Prospects for predicting changes to coastal ... - Wiley Online Library

Prospects for predicting changes to coastal wetland bird populations due to accelerated sea level rise BRYAN L. NUSE,1, ROBERT J. COOPER,

AND

ELIZABETH A. HUNTER

Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602 USA Citation: Nuse, B. L., R. J. Cooper, and E. A. Hunter. 2015. Prospects for predicting changes to coastal wetland bird populations due to accelerated sea level rise. Ecosphere 6(12):286. http://dx.doi.org/10.1890/ES15-00385.1

Abstract. Accelerating sea level rise (SLR) is likely to cause considerable changes to estuarine and other coastal wetlands. Efforts to forecast the effects of SLR on coastal wetland vegetation communities should be useful in making predictions for individual species that depend upon those communities. However, considerable uncertainty exists when predicting a chain of events that passes from the global climate to local effects to implications for a single species. One component of this uncertainty is the classification resolution used by SLR landscape change models such as the Sea Level Affects Marshes Model (SLAMM). To isolate and assess the effects of this kind of uncertainty on species-level SLR prediction, we analyzed surveys of birds and plants in the lower Altamaha River and its estuary in Georgia, USA. For 19 marsh and forest bird species, we tested the predictive value of three classes of covariates of site occupancy: (1) fieldmeasured habitat variables and spatial information, (2) information available from a SLAMM map, including the spatial configuration of the SLAMM habitat classes, and (3) SLAMM habitat class alone. We found that the predictive ability of occupancy models built from these three kinds of information varies widely among species. We therefore suggest criteria for classifying species according to the amount of detail necessary to describe their habitat niche, and thus to maximize the accuracy of predictive models. We point out that for species with habitat requirements that can be represented well by SLAMM classes, such as the Clapper Rail, forecasts of SLR-induced population change are probably feasible. For species with more narrow habitat needs, however, such as the Seaside Sparrow, reasonable predictions of SLR effects may not be possible without further refinement of SLR landscape change models. We suggest that improved thematic resolution of such models should be a priority, if the implications of SLR models for individual species are to be ascertained fully. Key words: Altamaha River estuary, Georgia, USA; Bayesian occupancy model; Clapper Rail (Rallus longirostris); coastal wetlands; habitat niche; k-fold cross-validation; Least Bittern (Ixobrychus exilis); marsh birds; model predictive accuracy; sea level rise; Seaside Sparrow (Ammodramus maritimus); separation in logistic regression. Received 17 June 2015; revised 21 August 2015; accepted 28 August 2015; published 21 December 2015. Corresponding Editor: D. P. C. Peters. Copyright: Ó 2015 Nuse et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/3.0/ 1

Present address: Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural

Resources, University of Georgia, Athens, Georgia 30602 USA. E-mail: [email protected]

INTRODUCTION

plausible (Jevrejeva et al. 2012, Schaeffer et al. 2012). This represents a global rate of rise unprecedented in historical time (Cronin 2012), and a significant potential driver of major alterations to coastal wetland ecosystems (Morris

Eustatic sea level is rising worldwide (Solomon et al. 2007), and recent modeling efforts predict that a total rise of 1 meter by the year 2100 is v www.esajournals.org

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et al. 2002, Cahoon et al. 2006, DeSantis et al. 2007). From a physical perspective, sea level rise (SLR) prediction is rife with uncertainty that surrounds many loosely related processes over a huge range of spatial scales (Solomon et al. 2007). To predict the manner in which particular species or biotic communities will respond to SLR, an estimate of the local SLR forecast curve is first required, and this estimate necessarily masks the underlying physical uncertainties. The approach taken by several recent SLR landscape change modeling efforts (Craft et al. 2009, Glick et al. 2013) is to consider a number of different scenarios corresponding to alternative local SLR curves. Given a particular SLR scenario, landscape change models (LCMs) such as the Sea Level Affects Marshes Model (SLAMM 6; Mcleod et al. 2010, Clough et al. 2014) attempt to predict changes in the spatial extent and configuration of various landcover classes, including coastal wetland types corresponding to particular biotic communities. Such predictions have the potential to be distinctly useful in assessing the impact of SLR on coastal biotic communities and particular species or guilds. However, considerable uncertainty exists as to the manner in which coastal wetlands will react to various rates of SLR, and SLAMM has until recently provided no direct method of combining and propagating uncertainty from its input datasets (Chu-Agor et al. 2011, Clough et al. 2012). These inputs include information on the local environmental niche breadths for wetland communities, expected sedimentation and accretion rates and patterns, and a simple water salinity model. In the interest of realism, SLAMM also incorporates regional processes such as storm frequency and river discharge that can affect erosion and sedimentation rates and structure wetland communities, but which carry their own sizable uncertainties in the face of climate change. To use an SLR LCM such as SLAMM, then, is to accept a complex, multi-layered system of implicit uncertainty that is difficult to visualize or quantify. Here, we acknowledge the difficulties inherent to developing a model of future landscape configuration under SLR, but we do not treat them any further. Instead we focus on an additional problem that arises when attempting v www.esajournals.org

