DOI: 10.5061/dryad.vq83bk40k
title: Increasing marsh bird abundance in coastal wetlands of the Great Lakes (2011–2021) likely caused by increasing water levels

description: Wetlands of the Laurentian Great Lakes of North America, i.e., lakes
 Superior, Michigan, Huron, Erie, and Ontario, provide critical habitat for
 marsh birds. We used 11 years (2011–2021) of data collected by the Great
 Lakes Coastal Wetland Monitoring Program at 1,962 point count locations in
 792 wetlands to quantify the first-ever annual abundance indices and
 trends of 18 marsh-breeding bird species in coastal wetlands throughout
 the entire Great Lakes. Nine species (50%) increased by 8–37% per year
 across all of the Great Lakes combined, whereas none decreased. Twelve
 species (67%) increased by 5–50% per year in at least 1 of the 5 Great
 Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at
 least 1 of the lakes. There were more positive trends among lakes and
 species (n = 34, 48%) than negative trends (n = 5, 7%). These large
 increases are welcomed because most of the species are of conservation
 concern in the Great Lakes. Trends were likely caused by long-term,
 cyclical fluctuations in Great Lakes water levels. Lake levels increased
 over most of the study, which inundated vegetation and increased open
 water-vegetation interspersion and open water extent, all of which are
 known to positively influence abundance of most of the increasing species
 and negatively influence abundance of all of the decreasing species.
 Coastal wetlands may be more important for marsh birds than once thought
 if they provide high-lake-level-induced population pulses for species of
 conservation concern. Coastal wetland protection and restoration are of
 utmost importance to safeguard this process. Future climate projections
 show increases in lake levels over the coming decades, which will cause
 “coastal squeeze” of many wetlands if they are unable to migrate landward
 fast enough to keep pace. If this happens, less habitat will be available
 to support periodic pulses in marsh bird abundance, which appear to be
 important for regional population dynamics. Actions that allow landward
 migration of coastal wetlands during increasing water levels by removing
 or preventing barriers to movement, such as shoreline hardening, will be
 useful for maintaining marsh bird breeding habitat in the Great Lakes.
description: <strong>Study Area and Design</strong>
 We conducted our study in coastal wetlands throughout the entire
 Great Lakes basin (see Figure 1 in Tozer et al.). We selected coastal
 wetlands using a stratified, random sampling protocol (Uzarski et al.
 2017, 2019). Further details regarding the study design are in Burton et
 al. (2008). The sampling universe was all coastal wetlands greater than 4
 ha in size with a permanent or periodic surface-water connection to an
 adjacent Great Lake or their connecting river systems (Uzarski et al.
 2017). We stratified our selection of wetlands for the study by 1) wetland
 hydrogeomorphic type (riverine, lacustrine, barrier protected; Albert et
 al. 2005), 2) region (northern or southern; Danz et al. 2005), and 3) lake
 (i.e., the watershed of 1 of the 5 Great Lakes). We sampled approximately
 20% of all wetlands in each stratum each year, so that nearly all coastal
 wetlands within the Great Lakes basin meeting the selection criteria were
 sampled at least once every 5 years. In addition, we resampled 10% of
 wetlands between years according to a rotating panel design. Sampled
 wetlands were dominated by emergent, herbaceous vegetation and shallow
 water (&lt; 2 m deep) containing floating and/or submerged vegetation.
