DOI: 10.5061/dryad.s7h44j13b
title: Structuring of plant communities across agricultural landscape mosaics: The importance of connectivity and the scale of effect

description: Background: Plant communities of fragmented agricultural landscapes, are
 subject to patch isolation and scale-dependent effects. Variation in
 configuration, composition, and distance from one another affect
 biological processes of disturbance, productivity, and the movement
 ecology of species. However, connectivity and spatial structuring among
 these diverse communities are rarely considered together in the
 investigation of biological processes. Spatially optimised predictor
 variables that are based on informed measures of connectivity among
 communities, offer a solution to untangling multiple processes that drive
 biodiversity. Results: To address the gap between theory and practice, a
 novel spatial optimisation method that incorporates hypotheses of
 community connectivity, was used to estimate the scale of effect of biotic
 and abiotic factors that distinguish plant communities. We tested: i)
 whether different hypotheses of connectivity among sites was important to
 measuring diversity and environmental variation among plant communities;
 and ii) whether spatially optimised variables of species relative
 abundance and the abiotic environment among communities were consistent
 with diversity parameters in distinguishing four habitat types; namely
 Crop, Edge, Oak, and Wasteland. The global estimates of spatial
 autocorrelation, which did not consider environmental variation among
 sites, indicated significant positive autocorrelation under four
 hypotheses of landscape connectivity. The spatially optimised approach
 indicated significant positive and negative autocorrelation of species
 relative abundance at fine and broad scales, which depended on the measure
 of connectivity and environmental variation among sites. Conclusions:
 These findings showed that variation in community diversity parameters
 does not necessarily correspond to underlying spatial structuring of
 species relative abundance. The technique used to generate is extendible
 to incorporate multiple variables of interest along with a priori
 hypotheses of landscape connectivity. spatially-optimised variables with
 appropriate definitions of connectivity might be better than diversity
 parameters in explaining functional differences among communities.
description: We performed this study between July 2015 and June 2017 in the
 Vega del Tajo-Tajuña agricultural region of the Tagus River Basin, in the
 South-Central Plateau of the Iberian Peninsula (Fig. 1). We conducted 78
 individual collections that included 23 sampling sites. Four sites each of
 Oak (<i>n </i>= 4 sites x 4 re-samples each) and Wasteland
 (<i>n </i>= 4 sites x 4 re-samples each) were visited with
 collections made in autumn and spring over two growing seasons. Edge
 (<i>n </i>= 2 sites x 6 re-samples + 2 sites x 5 re-samples)
 and Crop (<i>n </i>= 7 sites x 2 re-samples + 3 sites x 3
 re-samples + 1 site x 1 re-sample) with four and eleven sites
 respectively, were visited in spring, summer, autumn. Eleven sites were
 chosen to better characterise the variation expected from the Crop
 communities as cultivated fields are subject to crop rotation and fallow
 periods. Crop collections comprised 4 fields of <i>Cucumis melo
 </i>(melon), 2 of <i>Zea mays </i>(maize), 2 of
 <i>Brassica oleracea </i>(cabbage and cauliflower), and 3 of
 <i>Hordeum vulgare</i> (barley), i.e., the major summer or
 winter crops in the area. In Oak and Wasteland sites, 25 m x 25 m quadrats
 were marked out and 150 samples per site systematically collected at each
 resampling. In Edge and Crop, 50 samples from a 25 m x 2 m area at each
 site were collected at each resampling. A boustrophedonic transect method
 (a line taken alternately from right to left and from left to right, and
 so on) was used in all instances except for Edge that have highly linear
 configurations. Depending on the habit of the species, a number of leaves
 from different parts of the individual were collected, each collection of
 leaves from the individual representing a single sample. The samples were
 harvested at fixed points along the transect. Individuals of each plant
 species were preserved at each collection and specimens assigned a
 provisional species, genus, or family rank prior to consultation with an
 herbarium for taxonomic assignments. The identifications were undertaken
 by Dr. Rosario Gavilán.
description: <b>Structuring of plant communities across agricultural
 landscape mosaics: the importance of connectivity and the scale of
 effect</b> Michael
 McLeish<sup>1*</sup>, Adrián Peláez<sup>1</sup>,
 Israel Pagán<sup>1</sup>, Rosario
 Gavilán<sup>2</sup>, Aurora Fraile<sup>1</sup>,
 Fernando García-Arenal<sup>1</sup>.