to make predictions for populations of particular species, based upon SLR LCM output. This issue is essentially a mis-match between SLR LCM output (a raster map with a small number of cover classes) and the input required for species distribution models (SDMs). SLAMM, for instance, constructs a predicted map of broad habitat types representing coalesced National Wetlands Inventory classes (Clough et al. 2012, 2014). These include ‘‘Regularly Flooded Marsh’’ (corresponding to saltmarsh), ‘‘Irregularly Flooded Marsh’’ (brackish marsh, approximately), Freshwater Marsh, and Tidal Forest. We contend that knowledge of the future conditions within coastal wetland systems is limited not only by (1) the uncertainty surrounding the pace of SLR and other aspects of global climate change, and (2) uncertainty regarding the reaction of wetland ecosystems to SLR, but also by (3) the coarseness of habitat classification used in SLR LCM models such as SLAMM. From a habitat niche perspective, the problem is that while some species’ realized niches (Hutchinson 1957, Pulliam 2000) can be sufficiently described by coarse landcover classes, others with more specific habitat requirements cannot. With this work, we attempt to classify species according to their realized niches, with reference to SLAMM cover classes and their spatial configuration on the landscape. Our main hypothesis was that species within the coastal wetland mosaic would vary in terms of how adequately the SLAMM classes could describe their realized niches. We therefore expected to find differences among species, when we compared the predictive value of SDMs built from variables based on (1) SLAMM cover class at survey sites, (2) spatial configuration of SLAMM classes on the landscape, and (3) detailed sitelevel environmental and vegetation measurements. We also posited that our ability to make reasonable predictions of occupancy from SLAMM maps would be impeded when species exhibit more specific habitat requirements than the SLAMM habitat classes can represent. Using surveys of breeding birds and their habitats in an estuary on the coast of Georgia, USA, we attempted to simulate a situation in which both field- and map-based variables are available for describing species’ habitat associations (i.e., model selection), but in which only 2


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patches of coarse landcover classes are available for prediction, in the form of a predicted future landscape. We first constructed site occupancy models fit within a Bayesian estimation framework, selecting important variables by a method unique to the Markov Chain Monte Carlo approach to Bayesian modeling. We then used cross-validation to compare the utility of mapbased covariates to predict species’ patterns of occurrence, against a suite of field-quantified vegetation and environmental variables. We identify species for which the favored predictors and models are map-based, field-based, or a mixture of both; and we generalize these results to discuss types of empirical habitat niches present among our study species. We also comment on the kinds of species for which SLR predictions could most benefit from improved resolution of habitat types under SLR LCMs. In our modeling exercise, we restrict the focus to the details of SLAMM, but the issues we raise would accompany the application of any SLR LCM model to the production of species-level predictions.

protected in places by barrier and back-barrier islands, and punctuated by raised hammocks wooded mainly with pine (Pinus spp.) or live oak (Quercus virginiana).

Sample design Our strategy in designing the survey was to cover the entire salinity gradient, and all the major wetland habitat types from the delta tidal forest to the saltmarsh behind Sapelo Island, the first major barrier island north of the Altamaha’s mouth (Fig. 1). We stratified the study region into approximately ‘‘fresh,’’ ‘‘brackish,’’ and ‘‘saline’’ areas and used generalized random-tesselation stratified (GRTS) sampling in the R package spsurvey (Kincaid and Olsen 2012) to develop a spatially balanced set of sites (Stevens and Olsen 2004). We ensured that the four major wetland habitats (see Methods: Study area) were represented approximately in proportion to their areal contribution to the study region. At each of the 18 identified survey sites (hereafter called ‘‘clusters’’), 6 points were chosen, such that they were all at least 400 m apart. In marshes, the points were all along waterways. In contiguous tidal forest, 3 of the 6 were at the water’s edge, and 3 were placed 200 m into the floodplain.