 We surveyed a mean of 369 point count locations (range: 309–423) for marsh
 birds in each year throughout the Great Lakes basin, and the mean annual
 number of point count locations in each lake was 65 points for Erie
 (range: 31–98 points), 99 for Huron (76–116), 56 for Michigan (36–71), 95
 for Ontario (77–117), and 55 for Superior (35–78; see Figure 2 in Tozer et
 al.). <strong>Bird
 Surveys</strong> We conducted surveys at 1–8
 fixed point count locations at the edge of, or within, each wetland in
 each year that a wetland was selected for surveys. Point count locations
 were &gt; 250 m apart to avoid double counting individuals. We
 surveyed each point count location twice per year, at least 15 days apart,
 between 20 May and 10 July, which was the peak breeding period for marsh
 birds in the study area. Surveys took place either in the morning (30 min
 before sunrise to 4 h after sunrise) or the evening (4 h before sunset to
 30 min after sunset), with 1 or both of the 2 surveys being in the morning
 each year (Tozer et al. 2017). We conducted surveys only when there was no
 precipitation and wind was &lt; 20 km/h (Beaufort 3 or less). Each
 point count survey lasted 10 min, consisting of an initial 5-min passive
 listening period followed by a 5-min call broadcast period. The call
 broadcast period was intended to increase detections of secretive species
 by eliciting auditory responses and was composed of 30 sec of
 vocalizations followed by 30 sec of silence for each of the following: 1)
 Least Bittern, 2) Sora, 3) Virginia Rail, 4) a mixture of American Coot
 and Common Gallinule, and 5) Pied-billed Grebe, in that order. We trained
 observers so they thoroughly understood the field protocols and we
 required each observer to pass an aural and visual bird identification
 test in order to collect data. CWMP bird surveys were 15 min in duration
 from 2011 to 2018 but were reduced to 10 min from 2019 to 2021 (Tozer et
 al. 2017). To accommodate changes in survey protocol, we filtered the data
 to only include birds detected in the first 10 min of point counts from
 2011 to 2018. For a detailed description of the sampling protocol visit
 greatlakeswetlands.org/Sampling-protocols. <strong>Response
 Variable</strong> The response variable for each
 species was the maximum number of individuals observed during either of
 the 2 surveys at each point count location in each year (Tozer 2020,
 Hohman et al. 2021). We viewed these counts as indices of true density,
 meaning our modeled values estimated relative abundance (e.g., Thogmartin
 et al. 2004). We assumed that variation in species-specific detection was
 uncorrelated with the predictors in our models, including year. This was
 sufficient in our case because our objective was to quantify relative
 differences and changes in abundance and not to quantify actual density.
 Our assumption was warranted because our data were collected using
 standardized methods designed to reduce heterogeneity in detection, e.g.,
 observer training and testing, as well as restrictions on survey date,
 time of day, and wind (Conway 2011, Uzarski et al. 2017). It was further
 justified by other long-term, broad-scale studies of birds based on point
 counts conducted using similar standardized approaches, which found no
 differences in year or covariate effects based on counts that were
 adjusted or unadjusted for detection (Etterson et al. 2009, Zlonis et al.
 2019). We note that long-term (1996–2013) marsh-breeding bird monitoring
 data collected throughout the developed, southern portion of the Great
 Lakes basin showed no systematic trends in detectability over time for 14
 of 15 (93%) species (Tozer 2016). We also found no trends in detectability
 across years for all of the species in our dataset (see Supplemental
 Material Figure S1 in Tozer et al.), meaning that differences in detection
 did not bias our estimates of annual abundance indices or trends.
 Therefore, we did not adjust for detectability, which has been supported,
 for instance, by Hutto (2016) and Johnson (2008). The
 dataset consisted of 8,120 surveys completed at 1,962 point count
 locations in 792 coastal wetlands in 599 watersheds (defined by Forsyth et
 al. [2016]) over 11 years (2011–2021; see Figure 1, 2 and Supplemental
 Material Table S1 in Tozer et al.). There were 2.2 ± 1.6 (mean ± SD) point
 count locations per wetland (range: 1–8) and 1.3 ± 0.9 wetlands per
 watershed (range: 1–9). In total, we analyzed 18 species: 1) American
 Bittern, 2) American Coot, 3) Black Tern, 4) Common Gallinule, 5) Common
 Grackle, 6) Common Yellowthroat, 7) Forster’s Tern, 8) Least Bittern, 9)
 Marsh Wren, 10) Mute Swan, 11) Pied-billed Grebe, 12) Red-winged
 Blackbird, 13) Sandhill Crane, 14) Sedge Wren, 15) Sora, 16) Swamp
 Sparrow, 17) Virginia Rail, and 18) Wilson’s Snipe. We chose these species
