Divergent forecasts for two salt marsh ... - Wiley Online Library

Animal Conservation. Print ISSN 1367-9430

Divergent forecasts for two salt marsh specialists in response to sea level rise E. A. Hunter, N. P. Nibbelink & R. J. Cooper Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA

Keywords abundance models; ecosystem specialist; habitat loss; marshland birds; sea level rise; clapper rail; seaside sparrow. Correspondence Elizabeth Hunter, Warnell School of Forestry and Natural Resources, University of Georgia, 180 E. Green St., Athens, GA 30602, USA. Tel: 402-314-2742 Email: [email protected] Editor: Darren Evans Associate Editor: Jaime Ramos Received 04 October 2015; accepted 29 March 2016 doi:10.1111/acv.12280

Abstract Ecosystem specialists are predicted to be more vulnerable to global change than generalists, but whether specialists within an ecosystem will respond similarly to those changes is often largely unknown. Will specialists track changes in their habitats as a group, or are their distributions governed by landscape gradients that will make some species more sensitive to habitat changes? In this study, we forecasted the effects of sea level rise (SLR) on two salt marsh specialist bird species: clapper rails Rallus crepitans and seaside sparrows Ammodramus maritimus. We sampled the abundance of these two species in salt marshes throughout the Georgia, USA, coast in 2013–2014, and analyzed count data using a Bayesian N-mixture model. Model predictions were applied to an SLR land cover model to determine distribution shifts over 100 years. Both species distributions were most sensitive to the relative elevation gradient, with clapper rails using lower elevation marshes and seaside sparrows using higher elevation marshes. These disparities in habitat use, along with other differences according to marsh salinity and distance to forested areas, led to divergent responses to SLR. Clapper rail habitat is predicted to increase with SLR by 52%, but seaside sparrow habitat will contract by 81% by the year 2100. Seaside sparrow habitat is not predicted to decline until sometime between 2025 and 2050, at which point the decline will rapidly accelerate, indicating the importance of careful monitoring in future decades. Diverging responses to a global perturbation create a conservation planning dilemma: if specialists have opposing responses to SLR, it may be difficult to manage conservation areas that accommodate many species.

Introduction The effects of sea level rise (SLR) on coastal ecosystems varies depending on tidal range, geomorphic setting and previous disturbances, but it is clear that the current rate of SLR will make it difficult for coastal ecosystems to respond rapidly by transgressing upland, and coastal habitat will continue to be lost, likely at an accelerating rate (Nicholls, Hoozemans & Marchand, 1999; Craft et al., 2009; Nicholls & Cazenave, 2010). Salt marshes are coastal ecosystems that may be particularly at risk to changes from SLR, as they occur within a relatively narrow band of salinity and tidal range (Craft et al., 2009). Salt marshes have low biological diversity as few species have adapted to the conditions created by these strong environmental gradients (Odum, 1988), and many species that live in salt marshes are specialists (Greenberg & Maldonado, 2006). A large proportion of those specialists are already threatened by habitat loss, pollution and habitat degradation from nearby development (Benscoter et al., 2013; Wiest, Shriver & Messer, 2014). Under these conditions (low species diversity, specialization to strong environmental gradients, potential population vulnerability due to pre-existing threats), would we expect 20

specialists to respond in the same way to a strong perturbation such as SLR? Or will some specialists be more sensitive to changes than others? The answers to these questions will have consequences for the management of salt marshes and the protection of atrisk species. In cases where species are predicted to have similar responses to disturbances, conservation decisions can be relatively straightforward. Surrogate species can be monitored as indicators for entire communities, and reserves can be designed around projections of future habitat availability (Caro & O’Doherty, 1999). But in ecosystems where species are predicted to have different or opposing responses to disturbances, conservation planning may be considerably more challenging. Divergent responses to climate change are likely to be more common than previously thought, especially when individual species and guilds are considered at ecosystem-specific scales (Cook, Wolkovich & Parmesan, 2012). For instance, Tingley et al. (2012) found that bird species in the Sierra Nevada Mountains of California have already responded to climate change by migrating along mountain slopes, but some species have migrated downslope, contrary to global predictions of upslope migrations that track temperature (Root et al., 2003). Salt marsh plant communities will Animal Conservation 20 (2017) 20–28 ª 2016 The Zoological Society of London

E. A. Hunter, N. P. Nibbelink and R. J. Cooper

likely track changes in relative elevation as SLRs, with some marsh loss in areas where accretion cannot keep pace with inundation (Craft et al., 2009; Kirwan & Megonigal, 2013), but less is known about how salt marsh animals will respond to those corresponding changes in the plant community. Clapper rails Rallus crepitans and seaside sparrows Ammodramus maritimus are salt marsh endemic bird species that are both potentially vulnerable to habitat changes (e.g. fragmentation) from SLR (Hunter et al., 2015). Both species are resident within South Atlantic salt marshes throughout the year and they both have small (1.5 m) within a 50-m radius of the point. Total cover of two dominant plant species, S. alterniflora and J. roemarianus, were strongly correlated with one of the landscape gradients (see below), the proportion brackish gradient (correlation coefficient of 0.75 and 0.76, respectively); therefore, plant cover was not included in abundance and occupancy models.