METHODS Study area The Altamaha River is one of the largest on the Atlantic coast of the USA, with a drainage area of about 36,000 km2 and a recent annual mean discharge of 420.8 m3/s (water years 2007–2011; USGS 2012). The Altamaha’s catchment lies entirely within the state of Georgia and the river is unregulated, although its two major tributaries do have major mainstem dams. As the river nears the coast, its delta supports a large area of tidal cypress-gum forest (Taxodium spp. and Nyssa biflora), the floodplain here expanding to a total width .10 km. The river then braids into a mosaic of marsh habitats interspersed with patches of tidal forest, gradually shifting in character with the increasing influence of water salinity. Moving generally seaward, freshwater marsh (dominated by cutgrass, Zizania miliacea) grades into brackish marsh (characterized by big cordgrass, Spartina cynosuroides), giving way ultimately to saltmarsh (dominated by smooth cordgrass, Spartina alterniflora). The saltmarsh complexes of the Altamaha estuary are extensive, ranging in width from about 7 to 12 km; they are v www.esajournals.org

Bird and vegetation surveys We attempted to visit each point 3 times during the 2010 breeding season; we missed 5 individual visits, making the total number of observations 319 ¼ (18 3 6 3 3) 5. All 6 points in a single cluster were surveyed each morning, beginning as soon after civil twilight as was feasible. Travel to points was by motorboat (or sometimes canoe). In marsh habitats, we generally followed the North American Marsh Bird Monitoring Protocol (Conway 2008), conducting surveys from the boat deck. Each count lasted 10 minutes, with the first 5 minutes as a passive, silent period, and the second half consisting of a playback sequence of bird calls separated by 30 seconds of silence. The calls played were those of the Black Rail, Least Bittern, King Rail and Clapper Rail, in that order (see Table 1 for scientific names). Playback was from two 4-inch speakers, pointed in opposite directions. In forested areas with no marsh, the full 10 min was devoted to passive listening. All counts were conducted by a single observer (BLN), and all 3


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Fig. 1. Map of the study area, showing survey sites, as well as the principal vegetation communities present. Note that the vegetation communities shown were derived from multiple sources by the Georgia Department of Natural Resources, and differ from the classification used by SLAMM.

Table 1. Bird species considered in the analysis, with raw prevalence rates in the SLAMM-classified habitat types. Prevalence Common name

Scientific name

Code

RF

IF

FM

TS

Total

Least Bittern King Rail Clapper Rail Clapper/King Rail Yellow-billed Cuckoo Red-bellied Woodpecker Downy Woodpecker Pileated Woodpecker Red-eyed Vireo Marsh Wren Prothonotary Warbler Common Yellowthroat Hooded Warbler Northern Parula Yellow-throated Warbler Seaside Sparrow Summer Tanager Red-winged Blackbird Boat-tailed Grackle

Ixobrychus exilis Rallus elegans Rallus longirostris Rallus sp. Coccyzus americanus Melanerpes carolinus Picoides pubescens Dryocopus pileatus Vireo olivaceus Cistothorus palustris Protonotaria citrea Geothlypis trichas Setophaga citrina Setophaga americana Setophaga dominica Ammodramus maritimus Piranga rubra Agelaius phoeniceus Quiscalus major

LEBI KIRA CLRA XXRA YBCU RBWO DOWO PIWO REVI MAWR PROW COYE HOWA NOPA YTWA SESP SUTA RWBL BTGR

0.12 0.02 0.95 0.20 0.07 0.10 ... 0.07 ... 0.93 ... 0.22 ... 0.07 0.15 0.76 0.07 0.59 0.92

0.64 0.36 1.00 0.96 ... 0.04 0.02 0.04 ... 0.68 ... 0.60 ... ... 0.04 0.44 ... 1.00 1.00

