xd -0.213;<0.001 -0.265;<0.001 -0.237;<0.001 -0.028; 0.279

wet3_all_if_xd -0.276;<0.001 -0.355;<0.001 -0.430;<0.001 0.041; 0.113

wet4_all_if_xd -0.146;<0.001 -0.199;<0.001 -0.260;<0.001 0.044; 0.092

wet1_if_density 0.259;<0.001 0.248;<0.001 0.187;<0.001 -0.019; 0.481

wet2_if_density 0.338;<0.001 0.349;<0.001 0.349;<0.001 0.196;<0.001

wet3_if_density 0.355;<0.001 0.426;<0.001 0.402;<0.001 0.569;<0.001

wet4_if_density 0.282;<0.001 0.297;<0.001 0.227;<0.001 0.295;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 181 / 189

Table G.16: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_500_if_cnt wet4_1000_if_cnt wet4_2500_if_cnt wet4_5000_if_cnt

cws_shrub 0.064; 0.033 0.034; 0.243 -0.042; 0.138 -0.087; 0.001

mrps -0.154;<0.001 -0.196;<0.001 -0.248;<0.001 -0.109;<0.001

topo 0.082; 0.005 0.106;<0.001 0.126;<0.001 0.060; 0.025

wet1_500kd 0.369;<0.001 0.133;<0.001 0.091; 0.006 -0.051; 0.112

wet1_1000kd 0.250;<0.001 0.372;<0.001 0.156;<0.001 -0.013; 0.670

wet1_2500kd 0.111;<0.001 0.161;<0.001 0.221;<0.001 -0.005; 0.853

wet1_5000kd 0.093; 0.002 0.065; 0.027 0.021; 0.455 -0.012; 0.667

wet2_500kd 0.195;<0.001 0.093; 0.004 0.112;<0.001 -0.013; 0.651

wet2_1000kd 0.107;<0.001 0.131;<0.001 0.094; 0.001 0.025; 0.363

wet2_2500kd 0.015; 0.615 0.028; 0.340 -0.027; 0.330 0.028; 0.301

wet2_5000kd -0.086; 0.003 -0.134;<0.001 -0.159;<0.001 0.001; 0.969

wet3_500kd 0.294;<0.001 0.239;<0.001 0.279;<0.001 0.068; 0.026

wet3_1000kd 0.215;<0.001 0.312;<0.001 0.257;<0.001 0.068; 0.017

wet3_2500kd 0.117;<0.001 0.190;<0.001 0.174;<0.001 0.090;<0.001

wet3_5000kd 0.025; 0.397 0.051; 0.076 -0.003; 0.912 0.162;<0.001

wet4_500kd 0.783;<0.001 0.230;<0.001 0.193;<0.001 -0.010; 0.749

wet4_1000kd 0.479;<0.001 0.663;<0.001 0.284;<0.001 -0.060; 0.055

wet4_2500kd 0.220;<0.001 0.287;<0.001 0.407;<0.001 -0.083; 0.003

wet4_5000kd 0.109;<0.001 0.143;<0.001 0.077; 0.006 0.186;<0.001

wet1_500_cnt 0.380;<0.001 0.181;<0.001 0.116;<0.001 -0.039; 0.229

wet1_1000_cnt 0.207;<0.001 0.413;<0.001 0.141;<0.001 -0.017; 0.594

wet1_2500_cnt 0.092; 0.005 0.113;<0.001 0.222;<0.001 -0.019; 0.530

wet1_5000_cnt 0.106;<0.001 0.097; 0.001 0.012; 0.671 0.045; 0.098

wet2_500_cnt 0.271;<0.001 0.151;<0.001 0.177;<0.001 -0.004; 0.884

wet2_1000_cnt 0.173;<0.001 0.289;<0.001 0.201;<0.001 0.055; 0.053

wet2_2500_cnt 0.070; 0.017 0.110;<0.001 0.148;<0.001 0.118;<0.001

wet2_5000_cnt 0.016; 0.580 0.010; 0.738 -0.005; 0.856 0.247;<0.001

wet3_500_cnt 0.303;<0.001 0.233;<0.001 0.301;<0.001 0.087; 0.005

wet3_1000_cnt 0.142;<0.001 0.306;<0.001 0.247;<0.001 0.121;<0.001

wet3_2500_cnt 0.076; 0.011 0.132;<0.001 0.140;<0.001 0.138;<0.001

wet3_5000_cnt 0.012; 0.673 0.040; 0.170 -0.004; 0.890 0.274;<0.001

wet4_500_cnt 0.836;<0.001 0.239;<0.001 0.208;<0.001 -0.024; 0.449

wet4_1000_cnt 0.224;<0.001 0.770;<0.001 0.250;<0.001 -0.039; 0.217
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 182 / 189