Landscape gradients To estimate species responses to environmental features and project those distributions using an SLR model, we generated landscape gradients using raster map outputs (28 m resolution) from the Sea Level Affecting Marshes Model [SLAMM; (Clough, Park & Fuller, 2010)]. SLAMM was run with 2007 National Wetland Inventory wetland distribution data and a Light Detection and Ranging Digital Elevation Model (LiDAR DEM) that was collected in 2008–2010 at a 1.2-m resolution (Hunter et al., 2015). The distribution of wetlands in SLAMM is based on empirical ranges of elevations and salinities at which certain wetland types exist. Under SLR conditions, SLAMM decreases wetland elevations while sea level remains constant (thus, elevations are always relative to sea level). As elevations decrease, wetlands can transition to different cover types (such as tidal flats) or open water (Clough et al., 2010). We used SLAMM 22


wetland cover and relative elevation raster outputs from five time periods: the current time period (2007), and four projected time periods (2025, 2050, 2075 and 2100). We used an SLR scenario of 1-m rise from 2007 to 2100. Because sampling points were separated by 400 m, landscape gradients were measured within the 200-m radius circle that separated each sampling point from all others. We created all gradients using the combined salt and brackish marsh land cover types from the SLAMM dataset (corresponding to the E2EM1N and E2EM1P wetland types from the National Wetland Inventory). We created two gradients, edge density and proportion of landscape, with FRAGSTATS (McGarigal, Cushman & Ene, 2012) using a moving window analysis with a 4-neighbor rule (which is better than the 8-neighbor rule at identifying smaller patches) and a 200-m radius circular window. Relative Elevation was the average elevation (meters above mean tide level) of the LiDAR DEM within a 200-m radius of the point. Proportion brackish was the proportion of total salt marsh cells within 200 m of the point that were classified as brackish marsh. Patch area was the area of the contiguous patch of marsh (using 4-neighbor rule) that a point was located on. Distance to forest and distance to development were calculated using the Euclidian distance algorithm in ArcGIS 10.1 (ESRI, Redlands, CA, USA). All gradients were uncorrelated (|r| < 0.5). Each landscape gradient had a hypothesized relationship with the occupancy and abundance of each species, as well as a likely shift in average value as SLRs (Table 1, Fig. 2).

Parameter estimation We used different models for estimating the effects of landscape gradients on distributions of the two species. Clapper rails are known to occupy nearly all marshes within our study area (Nuse et al., 2015); therefore, we only needed to model the effects of landscape gradients on abundance for this species using a simple Poisson abundance model (Kery & Schaub, 2012; p. 398). Seaside sparrows do not have such high occupancy rates; therefore, we estimated effects of landscape gradients on both occupancy and abundance by using a zero-inflated Poisson binomial N-mixture model (Kery & Schaub, 2012; p. 401). This model represented an abundance process using a Poisson distribution only for sites that were modeled as ‘suitable’ based on a binomial distribution of site occupancy. Landscape gradient effects were estimated for both the abundance and occupancy processes, as effects could be different for each process. Both clapper rail and seaside sparrow models were Bayesian mixed effects models. We used counts for each species as the response variable for the analysis. We considered each observer’s counts to be independent ‘visits’ to the site, which is valid because the observers did not cross-reference their observations and most detections were auditory and could be recorded with little to no unintended signaling to the other observer (Royle & Dorazio, 2008, p. 177). Thus, in 2013, there were six ‘visits’ to all sites, and in 2014, there were six ‘visits’ to 124 sites and three ‘visits’ to 90 sites. Animal Conservation 20 (2017) 20–28 ª 2016 The Zoological Society of London


Specialist response to sea level rise

Table 1 Hypothesized effects of landscape gradients on the occupancy and/or abundance of clapper rails and seaside sparrows surveyed on the Georgia, USA, coast in 2013–2014. Effects could be either positive or negative depending on how the species selects habitat. The third column indicates the predicted shift of each gradient’s average value in response to sea level rise (SLR). When the species effect sign and the SLR effect sign oppose each other, abundance will decline as SLRs

