Occurrence patterns and trends of frogs in coastal wetlands of the Great Lakes call for further habitat restoration

Abstract

Countless wetlands have been lost and degraded globally, making amphibians the most threatened vertebrate class. However, despite facing extensive threats and stressors, coastal wetlands of the Laurentian Great Lakes of North America (lakes Superior, Michigan, Huron, Erie, and Ontario) still support sizable populations of frogs (order Anura, including toads). We used data from the Great Lakes Coastal Wetland Monitoring Program to quantify the first‐ever annual occurrence probabilities and trends (2011–2023) of eight marsh‐breeding frog species, or groups of species, at 1550 point count locations in 747 coastal wetlands throughout the Great Lakes, and to assess 11 potential drivers of occurrence. Sampled wetlands were marshes greater than 4 ha in area with a permanent or periodic surface‐water connection to an adjacent Great Lake or their connecting river systems. Across our study area, green frog (Lithobates clamitans) occurrence increased by 8% per year, whereas chorus frog (Pseudacris maculata, Pseudacris triseriata) occurrence decreased by 14% per year. We found more positive or stable trends in occurrence among lakes and species (85%) than negative trends (15%). Occurrence of all species was negatively associated with one or two indicators of poor water quality: specific conductance, ammonium nitrogen, nitrate nitrogen, and urban and agricultural land cover in the surrounding watershed (median area: 12 km²). Occurrence of multiple species was positively associated with high lake levels and surrounding wetland (<250 m) and forest (<2.5 km) land cover and negatively associated with surrounding road density (<2.5 km). Even though occurrence of most species was increasing or stable and was relatively high (>50%), all will likely benefit from conservation actions because 50%–90% of Great Lakes coastal wetlands have been lost and converted to anthropogenic land uses, leaving frog populations at a fraction of their former, original sizes. Therefore, extra precaution is critical to help ensure their growth and persistence. Improving water quality, increasing natural forest and wetland land cover within 2.5 km, and reducing roads within 2.5 km of Great Lakes coastal wetlands will help conserve these important indicator species in this globally recognized but imperiled ecosystem.

INTRODUCTION

Over 50% and perhaps as much as 87% of natural wetlands have been lost globally due to anthropogenic conversion to other land uses (Davidson, 2014; Fluet-Chouinard et al., 2023). As a result, more amphibian species are threatened by extinction than any other vertebrate class (Dudgeon et al., 2006; Luedtke et al., 2023). The Laurentian Great Lakes of North America (lakes Superior, Michigan, Huron, Erie, and Ontario; hereafter “Great Lakes”) contain some of the world's most unique and important wetlands (Keough et al., 1999; Sierszen et al., 2012). Great Lakes coastal wetlands—defined as wetlands under direct hydrologic influence from Great Lakes waters or their connecting river systems (McKee et al., 1992)—have especially a high wildlife conservation value (e.g., Falconer et al., 2016; Grand et al., 2020; Riffell et al., 2001; Studholme et al., 2023; Tozer et al., 2020, 2024; Wyman & Cuthbert, 2016). Of 10 Ramsar Wetlands of International Importance within the Great Lakes basin, nine are coastal wetlands due in large part to significant wildlife populations (Ramsar Sites Information Service, 2023), even though coastal wetlands comprise only 3% of the total wetland area in the region (Amani et al., 2022; United States Environmental Protection Agency, 2023). However, despite their importance, Great Lakes coastal wetlands are under threat due to ongoing loss and degradation from agriculture, development, pollution, invasive species, and the compounding effects of climate change (Allan et al., 2013; Danz et al., 2007; Mortsch, 1998; Smith et al., 2015). Even with aerial losses of 50%–90% depending on the region (Amani et al., 2022; Ducks Unlimited Canada, 2010; Hecnar, 2004), Great Lakes coastal wetlands still support sizable populations of frogs (order Anura, including toads; Hecnar, 2004; Price et al., 2004). It is, therefore, extremely important to monitor and assess the status of these animals within the globally recognized but imperiled coastal wetlands of the Great Lakes (Uzarski et al., 2017, 2019) and identify threats to be mitigated through conservation actions (Jenny et al., 2020).

Frogs are vital components of coastal wetlands in the Great Lakes because their relatively large populations support and interact with numerous other organisms (Hocking & Babbitt, 2014). A single coastal wetland can produce tens of thousands of individual frogs per species per year (Ashley & Robinson, 1996), and a single frog is capable of consuming nearly 5000 individual arthropods annually (Johnson & Christiansen, 1976). In turn, frogs are eaten by a variety of animals including fishes, reptiles, mammals, and birds, and they support higher trophic levels through the loss of tens of thousands of depredated eggs per reproductive female per year (Harding & Mifsud, 2017). In addition to their functional importance, frogs are useful indicators of wetland health and integrity (Cartwright et al., 2021; Macecek & Grabas, 2011). Occurrence of multiple species is related to anthropogenic environmental stress throughout different parts of the Great Lakes (Price et al., 2007), with numerous species being negatively influenced by pollutants, including fertilizers, pesticides, and road de-icers (Bishop & Gendron, 1998; Lawson & Jackson, 2021; Rouse et al., 1999). High concentrations of nitrogenous and phosphorous compounds, along with road salts, have been associated with reduced survival and mass and increased frequency of physiological and behavioral abnormalities in frogs (Egea-Serrano et al., 2012). In addition, many frog species hibernate in adjacent uplands rather than below the water surface and/or spend long periods of time foraging or undertaking other activities in adjacent uplands (Harding & Mifsud, 2017). Thus, several species are directly influenced by the composition and configuration (connectivity) of surrounding land cover (Eigenbrod et al., 2008b; Gagné & Fahrig, 2007). Urban land use in the surrounding landscape reduces availability of upland and overwintering habitat (Pope et al., 2000), and surrounding roadways increase mortality from vehicles during overland movements (Eigenbrod et al., 2009). Given their sensitivity to contaminants, and dependence on wetlands as well as surrounding uplands, the status and temporal trends of frog populations are excellent measures of the overall health and integrity of the entire lake-to-land ecotone in the Great Lakes.