Table G.16 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_500_if_cnt wet4_1000_if_cnt wet4_2500_if_cnt wet4_5000_if_cnt

wet4_2500_cnt 0.168;<0.001 0.201;<0.001 0.441;<0.001 -0.078; 0.007

wet4_5000_cnt 0.054; 0.071 0.065; 0.028 -0.013; 0.652 0.157;<0.001

wet1_all_cnt 0.100;<0.001 0.160;<0.001 0.141;<0.001 0.138;<0.001

wet2_all_cnt -0.024; 0.423 0.039; 0.189 0.042; 0.134 0.251;<0.001

wet3_all_cnt -0.022; 0.456 0.040; 0.168 0.055; 0.049 0.295;<0.001

wet4_all_cnt 0.074; 0.011 0.119;<0.001 0.105;<0.001 0.011; 0.673

field_size -0.109;<0.001 -0.120;<0.001 -0.092; 0.002 -0.084; 0.003

tro_all_cnt -0.152;<0.001 -0.228;<0.001 -0.199;<0.001 -0.158;<0.001

dug_all_cnt -0.201;<0.001 -0.192;<0.001 -0.245;<0.001 -0.132;<0.001

wet1_all_xd -0.053; 0.070 0.023; 0.436 0.144;<0.001 0.107;<0.001

wet2_all_xd -0.066; 0.023 -0.057; 0.050 0.013; 0.628 -0.127;<0.001

wet3_all_xd -0.064; 0.029 -0.078; 0.007 0.015; 0.597 -0.168;<0.001

wet4_all_xd -0.123;<0.001 -0.106;<0.001 -0.011; 0.696 -0.041; 0.124

tro_all_xd -0.006; 0.853 0.005; 0.876 -0.046; 0.147 0.092; 0.002

dug_all_xd -0.073; 0.025 -0.096; 0.003 -0.131;<0.001 0.096; 0.001

wet1_500_if_cnt 0.501;<0.001 0.236;<0.001 0.200;<0.001 0.032; 0.326

wet1_1000_if_cnt 0.225;<0.001 0.532;<0.001 0.235;<0.001 0.100; 0.002

wet1_2500_if_cnt 0.195;<0.001 0.228;<0.001 0.587;<0.001 0.195;<0.001

wet1_5000_if_cnt 0.184;<0.001 0.150;<0.001 0.267;<0.001 0.495;<0.001

wet2_500_if_cnt 0.307;<0.001 0.192;<0.001 0.256;<0.001 0.089; 0.003

wet2_1000_if_cnt 0.196;<0.001 0.335;<0.001 0.329;<0.001 0.185;<0.001

wet2_2500_if_cnt 0.130;<0.001 0.201;<0.001 0.459;<0.001 0.297;<0.001

wet2_5000_if_cnt 0.093; 0.002 0.118;<0.001 0.291;<0.001 0.488;<0.001

wet3_500_if_cnt 0.374;<0.001 0.309;<0.001 0.435;<0.001 0.228;<0.001

wet3_1000_if_cnt 0.225;<0.001 0.432;<0.001 0.464;<0.001 0.331;<0.001

wet3_2500_if_cnt 0.160;<0.001 0.266;<0.001 0.520;<0.001 0.392;<0.001

wet3_5000_if_cnt 0.073; 0.015 0.126;<0.001 0.235;<0.001 0.624;<0.001

wet4_500_if_cnt 1.000;<0.001 0.309;<0.001 0.302;<0.001 0.057; 0.076

wet4_1000_if_cnt 0.309;<0.001 1.000;<0.001 0.365;<0.001 0.086; 0.007

wet4_2500_if_cnt 0.302;<0.001 0.365;<0.001 1.000;<0.001 0.162;<0.001

wet4_5000_if_cnt 0.057; 0.076 0.086; 0.007 0.162;<0.001 1.000;<0.001

wet1_all_if_xd -0.058; 0.061 -0.122;<0.001 -0.224;<0.001 -0.078; 0.006

wet2_all_if_xd -0.078; 0.011 -0.173;<0.001 -0.244;<0.001 -0.080; 0.004
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 183 / 189