Edge density

Elevation

+

+

— — +

0.4

● ● ● ●

Animal Conservation 20 (2017) 20–28 ª 2016 The Zoological Society of London

●

●

Area

—

We modeled fixed effects of the landscape gradients on abundance and occupancy as a linear, multiple regression. We included random effects of site and year in both the abundance and occupancy model components. Within the detection submodel, we included fixed linear effects of observer, Julian date, tide, wind and noise, and also included a random site and visit effect. All landscape covariates were scaled so that most values fell between zero and one, and continuous detection covariates were centered (to improve model convergence). Categorical covariates of observer and tide for the detection submodel were modeled using the first observer and rising tide as the intercept effect. Thus, for each species, we used a single model that included all fixed and random effects. We estimated model parameters with Markov Chain Monte Carlo (MCMC) methods in WinBUGS (version 1.4) via the R2WinBUGS package in program R (version 3.1.2; R Core Team 2013). Uninformative Gaussian (for continuous covariates) and uniform (for categorical covariates) prior probability distributions were assigned to all parameters and three MCMC chains were initialized. We monitored parameters for 2 000 000 MCMC iterations, after an initial burn-in of 20 000 iterations, and retained values from every 10th iteration to reduce serial autocorrelation among MCMC samples. We visually confirmed convergence of the MCMC chains to a stationary posterior distribution. To assess the fit of our models to our data, we used a ‘Bayesian P-value,’ a measure of the discrepancy between the actual dataset and data simulated under estimated model parameters (Kery &

● ● ●

●

● Brackish Dist.Dev. –0.4

Patch area Proportion brackish Proportion of landscape

+

●

0.2

(+) Developed areas are sources of toxins and predators (+) Forests are a predator source (–) Forests offer wind/storm protection (+) Edges alter plant community composition favorably (–) More contiguous habitat, fewer access points for predators (+) Lower flooding probability (–) Taller grasses, more cover (+) Higher colonization probability (+) More brackish marsh (–) More salt marsh (+) More marsh (–) Marsh is on a larger channel, altering plant community

0.0

Distance to development Distance to forest

SLR effect Change from 2007 (proportion)

Species effect

●

–0.2

Gradient

●

Dist.For.

● Edge Elevation

● Prop.Land. 2007

2025

2050

2075

2100

Time (year)

Figure 2 Predicted changes from SLR to average landscape gradient values for Georgia coastal salt marshes. Predictions (years 2025, 2050, 2075 and 2100) are from the Sea Level Affecting Marshes Model using a 1-m rise in sea level. Proportion changes are from the baseline gradient value in the year 2007.

Schaub, 2012; p. 223). Values close to 0.5 indicate a wellfitting model. We also calculated the variance explained by covariates by comparing the random site effect estimates between models that included all covariates and models without covariates (Kery & Schaub, 2012, p. 189).

Distribution projections To determine the effects of SLR on these species from the present to 2100, we used posterior distributions of parameter estimates based on the relationship between our survey data and present-day (2007) landscape gradients to forecast species’ abundances. We first drew parameter estimates from the posterior distributions (including all three MCMC chains) of the full model (including all landscape gradient relationships, even those with zero mean effects, to account for all 23


uncertainties). For each draw (n = 100) from the posterior distributions, we multiplied the drawn parameter estimate by the corresponding landscape gradient raster and added together the effects according to the linear model. For clapper rails, for which we used a simple Poisson model, this represented the final abundance prediction. For seaside sparrows, we did this once for the Poisson abundance part of the model, and once for the binomial occupancy part of the model. The occupancy outputs were scaled 0–1 using a logit transformation, and then the two outputs (occupancy and abundance) were multiplied together so that the seaside sparrow abundance forecast reflected occupancy as well. We then averaged the set of 100 rasters (from 100 draws from posterior distributions) to produce a final mean predicted abundance raster for each species that incorporated uncertainty in parameter estimates. We repeated this process for each year within the SLAMM dataset. For present-day predictions (year 2007 in SLAMM), we assigned raster pixels as high abundance if they were within the top quartile of the range of predicted abundance values. We then used the abundance top quartile cutoff value from 2007 as the high abundance cutoff value for all subsequent prediction years in SLAMM. To determine which gradients had the greatest effect on species’ distributions, we performed a sensitivity analysis by increasing and decreasing each mean estimated parameter value by 5% and examining the per cent change in high abundance area (in km2) for SLAMM year 2007. We then measured changes to each species’ high abundance area over time by projecting mean gradient effects to the four projection years in SLAMM (2025, 2050, 2075 and 2100).