0.33 0.44 0.22 0.44 0.22 0.78 0.11 0.56 0.44 0.11 0.11 0.78 ... 0.78 0.89 ... 0.44 0.89 0.67

... 0.03 ... 0.06 0.67 0.91 0.42 0.76 0.72 0.03 0.42 0.36 0.21 0.97 0.79 ... 0.61 0.64 0.12

0.22 0.14 0.61 0.35 0.25 0.39 0.15 0.31 0.26 0.53 0.14 0.40 0.06 0.39 0.38 0.37 0.25 0.72 0.68

Notes: Raw prevalences are calculated as the number of points where the species was observed, divided by the total number of points in the SLAMM class: RF, regularly flooded; IF, irregularly flooded; FM, fresh marsh; TS, tidal swamp; Total, over the entire survey. Here, points are classified according to the SLAMM pixel identity at the point itself; this can result in counterintuitive patterns, such as woodpeckers in saltmarsh (they were actually in nearby forest stands).

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Fig. 2. Two-dimensional Non-metric Multidimensional Scaling (NMDS) plots of survey points. Polygons connect points within the same cluster. (A) Plant species cover is depicted. The red arrow labeled ‘‘salinity’’ represents the projection of the average water salinity variable onto the NMDS plane. (B) SLAMM class representation within a 100-m radius is depicted. Irreg Marsh ¼ irregularly flooded (;brackish) marsh; Reg Marsh ¼ regularly flooded (;salt) marsh.

individuals seen or heard were recorded, with no distance limit. Registrations from the 5-minute period before and after the formal count were also recorded. Because the present analysis focuses upon patterns of occupancy and not upon any estimate of density, we include all observations, not only those within the temporal bounds of the point count. Occupancy modeling is reportedly robust to the use of surveys of unequal duration and area (Royle and Dorazio 2008). Vegetation surveys were performed at a subset of points within each cluster. Within a set of standardized plots (called here topbank, 50 m2 in area; and circle, 314 m2) we estimated cover values for each species, canopy cover, and horizontal density. We also identified and measured the height of each stem within randomly placed 0.25-m2 quadrats. Our vegetation survey procedures are described fully in Appendix A, as are the methods used to impute values for vegetation metrics to un-surveyed points.

(Clough et al. 2012). Pixels at our survey points fell into four SLAMM classes, each subsuming several NWI classes: Regularly Flooded Marsh, Irregularly Flooded Marsh, Freshwater Marsh, and Tidal Forest (Table 1). We used the SLAMM map to generate a group of predictors describing landscapes at various scales surrounding our points (Table 2). We note that the task of discriminating habitats within Georgia’s expansive coastal marshes using remotely sensed data represents an ongoing challenge (Hladik et al. 2013).

Site ordinations To quantify and visualize differences between the manner in which points would be classified according to SLAMM landcover classes, versus field-measured plant community information, we performed two separate two-dimensional non-metric multidimensional scaling (NMDS) analyses in the R package vegan (Oksanen et al. 2012). The first characterized sites via the percent representation of each of the four SLAMM tidal wetland classes (saltmarsh, brackish marsh, fresh marsh, tidal forest) within a 100-m radius buffer (Fig. 2B). The second used the average across circles of the maximum cover values among all height strata within each circle (Fig. 2A; see

SLAMM map We reclassified a National Wetlands Inventory raster map (Cowardin et al. 1979, USFWS 2007) of our study area to produce a map appropriate for use as input by the SLAMM algorithm v www.esajournals.org

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NUSE ET AL. Table 2. Point-level predictor variables, for state process models. Category

Description

Mean

Range

rel_elev

Variable

field

3.04

[0, 9.87]

chan_width salinity

map field

124.3 9.7

[12, 680] [0.1, 31.7]

num_stems

field

48.1

[1.9, 240.8]

ht_mean

field

71.5

[6.2, 189.7]

region_sd

field

32.9

[1.6, 142.8]

total_len

field

59.9

[0.3, 282.8]

quad_spp circ_spp

field field

11.3 8.2

[1, 51] [1, 22]

max_strat

field

3.2

[2, 5]

shr_spp gro_spp water_pct dist_upl dist_for dist_dev slamm_1 slamm_2 comm_1 comm_2 salt