Table G.16 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet4_500_if_cnt wet4_1000_if_cnt wet4_2500_if_cnt wet4_5000_if_cnt

wet3_all_if_xd -0.122;<0.001 -0.204;<0.001 -0.334;<0.001 -0.031; 0.260

wet4_all_if_xd -0.148;<0.001 -0.211;<0.001 -0.363;<0.001 0.088; 0.002

wet1_if_density 0.246;<0.001 0.264;<0.001 0.397;<0.001 0.224;<0.001

wet2_if_density 0.243;<0.001 0.273;<0.001 0.445;<0.001 0.382;<0.001

wet3_if_density 0.160;<0.001 0.242;<0.001 0.284;<0.001 0.406;<0.001

wet4_if_density 0.216;<0.001 0.264;<0.001 0.361;<0.001 0.441;<0.001

Table G.17: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_all_if_xd wet2_all_if_xd wet3_all_if_xd wet4_all_if_xd

cws_shrub 0.124;<0.001 0.044; 0.089 0.103;<0.001 0.093;<0.001

mrps -0.060; 0.021 0.053; 0.041 -0.036; 0.168 -0.045; 0.084

topo 0.100;<0.001 0.060; 0.018 0.062; 0.015 0.079; 0.002

wet1_500kd -0.094; 0.003 -0.136;<0.001 -0.143;<0.001 -0.121;<0.001

wet1_1000kd -0.181;<0.001 -0.205;<0.001 -0.204;<0.001 -0.183;<0.001

wet1_2500kd -0.010; 0.716 -0.147;<0.001 -0.205;<0.001 -0.068; 0.014

wet1_5000kd 0.322;<0.001 -0.033; 0.205 -0.078; 0.003 0.144;<0.001

wet2_500kd -0.037; 0.186 -0.242;<0.001 -0.135;<0.001 -0.027; 0.331

wet2_1000kd -0.025; 0.338 -0.239;<0.001 -0.165;<0.001 -0.012; 0.638

wet2_2500kd 0.054; 0.034 -0.149;<0.001 -0.138;<0.001 0.054; 0.034

wet2_5000kd 0.080; 0.002 -0.083; 0.001 -0.124;<0.001 0.083; 0.001

wet3_500kd 0.040; 0.172 -0.118;<0.001 -0.172;<0.001 -0.036; 0.226

wet3_1000kd 0.049; 0.077 -0.102;<0.001 -0.187;<0.001 0.001; 0.981

wet3_2500kd 0.153;<0.001 -0.059; 0.022 -0.131;<0.001 0.117;<0.001

wet3_5000kd 0.255;<0.001 -0.019; 0.451 0.013; 0.612 0.288;<0.001

wet4_500kd 0.023; 0.453 -0.029; 0.344 -0.062; 0.044 -0.069; 0.024

wet4_1000kd -0.002; 0.953 -0.131;<0.001 -0.177;<0.001 -0.152;<0.001

wet4_2500kd 0.175;<0.001 -0.047; 0.082 -0.075; 0.006 -0.016; 0.555

wet4_5000kd 0.263;<0.001 -0.047; 0.067 0.123;<0.001 0.311;<0.001

wet1_500_cnt -0.103;<0.001 -0.136;<0.001 -0.142;<0.001 -0.129;<0.001

wet1_1000_cnt -0.164;<0.001 -0.183;<0.001 -0.174;<0.001 -0.166;<0.001

wet1_2500_cnt 0.049; 0.087 -0.093; 0.001 -0.135;<0.001 -0.027; 0.347

wet1_5000_cnt 0.374;<0.001 0.018; 0.483 0.008; 0.749 0.231;<0.001

wet2_500_cnt -0.015; 0.593 -0.265;<0.001 -0.140;<0.001 -0.032; 0.260
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 184 / 189

Table G.17 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_all_if_xd wet2_all_if_xd wet3_all_if_xd wet4_all_if_xd