Table 2 Effects of seven landscape gradients on the abundance and occupancy of clapper rails and seaside sparrows in the years 2013–2014, and the effects of five covariates on the detection probability of those species. Effects were estimated using a Bayesian hierarchical N-mixture model. Effect means and 95% credible intervals are reported

Effect Abundance Dist. dev. Dist. for. Edge Elevation Patch area Prop. brack. Prop. lands. Occupancy Dist. dev. Dist. for. Edge Elevation Patch area Prop. brack. Prop. lands. Detection Date Noise Observera Tideb Wind

Clapper rail

Seaside sparrow

Mean

Mean

0.02 0.52 0.18 1.02 0.10 0.66 0.26

0.27 0.17 0.56 0.63 0.44

95% C.I. ( 0.74, 0.77) ( 0.77, 0.29) ( 0.51, 0.15) ( 2.09, 0.04) ( 0.07, 0.28) (0.36, 0.96) ( 0.92, 0.40)

( 0.39, 0.15) (0.05, 0.30) ( 0.96, 0.15) ( 1.06, 0.19) ( 0.55, 0.31)

95% C.I.

0.02 0.10 0.58 2.92 0.45 1.15 0.12

( 1.18, 1.12) ( 0.17, 0.39) (0.04, 1.12) (1.15, 4.82) (0.19, 0.71) ( 1.72, 0.60) ( 0.87, 1.09)

9.19 16.46 3.96 8.43 0.78 5.13 0.60

(1.20, 18.68) (9.09, 27.63) ( 9.53, 0.12) ( 2.34, 18.68) ( 1.23, 3.30) ( 9.44, 1.87) ( 8.05, 6.62)

0.05 0.18 0.96 0.51 0.08

( ( ( ( (

0.01, 0.11) 0.27, 0.09) 0.50, 1.44) 0.82, 0.20) 0.17, 0.01)

a

Current species distributions

Maximum difference between observers. Maximum difference between tides. For clapper rails, high and rising tides had highest detection probability and falling tide had lowest detection probability. For seaside sparrows, rising and low tides had the highest detection probabilities, and falling tide had the lowest detection probability.

Clapper rails occupied 92% (196/214) of the survey points, but two relationships between landscape gradients and clapper rail abundance had 95% credible intervals that did not cross zero (Table 2, Fig. 3a). The proportion of brackish marsh had a positive effect on clapper rail abundance, indicating greater abundances in brackish marshes compared to salt marshes. Distance to forest had a negative effect on abundance (marshes closer to forests had higher abundances). Although effects of the other gradients had credible intervals that crossed zero, relative elevation, proportion of landscape and edge density were more likely to have negative effects, and patch area was more likely to have a positive effect (based on majority of posterior distribution density). Clapper rails were less detectable at higher wind speeds, but more detectable at higher noise levels (Table 2). Observer differences were minimal, with only one observer having a lower detection rate than the other five observers. Clapper rails were more detectable earlier in the breeding season and at high and rising tides. The Bayesian P-value of 0.41 indicated that the combined abundance and detection model fit the data well. An estimated 15.1% of the variation in abundance was explained by the covariates (the random site

effect was reduced from 0.53 to 0.45 by the addition of the landscape gradients to the abundance model). Three relationships between landscape gradients and seaside sparrow occupancy, and two relationships with abundance, had 95% credible intervals that did not cross zero (Table 2, Fig. 3b). Seaside sparrow occupancy was greater at sites farther from forests and developed areas and had less freshwater influence. Proportion brackish marsh had a negative effect on seaside sparrow abundance, and higher relative elevation sites had higher seaside sparrow abundances (Table 2, Fig. 3b). Seaside sparrow detection varied among observers, with the observer that was maintained between the 2 years of sampling having the highest detection probability compared to the other five observers (Table 2). Noise levels had a negative effect on detection probability, and seaside sparrows were less detectable in falling tides. The Bayesian P-value of 0.57 showed that the combined occupancy, abundance and detection model fit the data well. An estimated 45.2% of the variation in occupancy was explained by the covariates (the random site effect for occupancy was reduced from 7.77 to 4.26 by the addition of

b

Results

24



Dist. Dev.


(a)

(b)

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Dist. For.

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Edge

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Elevation

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Patch Area

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Prop. Brack.

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Prop. Land. –2.0

–1.0

● Occupancy

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0.0

0.5

1.0

Effect Size

–10

0

10

20

Effect Size

Figure 3 Estimated effects of landscape gradients on the distribution of (a) clapper rails and (b) seaside sparrows in Georgia coastal salt marshes in 2013–2014. Effects were estimated using a Bayesian hierarchical N-mixture model. Effect means and 95% credible intervals are shown.

the landscape gradients to the occupancy model); 10.0% of the abundance variance was explained by the covariates (the random site effect was reduced from 0.64 to 0.57). When the clapper rail model was applied back to the landscape gradients for the year 2007, the top quartile threshold value (the cutoff used to determine areas that had high abundance) was 1.04 individuals per 200-m radius area. The cutoff value for high seaside sparrow abundance was 1.22. The amount of high abundance area for both species was most sensitive to changes in species’ responses to the relative elevation gradient (Fig. 4), despite considerable uncertainty in the relationship between relative elevation and clapper rail abundance and seaside sparrow occupancy (Fig. 3).