field field map map map map map map field field map

3.6 3.7 27.2 1.83 1.20 3.24 0 0 0.09 0.02 0.380

[1, 10] [1, 10] [0, 71.3] [0, 5.51] [0, 2.75] [0.12, 6.49] [55.2, 79.1] [44.6, 74.8] [2.76, 4.84] [1.54, 3.68] f0, 1g

brack

map

0.231

f0, 1g

fresh

map

0.083

f0, 1g

swamp

map

Wetland elevation (feet), relative to mean sea level, excluding channels wetted at mean low water, within 200 m radius. Produced using lidar. Channel width, in meters. Water salinity, in practical salinity units (psu), averaged over three visits to each point. Number of stems (live and dead), averaged over all quadrats within circles at each point. Mean height of stems (live and dead), over all quadrats at each point. Mean stem height was first calculated for each region at a point, then the standard deviation of these average values was taken. This expresses the region-level variation in vegetation height, in marshy habitats. Heights (in meters) of all stems were summed for each quadrat at a point; then the mean of these values was taken. This represents a rough index for above-ground biomass, in an average 0.25-m2 area. Average number of species appearing in quadrats. Average number of species appearing in circle cover class estimates (all height strata). Species must cover at least 1% of the 10 m radius circle, in at least one stratum, to be counted. Average of the tallest physiognomic stratum noted for the circle. Here, 1 ¼ ground, 2 ¼ shrub, etc. Total number of species observed in shrub strata of circles. Total number of species observed in ground strata of circles. Percentage of 200 m radius landscape that is water. Distance in km to the nearest 10 ha upland. Distance in km to nearest forested pixel. Distance in km to nearest pixel classified as developed. Scores along SLAMM NMDS axis 1. Scores along SLAMM NMDS axis 2. Scores along plant community NMDS axis 1. Scores along plant community NMDS axis 2. Binary design variable, indicating whether SLAMM class at the point was ‘‘regularly flooded.’’ Binary design variable, indicating whether SLAMM class at the point was ‘‘irregularly flooded.’’ Binary design variable, indicating whether SLAMM class at the point was ‘‘freshwater marsh.’’ Binary design variable, indicating whether SLAMM class at the point was ‘‘tidal swamp.’’

0.306

f0, 1g

Notes: All variables are continuous except the binary design variables ‘‘salt,’’ ’’brack,’’ ‘‘fresh,’’ and ‘‘swamp.’’

to 33 variables. All predictors were classified as either map-based, if they could be derived from a predictive map output from the SLAMM algorithm, or field-based, if they must be measured directly (Fig. 3). Relative elevations derived from lidar were included in the field-based set. Detection process covariates were measured at the level of individual observations or days (Table 4), except channel width. Initial detection process predictor sets were the same for each species, except that tide variables were not used for forest birds. All continuous covariates (for both state and detection process) were centered and scaled to have a standard deviation of 1.

Appendix A). Site scores from each ordination were then used as candidate predictors in occupancy models: slamm_1 and slamm_2 for the SLAMM axes, comm_1 and comm_2 for the plant community axes.

Initial predictor sets To facilitate comparison among species, we first chose a set of 24 core predictor variables that described habitat gradients spanning the entire study area (Table 2), to be used in the initial set of state process covariates for all bird species. Each bird species was also afforded a set of additional predictors based on its general habitat requirements (Table 3; Appendix C: Table C1; Poole 2013). Initial predictor sets ranged in size from 30 v www.esajournals.org

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NUSE ET AL. Table 3. Bird- or habitat-specific, point-level variables used in state process models for CLRA, SESP and LEBI. Category

Description

salt_pct

Variable

map

brack_pct

map

water_ed

map

ba_sum_live

field

Percentage of 100 m radius landscape that is ‘‘regularly flooded,’’ or approximately saltmarsh. Percentage of 100 m radius landscape that is ‘‘irregularly flooded,’’ or approximately brackish marsh. Edge density of water class, within 200 m radius. Total basal area in m2 of living trees, averaged over circles.

ba_sum_snag

field

Total basal area in m2 of standing dead trees, averaged over circles.

denscl_circ

field

denscl_tb

field

sparalte

field

juncroem

field

Percentage of density cloth that was obscured, averaged over all observations within circles. Percentage of density cloth that was obscured, averaged over all observations within the topbank. Average of maximum cover values within circles, of Spartina alterniflora. Average of maximum cover values within circles, of Juncus roemerianus.