 <sup>1</sup>Centro de Biotecnología y Genómica de
 Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto
 nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and
 E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo,
 UPM, 28223 Pozuelo de Alarcón, Madrid, Spain.
 <sup>2</sup>Unidad de Botánica. Departamento de
 Farmacología, Farmacognosia y Botánica, Facultad de Farmacia, Universidad
 Complutense, Madrid, Spain.
 <b>Principal
 investigator:</b> Email:
 fernando.garciaarenal@upm.es <b>Date
 and geographic location of data collection:</b>
 We performed this study between July 2015 and June 2017 in the
 Vega del Tajo-Tajuña agricultural region of the Tagus River Basin, in the
 South-Central Plateau of the Iberian Peninsula. We conducted 78 individual
 collections that included 23 sampling sites, which comprised communities
 of four habitats with distinct cover types. The four habitat categories
 were nominated <i>a priori</i> to represent dominant
 land-cover types in the ecosystem and were distinguished by expert
 knowledge of community composition gained over twenty years of research in
 the region. We chose plant communities at sites present in forest (Oak),
 successional scrubland (Wasteland), and at the borders (Edge) between
 crops (Crop) to represent habitat categories.
 <b>Description of methods for data
 collection:</b> Four sites each of Oak
 (<i>n </i>= 4 sites x 4 re-samples each) and Wasteland
 (<i>n </i>= 4 sites x 4 re-samples each) were visited with
 collections made in autumn and spring over two growing seasons. Edge
 (<i>n </i>= 2 sites x 6 re-samples + 2 sites x 5 re-samples)
 and Crop (<i>n </i>= 7 sites x 2 re-samples + 3 sites x 3
 re-samples + 1 site x 1 re-sample) with four and eleven sites
 respectively, were visited in spring, summer, autumn. Eleven sites were
 chosen to better characterise the variation expected from the Crop
 communities as cultivated fields are subject to crop rotation and fallow
 periods. Oak sites supported expansive assemblies and required a
 relatively large sample size to account for patch heterogeneity and rare
 (low frequency) species. Similarly, Wasteland sites were also subject to
 patch heterogeneity but in a smaller area than Oak. Crop collections
 comprised 4 fields of <i>Cucumis melo </i>(melon), 2 of
 <i>Zea mays </i>(maize), 2 of <i>Brassica oleracea
 </i>(cabbage and cauliflower), and 3 of <i>Hordeum
 vulgare</i> (barley), i.e., the major summer or winter crops in the
 area. In Oak and Wasteland sites, 25 m x 25 m quadrats were marked out and
 150 samples per site systematically collected at each resampling. In Edge
 and Crop, 50 samples from a 25 m x 2 m area at each site were collected at
 each resampling. A boustrophedonic transect method (a line taken
 alternately from right to left and from left to right, and so on) was used
 in all instances except for Edge that have highly linear configurations.
 Depending on the habit of the species, a number of leaves from different
 parts of the individual were collected, each collection of leaves from the
 individual representing a single sample. The samples were harvested at
 fixed points along the transect. Individuals of each plant species were
 preserved at each collection and specimens assigned a provisional species,
 genus, or family rank prior to consultation with an herbarium for
 taxonomic assignments. The identifications were undertaken by Dr. Rosario
 Gavilán. The voucher specimens are available on request with permission
 from the authors. <b>Description of methods used
 for data processing:</b> <b>Filename:
 </b>McLeish_etal_Abundance_Dryad.csv (Created: 22 October
 2019). These data were collected during the study and
 comprise species relative abundance counts for each site and habitat of
 the study. The data were used in the detrended correspondence analysis
 (DCA) to visualise the homogeneity of relative species abundance estimates
 among the collections (<i>n </i>= 78) made in each habitat
 category. The data were also used in the computation of the extrapolated
 estimate of Sannon’s diverisity
 (<i>D<sub>AE</sub></i>) and Tsallis entropy
 diversity (<i>S<sub>q</sub></i>).