 because they were of conservation interest in the Great Lakes region
 (e.g., Bianchini and Tozer 2023) and regularly nested or foraged in Great
 Lakes coastal wetlands. We attempted to model abundance and trends for
 Trumpeter Swan (Cygnus buccinator) and Yellow-headed Blackbird
 (Xanthocephalus xanthocephalus), but data were too sparse for the models
 to converge. We considered some regions of our study area to be out of
 range for some species. We accounted for this by dividing our study area
 into 10 regions and dropped any of them from species-specific analyses if
 naive occupancy was &lt; 5% (Supplemental Material Table S2). By
 excluding out-of-range point count locations, we reduced the number of
 zero counts and focused our analysis on point count locations where zero
 counts were most likely to represent legitimate absences. As a result, the
 number of marsh-breeding bird species for which we quantified abundance
 and trends varied by lake due to uneven species occurrences across the
 study area: Superior (n = 10), Ontario (n = 12), Erie (n = 16), Huron (n =
 16), and Michigan (n = 17). The CWMP bird survey data are available by
 request at greatlakeswetlands.org. <strong>Environmental
 Predictors</strong> We included the following
 environmental predictors in our models, which were known to influence
 abundance of marsh-breeding birds in the Great Lakes: 1) percent local
 wetland cover within 250 m of point count locations (as a proxy for
 wetland size; e.g., Studholme et al. 2023), 2) detrended, standardized
 Great Lakes water levels (to avoid correlation with year; e.g., Hohman et
 al. 2021, Denomme-Brown et al. 2023), 3) percent urban land cover in the
 surrounding watershed (e.g., Rahlin et al. 2022), and 4) percent
 agricultural land cover in the surrounding watershed (e.g., Saunders et
 al. 2019). The land cover predictors were static covariates (i.e., they
 were the same for all years), whereas detrended, standardized Great Lakes
 water level was a dynamic covariate (i.e., it varied annually). Land cover
 and water-level information at finer spatial and temporal scales would
 have been preferred, but such data were unavailable. Nonetheless, it is
 reasonable to assume that the land cover and water-level data we used
 provided useful approximations of the true values, particularly at the
 watershed scale (e.g., Michaud et al. 2022). Percent local wetland cover
 was based on the coastal wetland layer built by the Great Lakes Coastal
 Wetland Consortium (Burton et al. 2008, Uzarski et al. 2017), and percent
 urban and agricultural land cover were from Host et al. (2019) with
 watersheds defined by Forsyth et al. (2016); all of these data are
 available at glahf.org/data. We used ArcGIS 10.8.1 to overlay CWMP sample
 points onto the land cover layers and extracted the relevant predictors
 for each point (see Figure 3 in Tozer et al.). Yearly water levels were
 from the National Oceanic and Atmospheric Administration (noaa.gov). We
 used the mean yearly water level from May to July since these months
 overlapped with our survey period. We detrended water levels from year by
 using the residuals from a line of best fit for each lake, given that
 water levels generally increased in all lakes over the course of the
 study. Water levels were also standardized across lakes by dividing the
 annual value for each lake by the long-term mean (2011–2021) for each
 lake, given the reference value is the same for all lakes (International
 Great Lakes Datum 1985). Our detrended, standardized lake levels therefore
 represent water levels without being confounded with year (see Figure 4 in
 Tozer et al.). The environmental predictors were not correlated (-0.2
 &lt; r &lt; 0.2; see Supplemental Material Figure S2 in Tozer et
 al.). <strong>Statistical
 Modeling</strong> We fit models in a Bayesian
 framework with Integrated Nested Laplace Approximation (INLA) using the
 R-INLA package (Rue and Martino 2009) for R statistical computing (version
 4.2.0; R Core Team 2022). For each species, we modeled the expected
 (predicted mean) number of individuals per point count location in each
 Great Lake in each year, as well as the trend in these values across years
 in each lake, and then pooled the lake-specific trends to obtain Great
 Lakes-wide estimates. We included spatial structure in the models using an
 intrinsic conditional autoregressive (iCAR) structure (Besag et al. 1991),
 which allowed for information on relative abundance to be shared across
 lakes sharing basin boundaries. By accounting for this spatial structure
 in counts, the model allowed abundance and trend information to be shared
 among adjacent lakes (as described below), which improved estimates for
 lakes with limited sample sizes (Bled et al. 2013) and reduced the amount
 of spatial autocorrelation in model residuals (Zuur et al.