Due to their usefulness as environmental indicators, various long-term frog monitoring programs have been implemented in different parts of the Great Lakes basin since the 1980s and 1990s (De Solla et al., 2006; Hecnar, 2004). These efforts were undertaken in part to monitor population change over time across the region, identify mechanisms responsible for changes, and develop associated conservation actions (Grant et al., 2020). Programs implemented throughout multiple Great Lakes states in the United States (Genet & Sargent, 2003; Hecnar, 2004; Mossman et al., 1998; Weir et al., 2014) and Ontario, Canada (Badzinski et al., 2008), follow a common, standardized, field sampling protocol (North American Amphibian Monitoring Program; Weir et al., 2009)—although relatively few survey locations are in Great Lakes coastal wetlands. Only the Great Lakes Marsh Monitoring Program (GLMMP) delivered by Birds Canada () has conducted long-term surveys for frogs in coastal and inland wetlands throughout the developed, southern portion of the Great Lakes basin in the United States and Canada annually since the mid-1990s (i.e., in the “Eastern Temperate Forests” ecological region; Commission for Environmental Cooperation, 1997). With the assistance of thousands of volunteer scientists (Tozer, 2020), the GLMMP has documented a mix of positive (n = 2), stable (n = 5), and negative (n = 1) trends in occurrence of eight frog species from 1995 to 2023 (Birds Canada, 2024). However, the GLMMP has never effectively monitored frogs throughout the relatively undeveloped, northern portion of the Great Lakes basin (i.e., the “Northern Forests” ecological region; Commission for Environmental Cooperation, 1997) due primarily to low availability of volunteer scientists (Crewe et al., 2006; Tozer et al., 2022; Weeber & Vallianatos, 2000).

To help fill the gap in frog sampling in the northern portion of the Great Lakes and monitor the status and temporal trends of the ecological condition of coastal wetlands throughout the Great Lakes basin, the Great Lakes Coastal Wetland Monitoring Program (CWMP; ) was implemented in 2011 by Central Michigan University and dozens of collaborating organizations in the United States and Canada in partnership with the United States Environmental Protection Agency (Uzarski et al., 2017, 2019). Each year, trained CWMP investigators survey marsh-breeding frogs, as well as birds, fishes, macroinvertebrates, vegetation, and water quality, at coastal wetlands throughout the Great Lakes (Uzarski et al., 2017, 2019). Frog data from the GLMMP and CWMP have been combined, where appropriate, to answer questions of conservation interest and to provide critical monitoring and status assessments (e.g., Tozer et al., 2022; Tozer & Mackenzie, 2019). CWMP data have also been used to examine findings of the GLMMP in areas where GLMMP data are sparse (Gnass Giese et al., 2018). Thus, these two programs provide flexible options for guiding frog conservation at inland and coastal wetlands throughout the entire Great Lakes basin.

Analyses based on data from the various monitoring programs and other research show that frog occurrences in the Great Lakes, like those in many regions across the globe, are negatively associated with (1) habitat loss and degradation, (2) climate change, (3) pollutants, and (4) diseases (Lesbarrères et al., 2014)—although the influence of these factors is region- and species-specific (Grant et al., 2020). The following have been suggested as key conditions for conserving frogs in the Great Lakes: (1) high water quality, free of pollutants (Hecnar, 1995; Rouse et al., 1999; Russell et al., 1995, 1997); (2) extensive wetland, forest, and other natural land cover in the surrounding landscape (e.g., <1–3 km; Eigenbrod et al., 2008a; Gagné & Fahrig, 2007; Price et al., 2004); and (3) low road density and urban land use in the surrounding landscape (Carr & Fahrig, 2001; Cosentino et al., 2014; Eigenbrod et al., 2009). With over a decade of data collection by the CWMP to date, our objective was to calculate the first-ever occurrence probabilities and temporal trends (hereafter “trends”) for marsh-breeding frog species at coastal wetlands throughout the Great Lakes. This will yield a more complete assessment of the status of frogs in the region. It will also help verify the driving factors described above and identify any additional factors for conservation action.