Projected distributions under SLR When the clapper rail model was applied to future SLAMM rasters, high clapper rail abundance area increased substantially over time (Fig. 5). By the year 2100, if habitat relationships remain constant, the predicted area of high clapper rail abundance will increase by 52.1% (Fig. 5). In contrast, the predicted area of high seaside sparrow abundance will decline by 81.4% by 2100 (Fig. 5). The distribution of high clapper rail abundance areas did not overlap with high seaside sparrow abundance areas. Only 2.7% of the seaside sparrow’s high abundance areas in 2007 overlapped the distribution of high clapper rail abundance, and this overlap remained small throughout all SLAMM years (Fig. 5).

Discussion Salt marsh plant communities will simultaneously migrate and disappear as sea levels rise (Kirwan & Megonigal, 2013), producing shifts in the distributions of specialized organisms that inhabit coastal wetlands (Brittain & Craft, 2012; Hunter et al., 2015), but little is known about how much we can expect specialists’ shifts to strictly follow those Animal Conservation 20 (2017) 20–28 ª 2016 The Zoological Society of London

of the plant community. Contrary to our predictions that clapper rails and seaside sparrows would respond similarly to predicted habitat changes from SLR, we found that the two species’ distributions of high abundance areas will likely diverge substantially in the next 100 years. Before 2025, neither species will experience losses or gains in high abundance areas, but sometime between 2025 and 2050, areas supporting high clapper rail abundance will increase and areas of high seaside sparrow abundance will decline precipitously. Such divergent responses to a strong ecosystem disturbance has rarely been reported for ecosystem specialists; for instance, specialized bird species have been found to respond more negatively to habitat fragmentation than generalists (Devictor, Julliard & Jiguet, 2008), and specialist bird species respond similarly to intense farming practices (Filippi-Codaccioni et al., 2010). The degree of specialization for clapper rails and seaside sparrows should be considered extreme: in Georgia, these are the only two bird species that breed solely in tidal salt marshes [one other species, the Marsh Wren Cistohorus palustris, has a subspecies (griseus) that is restricted to tidal marshes], whereas inland freshwater marshes in the state have at least an order of magnitude more bird species that are specialized to those habitats. Therefore, divergent responses to a broad-scale disturbance like SLR is remarkable for these two extreme specialists. Although the species’ distributions differed across several of the landscape gradients, relative elevation was the primary driver of their divergent responses to SLR, both because of the species’ sensitivity to small changes in this gradient (Fig. 4) and because relative elevation will undergo the largest proportional change in its mean values compared to the other gradients (Fig. 2). The decrease in relative elevation means that wetlands will not simply be lost to SLR (by transitioning to open water), but that low elevation marshes will overtake high elevation marshes (with a corresponding transition in the plant community). The decrease in the relative elevation gradient also means that high elevation marshes are not able to migrate 25



(a)

(b)

Dist. Dev.

Dist. For.

Edge

Elevation

Patch Area Abund./Inc. Prop. Brack.

Occ./Inc. Abund./Dec. Occ./Dec.

Prop. Land.

−0.15 −0.10 −0.05

0.00

0.05

0.10

Change in high abundance area

0.15

−0.15 −0.10 −0.05

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Change in high abundance area

●

●

300

400

500

Clapper rail

200

●

Seaside sparrow ●

100

Habitat Area (square km)

600

700

Figure 4 Sensitivity of (a) clapper rail and (b) seaside sparrow distributions to small changes (5%) in their responses to seven landscape gradients. Black bars represent changes in abundance parameters, and gray bars changes in occupancy parameters, where dashed bars are 5% increases, and solid bars are 5% decreases in the parameter value. Sensitivity is represented as the proportion change in high abundance area.