wet2_1000_cnt -0.000; 0.990 -0.274;<0.001 -0.171;<0.001 -0.023; 0.385

wet2_2500_cnt 0.217;<0.001 -0.128;<0.001 -0.083; 0.001 0.191;<0.001

wet2_5000_cnt 0.272;<0.001 0.008; 0.766 -0.035; 0.175 0.297;<0.001

wet3_500_cnt 0.052; 0.082 -0.123;<0.001 -0.175;<0.001 -0.027; 0.356

wet3_1000_cnt 0.089; 0.002 -0.088; 0.002 -0.146;<0.001 0.045; 0.107

wet3_2500_cnt 0.258;<0.001 -0.022; 0.388 -0.077; 0.003 0.198;<0.001

wet3_5000_cnt 0.240;<0.001 -0.024; 0.349 0.083; 0.001 0.358;<0.001

wet4_500_cnt 0.013; 0.667 -0.036; 0.247 -0.078; 0.011 -0.093; 0.003

wet4_1000_cnt -0.020; 0.513 -0.137;<0.001 -0.161;<0.001 -0.122;<0.001

wet4_2500_cnt 0.155;<0.001 -0.054; 0.049 -0.094;<0.001 -0.027; 0.324

wet4_5000_cnt 0.243;<0.001 -0.063; 0.016 0.089;<0.001 0.288;<0.001

wet1_all_cnt 0.207;<0.001 -0.031; 0.231 -0.053; 0.038 0.175;<0.001

wet2_all_cnt 0.137;<0.001 -0.007; 0.785 -0.046; 0.077 0.198;<0.001

wet3_all_cnt 0.159;<0.001 0.036; 0.160 0.026; 0.308 0.243;<0.001

wet4_all_cnt 0.140;<0.001 0.134;<0.001 0.129;<0.001 0.088;<0.001

field_size -0.220;<0.001 0.115;<0.001 -0.104;<0.001 -0.234;<0.001

tro_all_cnt 0.012; 0.685 0.052; 0.080 0.002; 0.948 -0.118;<0.001

dug_all_cnt -0.174;<0.001 0.106;<0.001 -0.066; 0.019 -0.199;<0.001

wet1_all_xd -0.308;<0.001 -0.047; 0.062 -0.024; 0.350 -0.095;<0.001

wet2_all_xd -0.245;<0.001 0.130;<0.001 0.025; 0.318 -0.284;<0.001

wet3_all_xd -0.275;<0.001 0.032; 0.214 -0.073; 0.004 -0.333;<0.001

wet4_all_xd -0.350;<0.001 0.031; 0.229 -0.150;<0.001 -0.262;<0.001

tro_all_xd 0.174;<0.001 0.084; 0.003 -0.072; 0.011 0.080; 0.005

dug_all_xd 0.158;<0.001 0.464;<0.001 0.390;<0.001 0.138;<0.001

wet1_500_if_cnt -0.185;<0.001 -0.157;<0.001 -0.161;<0.001 -0.164;<0.001

wet1_1000_if_cnt -0.254;<0.001 -0.212;<0.001 -0.201;<0.001 -0.216;<0.001

wet1_2500_if_cnt -0.353;<0.001 -0.273;<0.001 -0.302;<0.001 -0.297;<0.001

wet1_5000_if_cnt 0.074; 0.012 0.030; 0.303 -0.005; 0.857 -0.013; 0.658

wet2_500_if_cnt -0.129;<0.001 -0.321;<0.001 -0.183;<0.001 -0.103;<0.001

wet2_1000_if_cnt -0.194;<0.001 -0.405;<0.001 -0.289;<0.001 -0.179;<0.001

wet2_2500_if_cnt -0.248;<0.001 -0.469;<0.001 -0.341;<0.001 -0.238;<0.001

wet2_5000_if_cnt -0.079; 0.002 -0.073; 0.005 -0.187;<0.001 -0.066; 0.010

wet3_500_if_cnt -0.103;<0.001 -0.213;<0.001 -0.276;<0.001 -0.146;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 185 / 189

Table G.17 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_all_if_xd wet2_all_if_xd wet3_all_if_xd wet4_all_if_xd