We analyzed eight marsh-breeding frog species, or groups of species, hereafter “species,” using 13 years of data (2011–2023) collected at 1550 point count locations in 747 coastal wetlands by the CWMP. Sampled wetlands were marshes greater than 4 ha in area with a permanent or periodic surface-water connection to an adjacent Great Lake or their connecting river systems (Uzarski et al., 2017). For each species, we estimated annual occurrence probabilities and trends for each of the Great Lakes and overall, and we assessed 11 potential drivers of occurrence describing water quality, characteristics of the surrounding landscape, and lake levels. Based on previous studies, we predicted that (1) occurrence of American bullfrog (Lithobates catesbeianus) and green frog (Lithobates clamitans) would increase over time, occurrence of chorus frog (Pseudacris maculata, Pseudacris triseriata) would decrease over time, and occurrence of the rest of the species would be stable (Birds Canada, 2023; COSEWIC, 2008; Tozer, 2020); and (2) occurrence of American bullfrog, green frog, and northern leopard frog (Lithobates pipiens) would be greater in most years in lakes Erie and Ontario than in the other lakes (Tozer, 2020). We also predicted that occurrence of most species would (1) be negatively associated with indicators of poor water quality, that is, be negatively associated with specific conductance, ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃-N), soluble reactive phosphorus, agricultural land cover in the surrounding watershed, and urban land cover in the surrounding watershed (Bishop et al., 1999; Donald, 2021; Price et al., 2004, 2007); (2) be positively associated with wetland and forest land cover in the surrounding landscape (Gagné & Fahrig, 2007; Hecnar, 2004; Hecnar & M'Closkey, 1998; Houlahan & Findlay, 2003; Knutson et al., 1999; Marsh et al., 2017; Pope et al., 2000; Sawatzky et al., 2019; Trenham et al., 2003); (3) be negatively associated with road density in the surrounding landscape (Carr & Fahrig, 2001; Cosentino et al., 2014; Eigenbrod et al., 2008a, 2009; Lehtinen et al., 1999); and (4) increase with rising lake levels over time (Gnass Giese et al., 2018). However, despite these general predictions, we anticipated exceptions; for example, American bullfrog and American toad (Anaxyrus americanus) have been found to be insensitive to chloride/specific conductance (Collins & Russell, 2009; Matlaga et al., 2014), and American toad has been found to decrease with rising lake levels over time (Gnass Giese et al., 2018).

METHODS

Study area and design

We surveyed frogs in coastal wetlands throughout the Great Lakes basin (Figure 1). Coastal wetlands were selected according to CWMP protocol using a stratified, random sampling procedure (Uzarski et al., 2017, 2019). Further details regarding the study design are in Burton et al. (2008). The sampling domain included all marshes greater than 4 ha in area with a permanent or periodic surface-water connection to an adjacent Great Lake or their connecting river systems (Uzarski et al., 2017). The selection of wetlands was stratified 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 one of the five 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 five years. In addition, we resampled 10% of wetlands between years according to a rotating panel design. Sampled wetlands were safely accessible and were dominated by emergent herbaceous vegetation and shallow water (<2 m deep) containing floating and/or submerged vegetation.

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Frog surveys

We conducted frog surveys at one to six 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 >500 m apart to avoid double detections. Each point count location was surveyed for 3 min on each of three occasions at least 15 days apart between late March and early July, which is the main breeding season for frogs in the region. During each point count, observers recorded all species heard. Chorus frogs (Pseudacris spp.) were combined together, and treefrogs (Dryophytes spp.) were combined together due to challenges with identifying species within each of these groups by sound in the field. Surveys occurred at night starting at least 0.5 h after local sunset and only under weather conditions that were favorable for detecting all species present (no persistent or heavy precipitation; wind: Beaufort 0–3, 0–19 km/h). The first survey in the season was conducted when the air temperature was >5°C, the second survey when the air temperature was >10°C, and the third survey when the air temperature was >17°C. This approach maximized the odds that early-, mid-, and late-season breeders would be detected by timing surveys during peak calling for each group of species regardless of annual variation in timing of spring melt. We trained observers so they thoroughly understood the field protocols, and we required each observer to pass an aural frog identification test in order to collect data. For a detailed description of the sampling protocol, visit .

Water quality surveys

We sampled water quality once between mid-June and early September in each wetland in each year that a wetland was selected for surveys. Three water quality sampling points (replicates) were surveyed within each monodominant vegetation zone (1–4 zones per wetland). Replicates were >15 m apart in water >5 cm deep. Monodominant vegetation zones were visually identified as patches of macrophytes in which structurally similar species (often a single genus) represented at least 75% of the aquatic plant community (e.g., Typha, wet meadow). Water quality was sampled at the mid-depth of the water column at each sampling point using a multiparameter sonde for specific conductance (standardized to 25°C). For ammonium nitrogen, nitrate nitrogen, and soluble reactive phosphorus, a composite water sample from the three sampling points within each vegetation zone was filtered and frozen for later analysis in the laboratory following standard methods (Lipps et al., 2023). Values within each vegetation zone were averaged, and then zone-level means were averaged to yield a single measure for each wetland. For a detailed description of the sampling protocol, see Uzarski et al. (2017) or visit . Water quality surveys were completed at fewer wetlands each year than frog surveys; therefore, data for specific conductance, ammonium nitrogen, nitrate nitrogen, and soluble reactive phosphorus were available for only 1021 of the 1913 (53%) wetland–year combinations with frog survey data.

Response variable

The response variable for each species was the detection (occurrence) of at least one individual during any of the three surveys at each point count location in each year (e.g., similar to Grand et al., 2020; Tozer et al., 2024), whereas environmental predictors were measured within the surrounding wetland or at the landscape scale (within 250–2500 m of a point count location; Zuckerberg et al., 2012). We viewed detections/occurrences as indices of true occupancy, meaning our modeled values estimate the probability of occurrence unadjusted for detectability (MacKenzie et al., 2018). We assumed that variation in species-specific detection was uncorrelated with the predictors in our models, including year. This approach was sufficient in our case because our primary objective was to quantify relative differences and changes in occurrence and not to quantify true occupancy. Our assumption was warranted because our data were collected using standardized methods designed to reduce heterogeneity in detection, such as observer training and testing, as well as restrictions on temperature, time of day, and wind (Uzarski et al., 2017). We found no trends in detectability across years for any of the species in our dataset, meaning that variation in detection did not bias our trend estimates (Appendix S1: Figure S1).