Overlap

0

●

2007

2025

2050

2075

2100

Time (year) Figure 5 Predicted changes in the amount of high abundance habitat for clapper rails and seaside sparrows in coastal Georgia marshes due to SLR. The amount of overlap between high abundance areas for these species is based on estimates of their distributions in 2013–2014. Habitat change predictions (years 2025, 2050, 2075 and 2100) are from the Sea Level Affecting Marshes Model using a 1-m rise in sea level.

vertically due to man-made impediments such as sea walls and bulk heads, as well as the slow transition time from flooded uplands to high marshes (Nicholls et al., 1999; Woodrey et al., 2012). This outcome benefits clapper rails, which were more abundant in lower elevation marshes, and is a detriment to seaside sparrows, which were more abundant in higher elevation marshes. Proposals to accelerate accretion rates and maintain

26

marsh elevation include spraying sediment in particularly vulnerable areas (Ford, Cahoon & Lynch, 1999), but the expansiveness of Georgia salt marshes will likely make such efforts infeasible at large scales. In addition to elevation, the species differed in their responses to the distance to forest gradient, with clapper rails being more abundant closer to forests, and seaside sparrows completely avoiding marshes that are close to forests. We conjecture that differences in habitat selection along these two gradient (elevation and distance to forest) stems from how each species approaches trade-offs to nesting success and adult survival. Relative elevation affects flooding probability of nests, but so can proximity to uplands. Wind speed and direction often make the difference between moderate and extreme high tides (Bunya et al., 2010). Nesting closer to wind-deflecting uplands may reduce flooding risk but increase predation risk as forests are predator sources (Picman, Milks & Leptich, 1993; Greenberg et al., 2006). Understanding how nesting birds navigate these trade-offs (and thus why these habitat selection patterns arise) will require long-term studies on nest success and adult survival across elevation and distance to forest gradients. The habitat associations we found will likely make seaside sparrows more vulnerable to changes from SLR than clapper rails. Our models show that seaside sparrows may lose over 80% of their high abundance habitat in the next 100 years. Although SLAMM projections out to 2100 are full of uncertainties, as well as imprecisely modeled geologic processes (Clough et al., 2010; Chu-Agor et al., 2011), it is clear that some substantial portion of seaside sparrow habitat will be lost, simply due to the species’ relationship with relative elevation (which is directly related to sea level). Other studies of seaside sparrow population viability have shown that substantially less habitat loss from SLR than we have predicted could still increase population extinction probability by more than 50% (Shriver & Gibbs, 2004; Kern & Shriver, 2014). Animal Conservation 20 (2017) 20–28 ª 2016 The Zoological Society of London


However, these studies reported results based on loss of total marsh habitat, and so population extinction probabilities could be even more dire if areas of high population density are more likely to be lost than low density areas. The question of how population density relates to productivity needs to be investigated, as this will obviously affect population sizes. Our models show a decline in high abundance areas, but if low abundance areas are more productive than high abundance areas, then population size may not be as affected by SLR. However, a Gulf coast population of seaside sparrows had higher nest survival rates in high density areas (Lehmicke, 2014), so available evidence indicates that loss of high abundance areas would be detrimental to population size. Although clapper rail habitat area declines in the final prediction period (as a function of overall tidal marsh loss), we still predicted this species to have over 50% more high abundance breeding habitat in the year 2100 than it does currently. It is clear that there will be ‘winner’ species under conditions of global change (McKinney & Lockwood, 1999), and the clapper rail may be considered a winner when compared to the seaside sparrow in the next 100 years. But if SLR trends continue to accelerate in future centuries, as they are likely to do without drastic emission mitigations (Horton et al., 2014), clapper rails may also lose much of their preferred habitat. Divergent responses to SLR by these two salt marsh specialists pose conservation planning problems. Our result that salt marsh specialist species will have divergent responses to SLR makes it difficult to select areas for protection that may function as high quality habitat for a variety of species as marsh area is lost to SLR. A potential solution to this problem that has recently gained traction is to preserve areas that are likely to support high levels of biodiversity under new climatic regimes (Anderson & Ferree, 2010; Barrett, Nibbelink & Maerz, 2014; Beier, Hunter & Anderson, 2015). Locations that have higher geological and topographic heterogeneity have higher current biodiversity levels, and are predicted to be more resilient to climate change (Anderson & Ferree, 2010; Lawler et al., 2015). It is unclear how such tactics would work in low-lying coastal ecosystems, but preserving areas that contain a diversity of the landscape gradient values that we studied would be likely to capture habitat for both clapper rails and seaside sparrows.

Acknowledgements This research was funded by the United States Geological Survey’s Patuxent Wildlife Research Center through the South Atlantic Landscape Conservation Cooperative, the Georgia Department of Natural Resources (GADNR), and the United States Department of Agriculture National Institute of Food and Agriculture McIntire Stennis project GEOZ-0146-MS. E.A.H. was supported by a University of Georgia PhD Scholars of Excellence Assistantship. We thank K. Gillman, R. Guy, A. Mankofsky, L. Mengak, and J. Nelson for assistance with fieldwork, C. Alexander and J. Clough for access to SLAMM data, and T. Schneider and Animal Conservation 20 (2017) 20–28 ª 2016 The Zoological Society of London


the Warnell School graduate student writing group for helpful comments. All fieldwork was permitted by the GADNR and approved by the University of Georgia Institutional Animal Care and Use Committee.