wet3_1000_if_cnt -0.185;<0.001 -0.265;<0.001 -0.355;<0.001 -0.199;<0.001

wet3_2500_if_cnt -0.232;<0.001 -0.237;<0.001 -0.430;<0.001 -0.260;<0.001

wet3_5000_if_cnt -0.103;<0.001 -0.028; 0.279 0.041; 0.113 0.044; 0.092

wet4_500_if_cnt -0.058; 0.061 -0.078; 0.011 -0.122;<0.001 -0.148;<0.001

wet4_1000_if_cnt -0.122;<0.001 -0.173;<0.001 -0.204;<0.001 -0.211;<0.001

wet4_2500_if_cnt -0.224;<0.001 -0.244;<0.001 -0.334;<0.001 -0.363;<0.001

wet4_5000_if_cnt -0.078; 0.006 -0.080; 0.004 -0.031; 0.260 0.088; 0.002

wet1_all_if_xd 1.000;<0.001 0.369;<0.001 0.350;<0.001 0.571;<0.001

wet2_all_if_xd 0.369;<0.001 1.000;<0.001 0.467;<0.001 0.294;<0.001

wet3_all_if_xd 0.350;<0.001 0.467;<0.001 1.000;<0.001 0.563;<0.001

wet4_all_if_xd 0.571;<0.001 0.294;<0.001 0.563;<0.001 1.000;<0.001

wet1_if_density 0.152;<0.001 -0.006; 0.838 -0.080; 0.004 -0.020; 0.463

wet2_if_density -0.066; 0.018 -0.122;<0.001 -0.196;<0.001 -0.102;<0.001

wet3_if_density -0.101;<0.001 -0.069; 0.012 0.028; 0.314 0.129;<0.001

wet4_if_density -0.055; 0.051 -0.078; 0.005 0.107;<0.001 0.204;<0.001

Table G.18: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_if_density wet2_if_density wet3_if_density wet4_if_density

cws_shrub 0.051; 0.056 -0.001; 0.976 -0.029; 0.267 0.132;<0.001

mrps -0.334;<0.001 -0.320;<0.001 -0.022; 0.415 -0.227;<0.001

topo 0.074; 0.004 -0.034; 0.194 0.156;<0.001 0.191;<0.001

wet1_500kd 0.103; 0.001 0.026; 0.411 0.012; 0.713 0.065; 0.038

wet1_1000kd 0.157;<0.001 0.059; 0.056 -0.005; 0.881 0.096; 0.002

wet1_2500kd 0.226;<0.001 0.090; 0.001 -0.036; 0.204 0.018; 0.527

wet1_5000kd 0.217;<0.001 0.012; 0.655 0.016; 0.558 -0.036; 0.172

wet2_500kd 0.159;<0.001 0.196;<0.001 -0.061; 0.033 0.042; 0.142

wet2_1000kd 0.203;<0.001 0.272;<0.001 -0.093;<0.001 0.042; 0.120

wet2_2500kd 0.127;<0.001 0.235;<0.001 -0.143;<0.001 -0.023; 0.383

wet2_5000kd -0.072; 0.006 0.119;<0.001 -0.179;<0.001 -0.163;<0.001

wet3_500kd 0.203;<0.001 0.186;<0.001 0.232;<0.001 0.172;<0.001

wet3_1000kd 0.188;<0.001 0.169;<0.001 0.272;<0.001 0.165;<0.001

wet3_2500kd 0.076; 0.004 0.056; 0.031 0.332;<0.001 0.147;<0.001

wet3_5000kd -0.071; 0.007 -0.055; 0.034 0.390;<0.001 0.177;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 186 / 189

Table G.18 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_if_density wet2_if_density wet3_if_density wet4_if_density