The frog dataset consisted of 10,803 point count surveys completed at 1550 point count locations in 747 coastal wetlands over 13 years (2011–2023; Figures 1 and 2). In each year, we completed frog surveys at a mean of 277 point count locations (range: 216–329) in a mean of 147 wetlands (range: 111–169) throughout the Great Lakes basin. The mean annual number of point count locations in each lake was 57 points for Erie (range: 33–85 points), 81 for Huron (47–102), 43 for Michigan (24–55), 62 for Ontario (47–80), and 34 for Superior (21–56; Figure 2). We surveyed 2.1 ± 1.4 (mean ± SD) point count locations per wetland (range: 1–6). In total, we analyzed eight frog species or groups of species: (1) American toad; (2) American bullfrog; (3) chorus frog, which included boreal chorus frog (P. maculata) and western chorus frog (P. triseriata); (4) gray treefrog, which included Cope's gray treefrog (Dryophytes chrysoscelis) and eastern gray treefrog (Dryophytes versicolor); (5) green frog; (6) northern leopard frog; (7) spring peeper (Pseudacris crucifer); and (8) wood frog (Lithobates sylvaticus). We chose these species because they were widespread and bred regularly in Great Lakes coastal wetlands. Observations of other species were too few for analysis (Fowler's toad [Anaxyrus fowleri], mink frog [Lithobates septentrionalis], pickerel frog [L. palustris]; Appendix S1: Figure S2). To assess whether parts of our study area were out of range for some species, we divided our study area into 10 regions and dropped regions from species-specific analyses if raw (unmodeled) occurrence was <5% of point count locations (Appendix S1: Table S1). By excluding out-of-range point count locations, we reduced the number of absences and focused our analysis on point count locations where non-detections were most likely to represent legitimate absences. As a result, wood frog was dropped from analyses involving Lake Erie; all the other species were analyzed across all five of the Great Lakes.

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Environmental predictors

We included 11 environmental predictors in our models that are known to influence occurrence of frogs in the Great Lakes: six described water quality (specific conductance, ammonium nitrogen, nitrate nitrogen, soluble reactive phosphorus, urban land cover in the surrounding watershed, agricultural land cover in the surrounding watershed; e.g., Rouse et al., 1999), four described characteristics of the surrounding landscape (wetland <250 m from the point count location, wetland <2.5 km from the point count location, forest <2.5 km from the point count location, density of roads <2.5 km from the point count location; e.g., Price et al., 2004), and one described lake levels (detrended, standardized Great Lakes water levels; Gnass Giese et al., 2018). We viewed specific conductance as a proxy for road salt, given that specific conductance was highly correlated with chloride (r = 0.83), and we had more observations for specific conductance than for chloride. Two of the water quality predictors (urban and agricultural land cover in the surrounding watershed) and all the landscape predictors were static covariates (i.e., they were the same for all years at a given point count location), whereas the rest of the water quality predictors (specific conductance, ammonium nitrogen, nitrate nitrogen, soluble reactive phosphorus) and Great Lakes water levels were dynamic covariates (i.e., they varied annually). Landscape and water-level data at finer temporal and spatial scales, respectively, would have been preferred but were unavailable. Nonetheless, the landscape and water-level data we used provided useful approximations of the true values, particularly at the <2.5 km and watershed scales (e.g., Michaud et al., 2022). We measured characteristics of the surrounding landscape within 2.5 km because studies suggested frogs respond most to land cover within 2–3 km (Carr & Fahrig, 2001; Eigenbrod et al., 2008a, 2009; Houlahan & Findlay, 2003; Price et al., 2004). The environmental predictors were not strongly correlated with each other (−0.52 < r < 0.52; Appendix S1: Figure S3). We considered additional environmental predictors such as water temperature and clarity, pH, dissolved oxygen, total nitrogen, and total phosphorus, but we did not include them in our modeling because they were either correlated with at least one predictor already in our final set (|r| > 0.6) or were not associated, or only weakly associated, with frog occurrence based on existing literature.

Derivation of dynamic water quality predictors is described in water quality surveys above. Percent urban and agricultural land cover in the surrounding watershed was from Host et al. (2019), with watersheds defined by Forsyth et al. (2016). Percent wetland land cover <250 m was based on the coastal wetland inventory layer built by the Great Lakes Coastal Wetland Consortium (GLCWC_CWI; Albert et al., 2004, 2005; Burton et al., 2008; Uzarski et al., 2017). Percent wetland and forest land cover <2.5 km was based on the land cover and use layer built by the Great Lakes Aquatic Habitat Framework, which combined data from the 2011 version of the National Land Cover Database in the United States () and the 2009–2011 version of the Southern Ontario Land Resource Information System (). Road density was derived from the population and roads layer built by the Great Lakes Aquatic Habitat Framework using data from the US Census Bureau () and Statistics Canada (). The coastal wetland, land cover and use, and population and roads layers are available through the Great Lakes Aquatic Habitat Framework (Wang et al., 2015) at . We used ArcGIS 10.8.1 (Esri, 2020) to overlay point count locations onto the GIS layers and extracted the relevant predictors for each point (Figure 3). Yearly lake levels were from the Great Lakes Environmental Research Laboratory of the National Oceanic and Atmospheric Administration available at . We used the mean yearly lake level from May to July since these months overlapped with surveys in all years and regions. We detrended lake levels from year by using the residuals from a line of best fit for each lake, given that lake levels generally increased in all lakes over the course of the study. Lake levels were also standardized across lakes by subtracting the long-term mean (2011–2023) for each lake from the annual value for each lake and dividing by the SD, given that the reference value is the same for all lakes (International Great Lakes Datum 1985). Our detrended, standardized lake levels therefore represent lake levels without being confounded with year (Figure 4). To facilitate model convergence, we censored extreme observations for some of the predictors by dropping all values >mean + 3 times the SD. Thus, we removed 28 (1%) specific conductance observations >977 μS/cm, 42 (2%) ammonium nitrogen observations >0.15 mg/L, 48 (2%) nitrate nitrogen observations >1.2 mg/L, 57 (3%) soluble reactive phosphorus observations >0.14 mg/L, and 33 (1%) road density observations >5.8 km/ha.