 2017). We modeled counts уi,j,t using the maximum
 number of individuals observed at a point count location within a given
 wetland (j), lake (i), and year (t). The expected counts per lake within a
 given year µi,t for each of the 18 species took the form:
 log(µit) = αi + τiΤi,j,t + κj + ρj + уi,t + β1Wj + β2Lj + β3Uj +
 β4Ai where α = random lake intercept; T = year, indexed
 to 2021; τ = random lake slope effect; κ = random wetland effect; ρ =
 random wetland type effect; and у = random lake-year effect. Environmental
 predictors included: W = percent local wetland cover within 250 m; L =
 detrended, standardized water level; U = percent urban land cover in the
 surrounding watershed; and A = percent agricultural land cover in the
 surrounding watershed. The random lake intercept (αi)
 had an iCAR structure, where values of αi came from a normal distribution
 with a mean value related to the average of adjacent lakes. The random
 lake intercept also had a conditional variance proportional to the
 variance across adjacent lakes and inversely proportional to the number of
 adjacent lakes. We modeled the random lake slopes (τi) as spatially
 structured, lake-specific, random slope coefficients for the year effect,
 using the iCAR structure, with conditional means and variances as
 described above. We incorporated spatial structure into the random lake
 slopes (τi) to allow for information about year effects to be shared
 across neighboring lakes, and to allow year effects to vary among lakes.
 We transformed year (T) such that the maximum year was 0, and each
 preceding year was a negative integer. This scaling meant that the
 estimates of the random lake intercepts (αi) could be interpreted as the
 lake-specific expected counts (i.e., index of abundance) during the final
 year of the time series. We accounted for differences in relative
 abundance among wetlands (κ) and wetland types (ρ) with an independent and
 identically distributed (idd) random effect. To derive an annual index of
 abundance per lake, we included a random effect per lake-year (у) with an
 idd, and combined these effects with α and τ. Β1, β2, β3, and β4 were
 given normal priors with mean of zero and precision equal to 0.001. We
 scaled the spatial structure parameters α and τ such that the geometric
 mean of marginal variances was equal to one (Sørbye and Rue 2014, Riebler
 et al. 2016, Freni-Sterrantino et al. 2018), and priors for precision
 parameters were penalized complexity (PC) priors, with parameter values
 UPC = 1 and PC = 0.01 (Simpson et al. 2017). We also assigned precision
 for the random wetland, wetland type, and lake-year effects with a PC
 prior with parameter values previously stated. In general, the weakly
 informed priors used here tend to shrink the structured and unstructured
 random effects towards zero in the absence of a strong signal (Simpson et
 al. 2017). We validated distributional assumptions with
 simulation to ensure models could handle the large number of zero counts
 for some species. The abundance of most species was modeled using a
 zero-inflated Poisson (ZIP) distribution. Common Grackle and Red-winged
 Blackbird, which were more frequently detected compared to the other
 species, better fit a negative binomial distribution, and Common
 Yellowthroat better fit a Poisson distribution. We further validated
 models by visually inspecting 1) the fit versus raw counts; 2) residuals
 versus predictors; and 3) the estimate for Ф, the dispersion parameter
 (Zuur and Ieno 2016). Our visual inspections of fit versus raw counts
 suggested models were not overfit and were able to capture the variation
 of the raw counts. In general, residuals versus fit values behaved
 randomly around the zero line and residuals appeared to behave randomly
 with each predictor, suggesting the models fit well. The dispersion
 statistics were around 1 for all species, ranging lowest for Common
 Yellowthroat (0.72) and highest for Mute Swan (3.38), suggesting some
 residual under and over dispersion, respectively. Mute Swan had some high
 counts (outliers) which may have contributed to this. Following model
 analysis, we computed posterior estimates of trends (τ) and associated
 credible intervals for the full extent of the study area (i.e., by pooling
 lake-specific trends) using lake watershed size to calculate area-weighted