wet4_500kd 0.175;<0.001 0.091; 0.004 0.068; 0.030 0.128;<0.001

wet4_1000kd 0.226;<0.001 0.146;<0.001 0.106;<0.001 0.166;<0.001

wet4_2500kd 0.293;<0.001 0.110;<0.001 0.168;<0.001 0.211;<0.001

wet4_5000kd 0.170;<0.001 -0.041; 0.114 0.353;<0.001 0.356;<0.001

wet1_500_cnt 0.125;<0.001 0.048; 0.123 0.033; 0.288 0.088; 0.005

wet1_1000_cnt 0.157;<0.001 0.058; 0.063 0.003; 0.921 0.105;<0.001

wet1_2500_cnt 0.213;<0.001 0.044; 0.129 0.008; 0.770 0.025; 0.393

wet1_5000_cnt 0.225;<0.001 -0.011; 0.676 0.147;<0.001 0.089;<0.001

wet2_500_cnt 0.189;<0.001 0.208;<0.001 -0.003; 0.908 0.076; 0.009

wet2_1000_cnt 0.250;<0.001 0.293;<0.001 0.054; 0.048 0.130;<0.001

wet2_2500_cnt 0.226;<0.001 0.214;<0.001 0.127;<0.001 0.113;<0.001

wet2_5000_cnt 0.134;<0.001 0.173;<0.001 0.265;<0.001 0.135;<0.001

wet3_500_cnt 0.224;<0.001 0.205;<0.001 0.254;<0.001 0.189;<0.001

wet3_1000_cnt 0.194;<0.001 0.175;<0.001 0.301;<0.001 0.185;<0.001

wet3_2500_cnt 0.133;<0.001 0.086; 0.001 0.315;<0.001 0.151;<0.001

wet3_5000_cnt 0.019; 0.465 0.074; 0.004 0.471;<0.001 0.301;<0.001

wet4_500_cnt 0.186;<0.001 0.105;<0.001 0.063; 0.043 0.133;<0.001

wet4_1000_cnt 0.199;<0.001 0.131;<0.001 0.121;<0.001 0.168;<0.001

wet4_2500_cnt 0.265;<0.001 0.073; 0.009 0.142;<0.001 0.171;<0.001

wet4_5000_cnt 0.076; 0.004 -0.162;<0.001 0.285;<0.001 0.273;<0.001

wet1_all_cnt 0.239;<0.001 0.046; 0.077 0.359;<0.001 0.145;<0.001

wet2_all_cnt 0.019; 0.468 0.149;<0.001 0.356;<0.001 0.075; 0.004

wet3_all_cnt 0.023; 0.379 0.149;<0.001 0.456;<0.001 0.178;<0.001

wet4_all_cnt 0.060; 0.021 -0.139;<0.001 0.314;<0.001 0.219;<0.001

field_size -0.224;<0.001 -0.060; 0.030 -0.145;<0.001 -0.524;<0.001

tro_all_cnt 0.170;<0.001 0.014; 0.644 -0.641;<0.001 -0.318;<0.001

dug_all_cnt -0.562;<0.001 -0.391;<0.001 -0.198;<0.001 -0.579;<0.001

wet1_all_xd -0.045; 0.083 0.140;<0.001 0.164;<0.001 0.151;<0.001

wet2_all_xd -0.190;<0.001 -0.154;<0.001 -0.103;<0.001 -0.154;<0.001

wet3_all_xd -0.038; 0.143 -0.003; 0.923 -0.346;<0.001 -0.258;<0.001

wet4_all_xd -0.082; 0.002 0.121;<0.001 -0.177;<0.001 -0.184;<0.001

tro_all_xd -0.128;<0.001 0.017; 0.579 0.223;<0.001 -0.064; 0.034

dug_all_xd 0.021; 0.472 0.167;<0.001 -0.006; 0.836 -0.199;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 187 / 189

Table G.18 continued: Kendall wetland predictor correlations (Kendall's tau & p-value shown)

Predictors wet1_if_density wet2_if_density wet3_if_density wet4_if_density

wet1_500_if_cnt 0.182;<0.001 0.166;<0.001 0.128;<0.001 0.172;<0.001

wet1_1000_if_cnt 0.237;<0.001 0.227;<0.001 0.138;<0.001 0.198;<0.001

wet1_2500_if_cnt 0.370;<0.001 0.417;<0.001 0.164;<0.001 0.265;<0.001

wet1_5000_if_cnt 0.415;<0.001 0.515;<0.001 0.112;<0.001 0.209;<0.001

wet2_500_if_cnt 0.199;<0.001 0.320;<0.001 0.081; 0.006 0.140;<0.001

wet2_1000_if_cnt 0.263;<0.001 0.427;<0.001 0.121;<0.001 0.181;<0.001

wet2_2500_if_cnt 0.250;<0.001 0.473;<0.001 0.126;<0.001 0.166;<0.001

wet2_5000_if_cnt 0.286;<0.001 0.510;<0.001 0.129;<0.001 0.153;<0.001

wet3_500_if_cnt 0.259;<0.001 0.338;<0.001 0.355;<0.001 0.282;<0.001

wet3_1000_if_cnt 0.248;<0.001 0.349;<0.001 0.426;<0.001 0.297;<0.001

wet3_2500_if_cnt 0.187;<0.001 0.349;<0.001 0.402;<0.001 0.227;<0.001

wet3_5000_if_cnt -0.019; 0.481 0.196;<0.001 0.569;<0.001 0.295;<0.001

wet4_500_if_cnt 0.246;<0.001 0.243;<0.001 0.160;<0.001 0.216;<0.001

wet4_1000_if_cnt 0.264;<0.001 0.273;<0.001 0.242;<0.001 0.264;<0.001

wet4_2500_if_cnt 0.397;<0.001 0.445;<0.001 0.284;<0.001 0.361;<0.001

wet4_5000_if_cnt 0.224;<0.001 0.382;<0.001 0.406;<0.001 0.441;<0.001

wet1_all_if_xd 0.152;<0.001 -0.066; 0.018 -0.101;<0.001 -0.055; 0.051

wet2_all_if_xd -0.006; 0.838 -0.122;<0.001 -0.069; 0.012 -0.078; 0.005

wet3_all_if_xd -0.080; 0.004 -0.196;<0.001 0.028; 0.314 0.107;<0.001

wet4_all_if_xd -0.020; 0.463 -0.102;<0.001 0.129;<0.001 0.204;<0.001

wet1_if_density 1.000;<0.001 0.674;<0.001 0.068; 0.014 0.403;<0.001

wet2_if_density 0.674;<0.001 1.000;<0.001 0.228;<0.001 0.435;<0.001

wet3_if_density 0.068; 0.014 0.228;<0.001 1.000;<0.001 0.521;<0.001

wet4_if_density 0.403;<0.001 0.435;<0.001 0.521;<0.001 1.000;<0.001
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