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Statistical modeling

We used Bayesian hierarchical models with spatial dependencies (e.g., Ethier et al., 2022; Ethier & Nudds, 2015; Smith et al., 2015; Thogmartin et al., 2004). We fit these models using Integrated Nested Laplace Approximation (INLA) and the R-INLA package (Rue et al., 2009) for R statistical computing (version 4.3.0; R Core Team, 2024). Methods generally followed Meehan et al. (2019) and Tozer et al. (2024), but given that the response variable was occurrence and not abundance, we used a binomial distribution with a logit link. For each species, we modeled the expected occurrence 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 a spatial structure in the models using an intrinsic conditional autoregressive (iCAR) structure (Besag et al., 1991). By accounting for this spatial structure, the model allowed occurrence and trend information to be shared among adjacent lakes (as described later), 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 occurrence (у_i,j,t) at a point count location within a given wetland (j), lake (i), and year (t). The expected occurrence per lake within a given year (μ_i,t) for each of the eight species took the form: $$$ \log \left({\upmu}_{it}\right)={\upalpha}_i+{\uptau}_i{\mathrm{T}}_{i,j,t}+{\upkappa}_j+{\mathrm{y}}_{i,t}+{\upbeta}_1{X}_{1j}\dots {\upbeta}_{11}{X}_{11j} $$$ where α is the random lake intercept; T is the year, indexed to 2023; τ is the random lake slope effect; κ is the random wetland effect; and у is the random lake-year effect. Environmental predictors (X₁ – X₁₁) characterized water quality (six predictors), characteristics of the surrounding landscape (four predictors), and lake levels (one predictor), as described in detail in Environmental predictors above.

The random lake intercept (α_i) had an iCAR structure, where values of α_i were derived from a normal distribution with a mean value related to the average occurrence 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 (τ_i) as spatially structured, lake-specific, random slope coefficients for the year effect, using the iCAR structure, with conditional means and variances as described earlier. We incorporated the spatial structure into the random lake slopes (τ_i) 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 (α_i) could be interpreted as the lake-specific expected occurrence during the final year of the time series (Meehan et al., 2019). We accounted for differences in occurrence among wetlands (κ) with an independent and identically distributed (idd) random effect. To derive an annual occurrence probability per lake, we included a random effect per lake-year (у_i,t) with an idd and combined these effects with α and τ. The coefficients of X₁ to X₁₁ were given normal priors with means of zero and precision equal to 0.001. We scaled the spatial structure of parameters α and τ such that the geometric mean of marginal variances was equal to 1 (Freni-Sterrantino et al., 2018; Riebler et al., 2016; Sørbye & Rue, 2014), and priors for precision parameters were penalized complexity priors, with the upper probability constant (P) = 1 and the penalization constant (PC) = 0.01 (Simpson et al., 2017). We also assigned precision for the random wetland and lake-year effects with parameter values UPC = 1 and PC = 0.01. In general, the weakly informed priors used here tend to shrink the structured and unstructured random effects toward zero in the absence of a strong signal (Simpson et al., 2017). Following analysis, we computed posterior estimates of trends (τ), transformed into constant rates of population change using 100 × (exp(τ) − 1), 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 & Sauer, 2002). We note that all our models successfully converged.

We explored the predictive ability of the full model (i.e., spatially explicit structure and environmental predictors) for each species by comparing it with two reduced models: (1) a spatially explicit hierarchical model with no environmental predictors and (2) a generalized linear model with environmental predictors but no spatially explicit structure. These reduced models took the form:

• log(μ_it) = α_i + τ_iΤ_i,j,t + κ_j + у_i,t
• log(μ_it) = α + β₁X_1j … β₁₁X_11j

The predictive ability of each of the three models for each species was measured using area under the curve (AUC), with 0.7–0.8 indicating acceptable predictive ability, 0.8–0.9 excellent ability, and greater than 0.9 outstanding ability (Mandrekar, 2010). AUCs were lowest for the generalized linear model with environmental predictors but no spatially explicit structure (0.59–0.84, n = 8 species), suggesting poor to acceptable predictive ability if only environmental predictors were used to model occupancy. The spatially explicit hierarchical model with no environmental predictors and the full model each performed much better (AUCs: 0.85–0.98 in each case), indicating excellent predictive ability for both models. Therefore, we used the full model for inference.

Nearly half (47%) of our dataset was missing observations for specific conductance, ammonium nitrogen, nitrate nitrogen, and soluble reactive phosphorus, and the R-INLA package drops cases with missing data. Therefore, we conducted analyses in two ways. Inferences involving specific conductance, ammonium nitrogen, nitrate nitrogen, and soluble reactive phosphorus were based on a reduced dataset due to missing values, whereas all other inferences were based on models that dropped specific conductance, ammonium nitrogen, nitrate nitrogen, and soluble reactive phosphorus in order to utilize the full dataset. This approach maximized the power of our data for inference.