Mean

Range

BTGR, CLRA, LEBI, MAWR, SESP, XXRA

Species

25.5

[0, 98.3]

BTGR, CLRA, COYE, KIRA, LEBI, MAWR, RWBL, SESP, XXRA

14.5

[0, 91.9]

BTGR, CLRA, COYE, LEBI, MAWR, RWBL, SESP BTGR, COYE, DOWO, HOWA, KIRA, LEBI, NOPA, PIWO, PROW, RBWO, REVI, RWBL, SUTA, YBCU, YTWA BTGR, COYE, DOWO, HOWA, LEBI, NOPA, PIWO, PROW, RBWO, REVI, RWBL, SUTA, YBCU BTGR, CLRA, COYE, HOWA, KIRA, LEBI, MAWR, NOPA, PROW, RWBL, SESP, XXRA BTGR, CLRA, COYE, KIRA, LEBI, MAWR, RWBL, SESP, XXRA

0.009

[0, 0.021]

0.98

[0, 9.47]

0.06

[0, 0.74]

40.8

[11.0, 81.8]

69.0

[9.5, 100.0]

CLRA, MAWR, SESP, XXRA

2.0

[0, 4.7]

CLRA, MAWR, SESP

0.76

[0, 5]

Notes: All variables are continuous. For each predictor, codes are shown for species having that predictor in the initial set of candidate state process covariates. Variables for remaining species are given in Appendix C: Table C1.

We developed a model selection scheme according to several concomitant objectives. First, for each species we desired a compact occupancy model composed of interpretable relationships between well-supported covariates. Second, where multiple ways of representing a particular habitat component were possible, we wanted to determine the level of measurement precision required to support good predictions. Third, we wanted to assess the relative importance of mapbased versus field-measured variables (similar to

Model development, fitting, and selection We used site occupancy models (MacKenzie et al. 2002, Royle and Dorazio 2008) to relate bird presence/absence observations to habitat variables, while accounting for imperfect detection. Occupancy models comprise both a state process component describing the occupancy probability, w, and a detection process component, p, describing the probability of detection. Both components are related to a linear predictor via a logit link function.

Table 4. Visit-level predictor variables, for detection process models. Name

Type

Description

julian continuous, integer Ordinal date, integer. hours_rise continuous Hours after sunrise, at start of point count. noise ordinal, integer Estimate of ambient noise, excluding wind; 0–4 scale (4 corresponds to hearing radius , 25 m). wind ordinal, integer Estimate of wind speed; 0–5 on the Beaufort scale. prcp continuous Inches of rainfall on the day of the point count. prev continuous Inches of rainfall on the previous day. tide categorical Tidal stage: H, near high tide (140); M, mid tide (93); L, near low tide (87). arm categorical Limb of the tidal hydrograph: E, ebb tide (123); F, flood tide (127); T, turning tide (70).

Average

Range

129.1 1.58 0

[89, 167] [0.37, 4.10] [0, 4]

0 0.11 0.12 ...

[0, 5] [0, 1.45] [0, 1.45] ...

...

...

Notes: Averages are means for continuous variables, medians for ordinal. Ordinal variables are treated as continuous for modeling purposes. Number of observations at each level of categorical variables are parenthesized in the description. Note that channel width was also included as a detection process predictor; it appears in Table 2.


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Fig. 3. Summary of the modeling workflow. Unshaded boxes represent sets of predictors or models; gray boxes represent modeling steps. For each species, the entire workflow was performed three times, once for each initial predictor set (Full, Map, Class). Best models from each of the three cycles were then ranked via cross-validation (the terminal step).