 averages (Link and Sauer 2002). <strong>References</strong> Albert, D. A., D. A. Wilcox, J. W. Ingram, and T. A. Thompson (2005). Hydrogeomorphic classification for Great Lakes coastal wetlands. Journal of Great Lakes Research 31:129–146. Besag, J., J. York, and A. Mollié (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43:1–20. Bianchini, K., and D. C. Tozer (2023). Using Breeding Bird Survey and eBird data to improve marsh bird monitoring abundance indices and trends. Avian Conservation and Ecology 18(1):4. Bled, F., J. Sauer, K. Pardieck, P. Doherty, and J. A. Royle (2013). Modeling trends from North American breeding bird survey data: a spatially explicit approach. PLoS ONE 8, e81867. Burton, T. M., J. C. Brazner, J. J. H. Ciborowski, G. P. Grabas, J. Hummer, J. Schneider, and D. G. Uzarski (Editors) (2008). Great Lakes Coastal Wetlands Monitoring Plan. Developed by the Great Lakes Coastal Wetlands Consortium, for the US EPA, Great Lakes National Program Office, Chicago, IL. Great Lakes Commission, Ann Arbor, Michigan, USA. Conway, C. J. (2011). Standardized North American marsh bird monitoring protocol. Waterbirds 34:319–346. Danz, N. P., R. R. Regal, G. J. Niemi, V. J. Brady, T. Hollenhorst, L. B. Johnson, G. E. Host, J. M. Hanowski, C. A. Johnston, T. Brown, J. Kingston, and J. R. Kelly (2005). Environmentally stratified sampling design for the development of Great Lakes environmental indicators. Environmental Monitoring and Assessment 102:41–65. Denomme-Brown, S. T., G. E. Fiorino, T. M. Gehring, G. J. Lawrence, D. C. Tozer, and G. P. Grabas (2023). Marsh birds as ecological performance indicators for Lake Ontario outflow regulation. Journal of Great Lakes Research 49:479–490. Etterson, M. A., G. J. Niemi, and N. P. Danz (2009). 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N. Brown, J. J. H. Ciborowski, and L. B. Johnson (2019). Risk-based classification and interactive map of watersheds contributing anthropogenic stress to Laurentian Great Lakes coastal ecosystems. Journal of Great Lakes Research 45:609–618. Hutto, R. L. (2016). Should scientists be required to use a model-based solution to adjust for possible distance-based detectability bias? Ecological Applications 26:1287–1294. Johnson, D. H. (2008). In defense of indices: the case of bird surveys. Journal of Wildlife Management 72:857–868. Link, W. A., and J. R. Sauer (2002). A hierarchical analysis of population change with application to Cerulean Warblers. Ecology 83:2832–2840. Michaud, W., J. Telech, M. Green, B. Daneshfar, and M. Pawlowski (2022). Sub-indicator: land cover. In State of the Great Lakes 2022 Technical Report. Published by Environment and Climate Change Canada and U.S. Environmental Protection Agency. R Core Team (2022). R: A language and environment for statistical computing. 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Tozer, D. C., C. M. Falconer, A. M. Bracey, E. E. Gnass Giese, G. J. Niemi, R. W. Howe, T. M. Gerhing, and C. J. Norment (2017). Influence of call broadcast timing within point counts and survey duration on detection probability of marsh breeding birds. Avian Conservation and Ecology 12(2):8. [Tozer et al.] Tozer DC, Bracey AM, Fiorino GE, Gehring TM, Gnass Giese EE, Grabas GP, Howe RW, Lawrence GJ, Niemi GJ, Wheelock BA, Ethier DM. Increasing marsh bird abundance in coastal wetlands of the Great Lakes, 2011–2021, likely caused by increasing water levels. Ornithological Applications. Uzarski, D. G., D. A. Wilcox, V. J. Brady, M. J. Cooper, D. A. Albert, J. J. H. Ciborowski, N. P. Danz, A. Garwood, J. P. Gathman, T. M. Gehring, G. P. Grabas, et al. (2019). Leveraging a landscape-level monitoring and assessment program for developing resilient shorelines throughout the Laurentian Great Lakes. Wetlands 39:1357–1366. Uzarski, D. G., V. J. Brady, M. J. Cooper, D. A. Wilcox, D. A. Albert, R. P. Axler, P. Bostwick, T. N. Brown, J. J. H. Ciborowski, N. P. Danz, J. P. Gathman, et al. (2017). Standardized measures of coastal wetland condition: implementation at a Laurentian Great Lakes basin-wide scale. Wetlands 37:15–32. Zlonis, E. J., N. G. Walton, B. R. Sturtevant, P. T. Wolter, and G. J. Niemi (2019). Burn severity and heterogeneity mediate avian response to wildfire in a hemiboreal forest. Forest Ecology and Management 439:70–80. Zuur, A. F., and E. I. Ieno (2016). A protocol for conducting and presenting results of regression-type analyses. Methods in Ecology and Evolution 7:636–645. Zuur, A. F., E. I. Ieno, and A. A. Saveliev (2017). Beginner’s guide to spatial, temporal and spatial-temporal ecological data analysis with R-INLA. Volume I: Using GLM and GLMM. Highland Statistics, Newburgh, United Kingdom.
description: # Increasing marsh bird abundance in coastal wetlands of the Great Lakes,
 2011–2021, likely caused by increasing water levels
 Data cleaning an analysis
 code for: \[Tozer et al.] Tozer DC, Bracey AM, Fiorino GE, Gehring TM,
 Gnass Giese EE, Grabas GP, Howe RW, Lawrence GJ, Niemi GJ, Wheelock BA,
 Ethier DM. Increasing marsh bird abundance in coastal wetlands of the
 Great Lakes, 2011–2021, likely caused by increasing water levels.