RESULTS

Green frog occurrence increased by 8% per year across all the Great Lakes combined, whereas chorus frog occurrence decreased by 14% per year, and occurrence of the rest of the species was stable (i.e., 95% credible limits included zero; Table 1). Occurrence of three species—American bullfrog, green frog, and northern leopard frog—increased by 7%–17% per year in at least one of the lakes, whereas occurrence of two species—chorus frog and northern leopard frog—decreased by 8%–16% per year in at least one of the lakes (Figure 5). Northern leopard frog was the only species with occurrence that increased in one lake (Superior) and decreased in another lake (Ontario) (Figure 5). We found more positive or stable trends among lakes and species (85%) than negative trends (15%) (Figure 5). Trends among lakes involved a mix of increasing and stable trends for three species, entirely stable trends for four species, and entirely negative trends for chorus frog (Figure 5). Trends among lakes tended to be more positive for the three species that hibernate under the water surface (7.4% ± 6.5%, mean ± SD; American bullfrog, green frog, northern leopard frog) compared to the five species that hibernate in adjacent uplands (−3.5% ± 6.9%; American toad, chorus frog, gray treefrog, spring peeper, wood frog) (Figure 5).

TABLE 1 Trends (2011–2023) in occurrence (percent change per year) were mostly stable (95% credible limits included zero) for eight marsh-breeding frog species, or groups of species, in coastal wetlands throughout the Great Lakes.

Species	Taxa	Trend	Lower CL	Upper CL
American bullfrog	Lithobates catesbeianus	9.7	−3.9	25.5
Green frog	Lithobates clamitans	8.1	0.4	16.6
Northern leopard frog	Lithobates pipiens	6.9	−1.4	15.8
Gray treefrog	Dryophytes chrysoscelis, Dryophytes versicolor	3.4	−4.4	11.9
American toad	Anaxyrus americanus	2.9	−3.2	9.7
Spring peeper	Pseudacris crucifer	−4.5	−13.1	4.9
Wood frog	Lithobates sylvaticus	−6.1	−15.6	4.4
Chorus frog	Pseudacris maculata, Pseudacris triseriata	−14.3	−21.6	−6.6

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Relationships with environmental predictor variables varied among species. Occurrence of all species was negatively associated with one or two indicators of poor water quality: specific conductance (wood frog), ammonium nitrogen (American toad, gray treefrog), nitrate nitrogen (American bullfrog, chorus frog, northern leopard frog), agricultural land cover in the surrounding watershed (northern leopard frog, spring peeper), and urban land cover in the surrounding watershed (American bullfrog, green frog, northern leopard frog, spring peeper) (Figures 6 and 7). Only wood frog occurrence was positively associated with nitrate nitrogen, and only chorus frog occurrence was positively associated with agricultural land cover in the surrounding watershed (Figures 6 and 7). Occurrence of multiple species was positively associated with high lake levels (gray treefrog, green frog, spring peeper), surrounding wetland land cover <250 m (chorus frog, green frog, wood frog), and surrounding forest land cover <2.5 km (gray treefrog, green frog, spring peeper, wood frog) and negatively associated with surrounding road density <2.5 km (American bullfrog, green frog, northern leopard frog, spring peeper) (Figures 6 and 7). Only northern leopard frog occurrence was negatively associated with surrounding forest land cover <2.5 km (Figures 6 and 7). Occurrence of none of the species was associated with soluble reactive phosphorus or surrounding wetland land cover <2.5 km (Figure 6).

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Credible intervals around annual occurrence indices overlapped extensively among lakes for most species in most years, although some differences were worth noting, given that they appeared consistently across several or all the years. American bullfrog occurrence tended to be greater in lakes Erie and Ontario than in the other lakes, and occurrence of green frog and northern leopard frog tended to be greater in Lake Ontario than in the other lakes during the first half of the study period (Figure 8). By contrast, gray treefrog occurrence tended to be lower in Lake Erie than in other lakes, and spring peeper occurrence tended to be lower in lakes Erie and Superior than in the other lakes (Figure 8).

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DISCUSSION

We found more positive or stable trends in occurrence among lakes and species (85%) than negative trends (15%; Figure 5). Green frog occurrence increased by 8% per year across all the Great Lakes combined, whereas chorus frog occurrence decreased by 14% per year, and occurrence of the other six species was stable (Table 1). Declining occurrence for the chorus frog is concerning because the boreal chorus frog and western chorus frog, which we combined as the chorus frog, are threatened with extinction in parts of the Great Lakes (natureserv.org; COSEWIC, 2008). We also found that (1) occurrence of all the species was negatively influenced by one to two measures of poor water quality, (2) occurrence of four species (50%) was negatively influenced by surrounding road density <2.5 km, and (3) occurrence of five species (63%) was positively influenced by surrounding wetland land cover <250 m or forest land cover <2.5 km (Figure 8). As elaborated below, our findings suggest that further habitat restoration is needed to help conserve frogs in Great Lakes coastal wetlands.

Increasing or stable trends in occurrence for most of the species we analyzed are encouraging because populations of many amphibians are decreasing globally, although patterns vary regionally (Luedtke et al., 2023). Over 40% of amphibians are threatened with extinction worldwide, but rates are much higher throughout parts of the tropics (37%–48%), compared to 22% in the United States and 2% in Canada (Re:wild, Synchronicity Earth, IUCN SSC Amphibian Specialist Group, 2023). Local amphibian populations declined by 4% per year across North America (based on 389 time series of varying lengths; Grant et al., 2016), although 7 of 17 (41%) frog species decreased by 2%–11% per year from 2001 to 2011 in the Northeastern United States, immediately south of our study area (Weir et al., 2014), whereas none of 14 frog species decreased from 2001 to 2013 in the Southeastern United States (Villena et al., 2016). With such high regional variation in trends and associated threats and stressors, it is perhaps unsurprising that our trend results were more positive than the predominantly negative global norms for amphibians (Ford et al., 2020; Green et al., 2020).