References Anderson, M.G. & Ferree, C.E. (2010). Conserving the stage: climate change and the geophysical underpinnings of species diversity. PLoS ONE 5, e11554. Barrett, K., Nibbelink, N.P. & Maerz, J.C. (2014). Identifying priority species and conservation opportunities under future climate scenarios: amphibians in a biodiversity hotspot. J. Fish Wildlife Manage. 5, 282–297. Beier, P., Hunter, M.L. & Anderson, M. (2015). Special section: conserving nature’s stage. Conserv. Biol. 29, 613–617. Benscoter, A.M., Reece, J.S., Noss, R.F., Brandt, L.A., Mazzotti, F.J., Roma~nach, S.S. & Watling, J.I. (2013). Threatened and endangered subspecies with vulnerable ecological traits also have high susceptibility to sea level rise and habitat fragmentation. PLoS ONE 8, e70647. Brittain, R.A. & Craft, C.B. (2012). Effects of sea-level rise and anthropogenic development on priority bird species habitats in coastal Georgia. USA. Environ. Manage. 49, 473–482. Bunya, S., Dietrich, J.C., Westerink, J.J., Ebersole, B.A., Smith, J.M., Atkinson, J.H., Jensen, R., Resio, D.T., Luettich, R.A., Dawson, C., Cardone, V.J., Cox, A.T., Powell, M.D., Westerink, H.J. & Roberts, H.J. (2010). A high-resolution coupled riverine flow, tide, wind, wind wave, and storm surge model for southern Louisiana and Mississippi. Part I: model development and validation. Mon. Weather Rev. 138, 345–377. Caro, T.M. & O’Doherty, G. (1999). On the use of surrogate species in conservation biology. Conserv. Biol. 13, 805–814. Chu-Agor, M.L., Mu~noz-Carpena, R., Kiker, G., Emanuelsson, A. & Linkov, I. (2011). Exploring vulnerability of coastal habitats to sea level rise through global sensitivity and uncertainty analyses. Environ. Model. Softw. 26, 593–604. Clough, J.S., Park, R.A. & Fuller, R. (2010). SLAMM 6 beta technical documentation: release 6.0.1 beta. Waitsfield: Warren Pinnacle Consulting, Inc. Conway, C.J. (2011). Standardized North American marsh bird monitoring protocol. Waterbirds 34, 319–346. Cook, B.I., Wolkovich, E.M. & Parmesan, C. (2012). Divergent responses to spring and winter warming drive community level flowering trends. P. Natl. Acad. Sci. USA 109, 9000–9005. Craft, C., Clough, J., Ehman, J., Joye, S., Park, R., Pennings, S., Guo, H. & Machmuller, M. (2009). Forecasting the effects of accelerated sea-level rise on tidal marsh ecosystem services. Front. Ecol. Environ. 7, 73–78. Devictor, V., Julliard, R. & Jiguet, F. (2008). Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation. Oikos 117, 507–514. Filippi-Codaccioni, O., Devictor, V., Bas, Y., Clobert, J. & Julliard, R. (2010). Specialist response to proportion of 27