Commission for Environmental Cooperation 188 / 189

Appendix H: Correlations between Wetland and Fire & Grazing Predictors 

Table H.1: Correlations between Wetland and Fire & Grazing Variables*

Predictors Horse old Cattle old Total burns Years since fire Fire index All pellets

cws_shrub 0.022; 0.764 -0.224;<0.001 -0.091; 0.208 -0.058; 0.380 -0.076; 0.267 -0.219;<0.001

mrps -0.142; 0.048 0.178; 0.004 -0.172; 0.016 0.201; 0.002 -0.214; 0.002 0.179; 0.003

topo 0.039; 0.584 -0.334;<0.001 -0.050; 0.476 -0.013; 0.843 0.007; 0.917 -0.338;<0.001

wet1_500kd -0.057; 0.502 -0.125; 0.082 -0.109; 0.200 0.037; 0.638 -0.106; 0.191 -0.131; 0.068

wet1_1000kd -0.030; 0.723 -0.138; 0.050 -0.039; 0.641 0.007; 0.928 -0.021; 0.790 -0.143; 0.042

wet1_2500kd 0.053; 0.474 -0.098; 0.118 -0.105; 0.154 0.062; 0.357 -0.074; 0.291 -0.098; 0.116

wet1_5000kd 0.010; 0.892 -0.146; 0.015 -0.015; 0.829 0.024; 0.711 -0.014; 0.841 -0.145; 0.016

wet2_500kd 0.034; 0.666 0.054; 0.421 -0.085; 0.280 0.057; 0.431 -0.040; 0.593 0.050; 0.457

wet2_1000kd 0.107; 0.147 -0.009; 0.882 0.067; 0.360 -0.073; 0.278 0.128; 0.067 -0.012; 0.848

wet2_2500kd 0.186; 0.009 -0.056; 0.354 0.179; 0.012 -0.156; 0.016 0.188; 0.005 -0.059; 0.329

wet2_5000kd 0.232; 0.001 -0.027; 0.652 0.464;<0.001 -0.386;<0.001 0.471;<0.001 -0.020; 0.743

wet3_500kd 0.017; 0.827 -0.017; 0.797 -0.036; 0.641 0.046; 0.517 0.000; 1.000 -0.023; 0.724

wet3_1000kd 0.084; 0.248 -0.040; 0.513 0.091; 0.211 -0.056; 0.397 0.134; 0.052 -0.044; 0.474

wet3_2500kd 0.264;<0.001 -0.027; 0.649 0.313;<0.001 -0.294;<0.001 0.352;<0.001 -0.020; 0.740

wet3_5000kd 0.224; 0.002 -0.051; 0.398 0.460;<0.001 -0.388;<0.001 0.477;<0.001 -0.046; 0.439

wet4_500kd -0.083; 0.325 0.051; 0.472 0.028; 0.741 -0.030; 0.697 0.035; 0.660 0.045; 0.526

wet4_1000kd 0.029; 0.712 0.126; 0.059 0.066; 0.401 -0.070; 0.332 0.093; 0.213 0.124; 0.062

wet4_2500kd 0.017; 0.811 -0.015; 0.801 0.023; 0.745 0.012; 0.850 0.038; 0.570 -0.011; 0.859

wet4_5000kd -0.067; 0.346 -0.298;<0.001 0.156; 0.027 -0.102; 0.115 0.131; 0.051 -0.300;<0.001

wet1_500_cnt 0.065; 0.448 -0.134; 0.064 -0.122; 0.151 0.035; 0.654 -0.117; 0.145 -0.136; 0.059

wet1_1000_cnt -0.097; 0.244 -0.116; 0.101 -0.040; 0.631 0.025; 0.741 -0.019; 0.809 -0.123; 0.082

wet1_2500_cnt 0.045; 0.550 -0.074; 0.237 -0.120; 0.106 0.099; 0.147 -0.102; 0.147 -0.075; 0.230

wet1_5000_cnt -0.008; 0.909 -0.178; 0.003 -0.065; 0.363 0.031; 0.633 -0.037; 0.581 -0.176; 0.004