 <b>Filename: </b>McLeish_etal_LDA_Dryad.csv (Created:
 22 October 2019). These data were generated during the
 study and from the species relative abundance counts and comprise columns
 for: 1) a factor for each site of the study (“site”); 2) a factor for each
 habitat category (“habitat”); 3) a scaled (not centred) continuous
 variable for asymptotic estimator
 (<i>D<sub>AE</sub></i>) of Shannon diversity
 (“DAE.sca); 4) a scaled (not centred) continuous variable for Tsallis
 entropy (<i>S<sub>q</sub></i>) estimate of
 diversity (“<i>S<sub>q</sub></i>.sca”); and 5)
 three successive scaled (not centred) continuous variables for Moran’s
 eigenvector map scores (“mem.x.sca”). The dataset was used in the Linear
 discriminant analysis (LDA) that was used to compare the variables in
 distinguishing the four habitat categories. Three sets of LDAs were used
 that comprised either the two diversity estimates and the spatially
 optimised predictors (MEMs), the two diversity estimates, or the three
 spatially optimised predictors (MEMs).
 <b>Filename: </b>McLeish_etal_Spatial_Dryad.csv
 (Created: 22 October 2019). The spatial autocorrelation
 approaches were computed using the geographic coordinates georeferenced
 during the study (<b>Filename:
 </b>Virome_Host_Ecology_Coordinates_Dryad.csv, Created: 22 October
 2019) and site-by-species relative abundance matrix generated from the
 counts described above. These data were used to compute the MEM
 eigenfunctions that correspond to the (<i>n</i> = 23 - 1)
 study sites. Subsets of the data table were used in the principal
 component analysis (PCA) of the site-by-species matrix (columns P to MF),
 a redundancy analysis (RDA) of the site-by-species relationships given
 environmental variation (columns D to O), and a partial residual analysis
 (PRA) used to model the effects (a filter of the orthogonal instrumental
 variable) of the environment on the residuals of species relative
 abundances. The continuous environmental variables comprise columns for
 “elevation”, “slope”, and “aspect” for each site. These topographic
 variables were generated from a raster layer of “elevation” (available
 from: https://www.europeandataportal.eu). Aspect and slope were calculated from the elevation layer. The data also include a factor for “landcover” type derive from a spatial polygon object (available from: http://centrodedescargas.cnig.es) as well as a factor for soil type (“fao9soil”) [1, 2], also derived from a spatial polygon object (permissions granted to the first author ID 22640). The polygon objects were rasterised and the variable factors extracted using the minimum value function. A generalised linear model was used to select from the 19 WorldClim climate variables (available from: https://www.worldclim.org/bioclim). Six WorldClim climate variables were used: Annual Mean Temperature (“bio_1”), Mean Temperature Diurnal Range (“bio_2”), Temperature Seasonality (“bio_4”), Minimum Temperature of Coldest Month (“bio_6”), Precipitation Seasonality (“bio_15”), and Precipitation of Coldest Quarter (“bio_19”). <b>Funding:</b> This work was funded by Plan Estatal de I+D+I, MINECO, Spain (grant RTI2018-094302-B-I00) and a Formación de Personal Investigador contract awarded to Adrián Peláez, reference number BFU2015-64018-R (BES-2016-077810). Plan Estatal de I+D+I provided funding for a metagenomics study of plant virus ecology and evolution. The latter funder supported AP’s doctorate. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. <b>References:</b> 1. The European soil database (2006) GEO: connexion, 5 (7), pp. 32-33. 2. The European Soil Database distribution version 2.0, European Commission and the European Soil Bureau Network, CD-ROM, EUR 19945 EN, 2004.