 Ornithological Applications. ## Description of the data and file structure
 All of the raw data are in 1 flat table: CWMP_MaxCount.csv. Each row in
 the table is the maximum number of individuals observed during either of 2
 annual surveys at a point count location in a particular year for 1 of the
 18 species analyzed, along with covariates. The fields are as follows:
 **site_id:** Wetland site number assigned by the Great Lakes Coastal
 Wetland Monitoring Program
 ([www.greatlakeswetlands.org](http://www.greatlakeswetlands.org)). Wetland
 sites based on polygons in the coastal wetland layer built by the Great
 Lakes Coastal Wetland Consortium. Referred to as "wetland" in
 Tozer et al. **point_id:** Sample point number within a wetland site.
 Referred to as "point count location" in Tozer et al. **year:**
 Year of observation. **lat:** Latitude of point_id (note that coordinates
 vary slightly among years for some points). **lon:** Longitude of point_id
 (note that coordinates vary slightly among years for some points).
 **class:** 1 of 3 wetland hydrogeomorphic types (i.e., riverine,
 lacustrine, or barrier protected) assigned by Albert, D. A., D. A. Wilcox,
 J. W. Ingram, and T. A. Thompson (2005). Hydrogeomorphic classification
 for Great Lakes coastal wetlands. Journal of Great Lakes Research
 31:129–146. Referred to as "wetland hydrogeomorphic type" or
 "wetland type" in Tozer et al. **basin:** 1 of the 5 Great Lakes
 (i.e., lk_erie [Lake Erie], lk_huron [Lake Huron], lk_mich [Lake
 Michigan], lk_ont [Lake Ontario], lk_sup [Lake Superior]). Referred to as
 "lake" in Tozer et al. **region:** 1 of 10 regions within the
 study area (i.e., Erie North, Erie South, Huron East, Huron South, Huron
 West, Michigan North, Michigan South, Ontario North, Ontario South,
 Superior). Developed by Tozer et al. because we considered some regions of
 our study area to be out of range for some species. We accounted for this
 by dividing our study area into these 10 regions and dropped any of them
 from species-specific analyses if naïve occupancy was &lt; 5%. See Table
 S2 in Supplemental Material of Tozer et al. for more details. **area_ha:**
 Area of wetland site in hectares. Based on polygons in the coastal wetland
 layer built by the Great Lakes Coastal Wetland Consortium. **GLHDID:**
 Watershed ID number described by Forsyth et al. as: unique ID created by
 the Great Lakes Hydrography Dataset team that numbers all of the
 watersheds and interfluves sequentially counterclockwise across the Basin,
 beginning with the mainland Boundary Waters area. Island numbers start at
 10,000 and are also numbered sequentially counterclockwise across the
 Basin. For more details see:
 [glahf.‌org/‌data](https://www.glahf.org/data/) and Forsyth, D. K., C. M.
 Riseng, K. E. Wehrly, L. A. Mason, J. Gaiot, T. Hollenhorst, C. M.
 Johnston, C. Wyrzykowski, G. Annis, C. Castiglione, K. Todd, et al. (2016)
 The Great Lakes hydrography dataset: consistent, binational watersheds for
 the Laurentian Great Lakes basin. Journal of the American Water Resources
 Association 52:1068–1088. **pcntag:** Percent agricultural land cover in
 the surrounding watershed (as defined by GLHDID above) given as a
 proportion between 0 and 1. A static covariate (i.e., it was the same for
 all years). Data from Host, G. E., K. E. Kovalenko, T. N. Brown, J. J. H.