wet2_500_cnt 0.045; 0.574 0.043; 0.524 -0.067; 0.395 0.042; 0.565 -0.022; 0.770 0.038; 0.571

wet2_1000_cnt 0.109; 0.146 -0.034; 0.590 0.102; 0.172 -0.085; 0.213 0.155; 0.028 -0.036; 0.564

wet2_2500_cnt 0.231; 0.001 -0.026; 0.673 0.290;<0.001 -0.203; 0.002 0.294;<0.001 -0.021; 0.730

wet2_5000_cnt 0.196; 0.006 -0.003; 0.964 0.495;<0.001 -0.401;<0.001 0.508;<0.001 0.001; 0.984

wet3_500_cnt 0.012; 0.878 -0.002; 0.976 0.009; 0.910 0.014; 0.846 0.045; 0.550 -0.008; 0.902

wet3_1000_cnt 0.135; 0.071 0.012; 0.853 0.216; 0.004 -0.139; 0.042 0.249;<0.001 0.010; 0.868

wet3_2500_cnt 0.196; 0.006 0.001; 0.986 0.323;<0.001 -0.244;<0.001 0.327;<0.001 0.003; 0.956

* Kendall's tau & p-value shown
Analysis of the effects of fire, grazing, and the distance to wetlands on grassland birds abundance

 Commission for Environmental Cooperation 189 / 189

Table H1 continued: Correlations between Wetland and Fire & Grazing Variables*

Predictors Horse old Cattle old Total burns Years since fire Fire index All pellets

wet3_5000_cnt 0.218; 0.002 0.087; 0.151 0.437;<0.001 -0.346;<0.001 0.454;<0.001 0.091; 0.130

wet4_500_cnt -0.086; 0.310 0.036; 0.609 0.015; 0.859 -0.041; 0.595 0.025; 0.756 0.030; 0.672

wet4_1000_cnt 0.052; 0.520 0.125; 0.067 0.023; 0.775 -0.058; 0.432 0.068; 0.371 0.127; 0.063

wet4_2500_cnt 0.059; 0.418 -0.089; 0.151 -0.147; 0.043 0.122; 0.068 -0.087; 0.208 -0.088; 0.155

wet4_5000_cnt -0.082; 0.250 -0.185; 0.002 0.004; 0.959 -0.004; 0.951 -0.000; 0.998 -0.190; 0.002

wet1_all_cnt 0.138; 0.052 -0.365;<0.001 0.296;<0.001 -0.318;<0.001 0.356;<0.001 -0.361;<0.001

wet2_all_cnt 0.185; 0.011 0.133; 0.032 0.408;<0.001 -0.345;<0.001 0.416;<0.001 0.135; 0.029

wet3_all_cnt 0.153; 0.033 0.200;<0.001 0.301;<0.001 -0.238;<0.001 0.294;<0.001 0.204;<0.001

wet4_all_cnt -0.180; 0.012 -0.077; 0.203 -0.203; 0.004 0.132; 0.043 -0.235;<0.001 -0.074; 0.220

tro_all_cnt -0.162; 0.046 0.363;<0.001 -0.429;<0.001 0.472;<0.001 -0.445;<0.001 0.355;<0.001

dug_all_cnt -0.126; 0.119 0.430;<0.001 -0.301;<0.001 0.206; 0.005 -0.325;<0.001 0.425;<0.001

wet1_all_xd -0.004; 0.952 0.125; 0.037 0.159; 0.024 -0.120; 0.064 0.136; 0.043 0.125; 0.036

wet2_all_xd -0.213; 0.003 -0.020; 0.744 -0.294;<0.001 0.244;<0.001 -0.319;<0.001 -0.018; 0.761

wet3_all_xd -0.221; 0.002 -0.028; 0.641 -0.387;<0.001 0.307;<0.001 -0.405;<0.001 -0.027; 0.648

wet4_all_xd -0.012; 0.867 0.154; 0.010 0.057; 0.422 -0.043; 0.505 0.014; 0.831 0.160; 0.008

tro_all_xd 0.142; 0.140 0.041; 0.614 0.409;<0.001 -0.349;<0.001 0.402;<0.001 0.045; 0.578

dug_all_xd 0.160; 0.097 0.113; 0.163 -0.009; 0.924 -0.079; 0.390 -0.014; 0.883 0.118; 0.143

field_size -0.005; 0.951 0.238;<0.001 0.092; 0.237 -0.059; 0.405 0.069; 0.348 0.233;<0.001

* Kendall's tau & p-value shown