Given the strong magnitude of threats and stressors influencing frogs and other wildlife in the Great Lakes (Allan et al., 2013), our overall positive results may suggest that some frog species are more resilient to anthropogenic influences than previously thought (e.g., Kerby et al., 2010). This may be especially true for species that we found had increasing or stable trends and relatively high occurrences (i.e., American bullfrog, American toad, gray treefrog, green frog, northern leopard frog, and spring peeper; Table 1, Figures 5 and 8). Nevertheless, conservation actions will be critical for supporting and expanding populations of all the species because 50%–90% of Great Lakes coastal wetlands have been lost and converted to anthropogenic land uses (e.g., Ducks Unlimited Canada, 2010; Hecnar, 2004; Wolter et al., 2006), leaving frog populations at a fraction of their former, original sizes (Hecnar, 2004). Therefore, extra precaution is critical to help ensure their growth and persistence.

As of 2024, at least 11 frog and toad species were endangered, threatened, or of conservation concern in at least one of the eight US Great Lakes states or Ontario (). Most of the 11 species, such as Illinois chorus frog (Pseudacris illinoensis) and plains leopard frog (Lithobates blairi), have restricted geographic distributions in the Great Lakes region. Only 2 of the 11 species occur in Great Lakes coastal wetlands or were widespread enough to be analyzed in our study: boreal chorus frog and western chorus frog, which we combined as chorus frog. Given that these two species are considered at risk in parts of the Great Lakes (; COSEWIC, 2008), we expected that chorus frog would decrease during this study (Table 1 and Figure 5). Both boreal chorus frog and western chorus frog prefer shallow, grassy (i.e., dominated by graminoids), fishless, ephemeral pools for breeding, although they also breed to a lesser extent in permanent waterbodies containing fish (Harding & Mifsud, 2017; Hecnar & M'Closkey, 1997; Ouellet et al., 2009). As such, both species may occur less frequently in coastal wetlands as compared to inland wetlands in the Great Lakes due to the ubiquitous presence of fishes in coastal wetlands that are connected to the Great Lakes (Birds Canada, 2023; Skelly, 1997; Trebitz et al., 2009). Indeed, occurrence of chorus frog was the lowest of all the species we analyzed in this study (Figure 8). Chorus frogs may prefer to breed in the wet meadow marsh zones of Great Lakes coastal wetlands because this vegetation type is located along the landward edge where shallow, grassy, sometimes fishless, breeding pools are most likely to occur (Smith, 1983). If true, then chorus frogs may have declined in Great Lakes coastal wetlands during our study due to concurrent decreases in their preferred wet meadow marsh breeding habitat. Meadow marsh extent may have decreased in Great Lakes coastal wetlands during our study due to flooding associated with record-high water levels (Anderson et al., 2023; Smith et al., 2021) and/or potentially encroachment by emergent plant species (especially cattails) driven by high water levels, anthropogenic nutrient inputs, among other factors (Frieswyk & Zedler, 2007; Rupasinghe & Chow-Fraser, 2024; Wilcox & Bateman, 2018; Woo & Zedler, 2002). Thus, restoration or reappearance of wet meadow marsh habitat containing shallow, grassy, breeding pools may be important for recovering at-risk populations of chorus frogs in Great Lakes coastal wetlands (Bleakney, 1959; COSEWIC, 2008).

We found that occurrence of all the frog species we analyzed was negatively associated with one or two indicators of poor water quality. This result was expected and consistent with the findings of other studies showing negative influences of increasing amounts of the same factors, including increasing chloride/specific conductance (Collins & Russell, 2009; Donald, 2021), ammonium nitrogen (Hecnar, 1995; Jofre & Karasov, 1999), nitrate nitrogen (Rouse et al., 1999), soluble reactive phosphorus (Bishop et al., 1999), and urban and agricultural land cover in the surrounding watershed (Price et al., 2004, 2007). We found two exceptions to these patterns in our study: wood frog occurrence was positively associated with nitrate nitrogen, and chorus frog occurrence was positively associated with agricultural land cover in the surrounding watershed (Figure 7). However, wood frog occurrence has been found to be positively associated with dissolved nitrogen and phosphorus in northern landscapes (Donald, 2021; Ruso et al., 2019), so additional nutrients in the form of nitrogen may be a benefit rather than a hindrance in the relatively nutrient-poor wetlands across its comparatively northern distribution in the Great Lakes (Appendix S1: Table S1; Cooper et al., 2016); and chorus frogs have been found to prefer, and sometimes be restricted to, open, grassy, breeding habitat, which is more common in agricultural areas (Bleakney, 1959; Ouellet et al., 2009). Despite these exceptions, all the frog species in the Great Lakes will likely benefit from efforts to improve water quality within coastal wetlands (Bishop et al., 1999; Bishop & Gendron, 1998), a strategy also recommended for conserving frogs in prairie pothole wetlands (Ruso et al., 2019) and other wetland types throughout North America (Rouse et al., 1999) and globally (Egea-Serrano et al., 2012).