 Ciborowski, and L. B. Johnson (2019). Risk-based classification and
 interactive map of watersheds contributing anthropogenic stress to
 Laurentian Great Lakes coastal ecosystems. Journal of Great Lakes Research
 45:609–618. See Figure 3 of Tozer et al. for an illustration. **pcntdev:**
 Percent urban land cover in the surrounding watershed (as defined by
 GLHDID above) given as a proportion between 0 and 1. A static covariate
 (i.e., it was the same for all years). Data from Host, G. E., K. E.
 Kovalenko, T. N. Brown, J. J. H. Ciborowski, and L. B. Johnson (2019).
 Risk-based classification and interactive map of watersheds contributing
 anthropogenic stress to Laurentian Great Lakes coastal ecosystems. Journal
 of Great Lakes Research 45:609–618.See Figure 3 of Tozer et al. for an
 illustration. **PerWetland:** Percent local wetland cover within 250 m of
 point count locations (as a proxy for wetland size) given as a proportion
 between 0 and 1. A static covariate (i.e., it was the same for all years).
 Calculated in a GIS based on polygons in the coastal wetland layer built
 by the Great Lakes Coastal Wetland Consortium. See Figure 3 of Tozer et
 al. for an illustration. **lakelevel:** Detrended, standardized Great
 Lakes water level (to avoid correlation with year). A dynamic covariate
 (i.e., it varied annually). Yearly water levels were from the National
 Oceanic and Atmospheric Administration
 ([noaa.‌gov](https://www.noaa.gov/)). We used the mean yearly water level
 from May to July since these months overlapped with our survey period. We
 detrended water levels from year by using the residuals from a line of
 best fit for each lake, given that water levels generally increased in all
 lakes over the course of the study. Water levels were also standardized
 across lakes by dividing the annual value for each lake by the long-term
 mean (2011–2021) for each lake, given the reference value is the same for
 all lakes (International Great Lakes Datum 1985). Our detrended,
 standardized lake levels therefore represent water levels without being
 confounded with year. See Figure 4 of Tozer et al. for an illustration.
 **maxcount:** Maximum number of individuals observed during either of the
 2 surveys at each point count location in each year. **taxa_code:** 1 of
 18 species analyzed: 1) AMBI (American Bittern), 2) AMCO (American Coot),
 3) BLTE (Black Tern), 4) COGA (Common Gallinule), 5) COGR (Common
 Grackle), 6) COYE (Common Yellowthroat), 7) FOTE (Forster’s Tern), 8) LEBI
 (Least Bittern), 9) MAWR (Marsh Wren), 10) MUSW (Mute Swan), 11) PBGR
 (Pied-billed Grebe), 12) RWBL (Red-winged Blackbird), 13) SACR (Sandhill
 Crane), 14) SEWR (Sedge Wren), 15) SORA (Sora), 16) SWSP (Swamp Sparrow),
 17) VIRA (Virginia Rail), and 18) WISN (Wilson’s Snipe). See Tozer et al.
 for scientific names. ## Sharing/Access information The bird survey data
 are available by request at . If
 one wishes to use the bird survey data published in this Dryad dataset,
 then a formal data request should be submitted at
 , although this is not required.
 Please note that further details regarding the Great Lakes Coastal Wetland
 Monitoring Program's design, sampling protocols, and other metadata
 are available at
 , including a
 detailed Quality Assurance Project Plan, and that the program's
 principal investigators are available to work with researchers to help you
 better understand and use the dataset. The coastal wetland layer built by
 the Great Lakes Coastal Wetland Consortium and the watershed layer built
 by Forsyth et al (full citation given above) are available at
 . Yearly water levels were from the
 Great Lakes Environmental Research Laboratory of the National Oceanic and
 Atmospheric Administration available at
 . The data cleaning an
 analysis code for Tozer et al. are available at
 . ## Code/Software
 All analytical scripts used to run this analysis are openly available on
 Birds Canada's Github page, under the repository name CWMP_Tozer2023.
 There are three files in the repository: 01-Introduction.Rmd: An overview
 of the data sources used in the analysis. 02-DataManipulation.Rmd: Scripts
 used to clean and compile the data prior to running the analysis.
 03_Analysis_SpatialZIP.Rmd: Scripts that run the analysis and create the
 output. R statistical computing (version 4.2.0; R Core Team 2022) was used
 to run and analysis. The following packages were loaded: corrplot, INLA,
 inlabru, inlatools, leaflet, ggmap, mapview, rgdal, reshape,
 rnaturalearth, sf, spdep, tidyverse, VGAM.