arable land and pesticide input in agricultural landscapes. Biol. Conserv. 143, 883–890. Ford, M.A., Cahoon, D.R. & Lynch, J.C. (1999). Restoring marsh elevation in a rapidly subsiding salt marsh by thin-layer deposition of dredged material. Ecol. Eng. 12, 189–205. Gjerdrum, C., Elphick, C.S. & Rubega, M. (2005). Nest site selection and nesting success in saltmarsh breeding sparrows: the importance of nest habitat, timing, and study site differences. Condor 107, 849–862. Greenberg, R. & Maldonado, J.E. (2006). Diversity and endemism in tidal-marsh vertebrates. S Stud. Avian Biol. 32, 32–53. Greenberg, R., Elphick, C., Nordby, J.C., Gjerdrum, C., Spautz, H., Shriver, G., Schmeling, B., Olsen, B., Marra, P., Nur, N. & Winter, M. (2006). Flooding and predation: trade-offs in the nesting ecology of tidal-marsh sparrows. Stud. Avian Biol. 32, 96–109. Horton, B.P., Rahmstorf, S., Engelhart, S.E. & Kemp, A.C. (2014). Expert assessment of sea-level rise by AD 2100 and AD 2300. Quaternary Sci. Rev. 84, 1–6. Hunter, E.A., Nibbelink, N.P., Alexander, C.R., Barrett, K., Mengak, L.F., Guy, R.K., Moore, C.T. & Cooper, R.J. (2015). Coastal vertebrate exposure to predicted habitat changes due to sea level rise. Environ. Manage. 56, 1528–1537. Kern, R.A. & Shriver, W.G. (2014). Sea level rise and prescribed fire management: implications for seaside sparrow population viability. Biol. Conserv. 173, 24–31. Kery, M. & Schaub, M. (2012). Bayesian population analysis using WinBUGS: a hierarchical perspective. Waltham: Academic Press. Kirwan, M.L. & Megonigal, J.P. (2013). Tidal wetland stability in the face of human impacts and sea-level rise. Nature 504, 53–60. Lawler, J.J., Ackerly, D.D., Albano, C.M., Anderson, M.G., Dobrowski, S.Z., Gill, J.L., Heller, N.E., Pressey, R.L., Sanderson, E.W. & Weiss, S.B. (2015). The theory behind, and the challenges of, conserving nature’s stage in a time of rapid change. Conserv. Biol. 29, 618–629. Lehmicke, A.J.J. (2014). Breeding ecology of the Seaside sparrow (Ammodramus maritimus) in northern Gulf of Mexico tidal salt marshes. PhD Dissertation, University of Georgia. McGarigal, K., Cushman, S.A. & Ene, E. (2012). FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Available at: http:// www.umass.edu/landeco/research/fragstats/fragstats.html McKinney, M.L. & Lockwood, J.L. (1999). Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453. Nicholls, R.J. & Cazenave, A. (2010). Sea-level rise and its impact on coastal zones. Science 328, 1517–1520. Nicholls, R.J., Hoozemans, F.M.J. & Marchand, M. (1999). Increasing flood risk and wetland losses due to global sealevel rise: regional and global analyses. Global Environ. Chang. 9, 69–87. Nuse, B.L., Cooper, R.J. & Hunter, E.A. (2015). Prospects for predicting changes to coastal wetland bird populations due to accelerated sea level rise. Ecosphere 6, 286.

28


Odum, W.E. (1988). Comparative ecology of tidal freshwater and salt marshes. Annu. Rev. Ecol. Syst. 19, 147–176. Picman, J., Milks, M.L. & Leptich, M. (1993). Patterns of predation on passerine nests in marshes: effects of water depth and distance from edge. Auk 110, 89–94. Post, W. (1974). Functional analysis of space-related behavior in the seaside sparrow. Ecology 55, 564–575. R Core Team. (2013). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Available at: http://www.R-project.org/ Ricketts, C.E. (2011). Multi-scale analyses of habitat selection and demographic consequences for South Carolina Clapper rails (Rallus longirostris). Master’s thesis, University of Georgia. Root, T.L., Price, J.T., Hall, K.R., Schneider, S.H., Rosenzweig, C. & Pounds, J.A. (2003). Fingerprints of global warming on wild animals and plants. Nature 421, 57–60. Royle, J.A. & Dorazio, R.M. (2008). Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations, and communities. Waltham: Academic Press. Shriver, W.G. & Gibbs, J.P. (2004). Seaside sparrows (Ammodramus maritimus) in Connecticut: projected effects of sea-level rise. In Species conservation and management case studies: 397–409. Akcakaya, R.H., Burgman, M.A., Kindvall, O., Wood, C.C., Sjogren-Gulve, P., Hatfield, J.S. & McCarthy, M.A. (Ed.). Oxford: Oxford University Press. Shriver, W.G., Hodgman, T.P., Gibbs, J.P. & Vickery, P.D. (2004). Landscape context influences salt marsh bird diversity and area requirements in New England. Biol. Conserv. 119, 545–553. Tingley, M.W., Koo, M.S., Moritz, C., Rush, A.C. & Beissinger, S.R. (2012). The push and pull of climate change causes heterogeneous shifts in avian elevational ranges. Glob. Change Biol. 18, 3279–3290. US Fish and Wildlife Service (USFWS). (2007). National Wetlands Inventory website. Washington: US Department of the Interior, Fish and Wildlife Service. Available at: http:// www.fws.gov/wetlands/ Wiest, W.A., Shriver, W.G. & Messer, K.D. (2014). Incorporating climate change with conservation planning: a case study for tidal marsh bird conservation in Delaware, USA. J. Conserv. Plan. 10, 25–42. Woodrey, M.S., Rush, S.A., Cherry, J.A., Nuse, B.L., Cooper, R.J. & Lehmicke, A.J.J. (2012). Understanding the potential impacts of global climate change on marsh birds in the Gulf of Mexico region. Wetlands 32, 35–49.

Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Appendix S1. Power analysis to select sampling points.