Occurrence of multiple frog species in our study was positively influenced by high lake levels and surrounding wetland and forest land cover and negatively influenced by surrounding road density. These results were expected and are consistent with observations of the same patterns in other studies in different regions of the Great Lakes (Gnass Giese et al., 2018; Knutson et al., 1999; Lehtinen et al., 1999; Price et al., 2004; Trenham et al., 2003), throughout the central and eastern United States (for road density and forest land cover; Cosentino et al., 2014; Marsh et al., 2017), and elsewhere globally (e.g., Australia for roads; Callaghan et al., 2021). We found one exception to these patterns in our study: northern leopard frog occurrence was negatively influenced by surrounding forest land cover. However, this species has been found to occur less frequently in forested areas compared to more open locations (Eigenbrod et al., 2008a; Sawatzky et al., 2019), especially grasslands (Knutson et al., 2004). Despite this exception, all the frog species in the Great Lakes will likely benefit from efforts to increase natural wetland and forest land cover and reduce roads within 2.5 km of coastal wetlands. Numerous other studies have voiced the same recommendations to help conserve these important animals (Carr & Fahrig, 2001; Cosentino et al., 2014; Eigenbrod et al., 2008a, 2009; Gagné & Fahrig, 2007; Hecnar & M'Closkey, 1998; Houlahan & Findlay, 2003; Knutson et al., 1999, 2004; Lehtinen et al., 1999; Marsh et al., 2017; Pope et al., 2000; Price et al., 2004; Ruso et al., 2019; Sawatzky et al., 2019; Trenham et al., 2003).

We found that trends in occurrence among lakes were more positive in our study for frog species that hibernate under the water surface than species that hibernate in adjacent uplands. Upland hibernators probably decreased more than underwater hibernators in our study due to loss of surrounding natural land cover. Wetland, forested wetland, and forest land cover decreased by 3483 km² between 2000 and 2015 in the Great Lakes basin (Michaud et al., 2022), and much of the loss (>38%) was due to development and agricultural expansion in coastal areas (Wolter et al., 2006), with forest loss exceeding wetland loss during our study (Amani et al., 2022). By contrast, developed areas increased by 2893 km² between 2000 and 2015 in the Great Lakes basin (Michaud et al., 2022), which likely contributed to the greater decline of upland hibernators due to increased road mortality during their dispersal to remaining upland hibernation sites (Gagné & Fahrig, 2007). This further illustrates the need for, and highlights the importance of, efforts to increase natural wetland and forest land cover and reduce roads surrounding Great Lakes coastal wetlands to help conserve frogs. Restoration and protection of natural land cover will be most effective as a source of non-breeding habitat for frogs if it occurs within a buffer of 1 km or greater (e.g., <2.5 km) beyond the edge of coastal wetlands (Sawatzky et al., 2019), with percent wetland land cover at least 40% or more of the historic wetland coverage within the buffer, and percent forest land cover at least 50% or more within the buffer (Environment Canada, 2013). In addition, wetlands and patches of natural upland land cover should be connected by overland movement pathways that are safe for frogs (Lee et al., 2022). Connecting corridors that do not cross roads will be most effective (Eigenbrod et al., 2008b). Thus, coordinated, landscape-level conservation planning will be critical for enhancing the quality of coastal wetlands for frogs in the Great Lakes.

CONCLUSION

We found increasing or stable trends (2011–2023) and relatively high occurrences (>50%) for most marsh-breeding frog species in coastal wetlands throughout the Great Lakes. These results are encouraging, but we also found that (1) chorus frog occurrence decreased across our study area and in each of the lakes; (2) occurrence of all the species was negatively influenced by indicators of poor water quality; (3) occurrence of multiple species was positively influenced by surrounding wetland and forest land cover; and (4) occurrence of many species was negatively influenced by surrounding road density. Improving water quality, increasing natural forest and wetland land cover within 2.5 km, and reducing roads within 2.5 km of Great Lakes coastal wetlands will help conserve these important indicator species in this globally recognized but imperiled ecosystem.

AUTHOR CONTRIBUTIONS

Douglas C. Tozer: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; supervision; visualization; writing—original draft. Annie M. Bracey: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Valerie J. Brady: Data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Michael F. Chislock: Data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Jan J. H. Ciborowski: Data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Matthew J. Cooper: Data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Giuseppe E. Fiorino: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Thomas M. Gehring: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Erin E. Gnass Giese: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Greg P. Grabas: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Anna M. Harrison: Data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Robert W. Howe: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Gary A. Lamberti: Data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Gregory J. Lawrence: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Gerald J. Niemi: Conceptualization; data curation; funding acquisition; methodology; project administration; supervision; writing—review and editing. Donald G. Uzarski: Data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Bridget A. Wheelock: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. Danielle M. Ethier: Conceptualization; formal analysis; methodology; writing—review and editing.

ACKNOWLEDGMENTS

T. Brown, T. Redder, J. Schneider, K. O'Donnell, and M. Pawlowski in particular, and dozens of past and current collaborators and technicians, have made the CWMP a reality. We thank members of the Scientific Advisory Committee of the Long Point Waterfowl and Wetlands Research Program of Birds Canada for comments that improved the paper, and numerous landowners and Indigenous Nations for access to their lands. Douglas C. Tozer and Danielle M. Ethier were supported during this work by the Long Point Waterfowl and Wetlands Research Program of Birds Canada, Environment and Climate Change Canada (contract number 3000767152), and Wildlife Habitat Canada (grant number 24-300; primarily from funds generated by the purchase of Canadian Wildlife Habitat Conservation Stamps by waterfowl hunters). This work was funded by the Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency (USEPA), grant numbers GL-00E00612-0, 00E01567, and 00E02956. Although the research described in this work has been funded by the USEPA, it has not been subjected to the agency's required peer and policy review and therefore does not necessarily reflect the views of the agency and no official endorsement should be inferred. This is contribution number 212 of the Central Michigan University Institute for Great Lakes Research.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

Data and code (Tozer et al., 2025) are available from Dryad: .

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