Earnst and Holmes 2012). And fourth, we wanted to judge the predictive value of a bestfitting model. Two primary constraints also influenced our modeling process (see Appendix B). We observed considerable separation when attempting to fit occupancy models for several species within a maximum likelihood context (Albert and Anderson 1984, Hosmer et al. 2000). We therefore fit all models within a Bayesian framework (Royle and Dorazio 2008), with estimation achieved via Markov Chain Monte Carlo sampling implemented in JAGS (Just Another Gibbs Sampler 3.3; Plummer 2012a) and called from R (R Core Team 2013) with the rjags package (Plummer 2012b). We used uninformative priors for all regression coefficients in both w and p models. Convergence of parameters was checked with the GelmanRubin convergence diagnostic (Gelman and Rubin 1992) in the coda package (Plummer et al. 2006). Our second major constraint was that we lacked for most species a specific set of a priori hypotheses. We could not therefore pursue a strong inference approach to model selection (Burnham and Anderson 2001, 2002), choosing instead to use a Bayesian variable selection technique in an iterative process. Under the Kuo and Mallick (1998) approach (hereafter ‘‘KM’’), each eligible term in a model is premultiplied by a Bernoulli-distributed indicator variable (Royle and Dorazio 2008:72). These indicators act as ‘‘switches’’ to determine the set of terms that are either included or ignored, for a particular iteration in an MCMC chain. The posterior mean for a particular indicator (which we label s¯ ) represents the frequency with which v www.esajournals.org

the term was left ‘‘turned on’’ in the model, and estimates the probability that the associated covariate should be included in the model, given the data and the set of covariates used. We fixed the prior probability for all such indicator variables at 0.5 (Royle and Dorazio 2008; cf. O’Hara and Sillanpaä¨ 2009). For each species in both KM steps described below, inclusion parameters (denoted s) were monitored over 50,000 MCMC iterations after a 10,000 iteration burn-in, with 3 chains, retaining values from every third iteration. The KM method has the advantage that it very efficiently considers all subsets of the global model (up to 233, in this case). It has the disadvantage that the estimated probabilities of inclusion are conditional upon the set of predictors considered (Kuo and Mallick 1998). Many predictors in our original sets were highly correlated, and although this fact does not preclude the use of KM variable selection, it can result in a large number of models in the MCMC chain with unacceptable levels of collinearity. Because models with high multicollinearity often increase the model likelihood and are therefore favored, variables with one or more correlated partners in the predictor set can appear to have outsized importance. To obtain more interpretable measures of variable importance, we thus preferred to condition the indicator probabilities on a predictor set relatively free from redundancy, selecting variables in two stages. For each species, we first performed KM variable selection on a model composed of additive combinations of all eligible predictors in the p and w components, respectively (see below). We then ranked w predictors according 8


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Information Criterion, DICv, calculated as D þ varðDÞ=2 (Gelman et al. 2004). The best model chosen by the DICv was retained for assessment via cross-validation.

to the posterior means of their associated indicator variables, and eliminated all intercorrelations within the predictor set, in the following way. Starting with the highest ranked predictor, we eliminated all lower-ranked predictors that were correlated with it, at r 0.6. We then moved to the next-highest ranked remaining predictor, eliminating all variables correlated with it. This continued to the end of the original set. We call the resulting set of covariates, free from inter-correlation, the refined set. We also retained the p predictor with the highest posterior mean. We then repeated the KM variable selection process using the single retained p covariate and the refined set of w covariates. We noticed in many cases that predictors which were moderately well-ranked in the first KM round, jumped to the top of the set in the second round. We suggest this is because the p predictor set was adjusted between the two rounds, and the indicator means are sensitive to the choice of p model; and because models containing correlated terms were no longer present. Our final model selection step was to fit models composed of predictors favored by the KM process applied to the refined set of variables. For the detection component of these final models, we used the single p covariate from the second KM step, if the probability of inclusion for that variable was greater than 50%; if it was less than 50%, we used that predictor, but also fit all considered w models with no p covariates (i.e., intercept only). For the state process component, we used all w predictors with estimated inclusion probabilities greater than 50% in the second KM step, combined additively. Predictors were added in order of descending inclusion probabilities, such that the first model contained only the single mostprobable predictor, the second included the best two, and so on. The total number of considered models, then, was equal to the number of w predictors favored in the second KM step, times the number of eligible p models (either 1 or 2). Model parameters were monitored over 15,000 MCMC iterations after a 5,000-iteration burn-in, using 3 chains and retaining values from every third iteration. The posterior mean of the deviance D was calculated for each model and used in deriving a form of the Deviance v www.esajournals.org

Model validation We used cross-validation (CV) to assess the predictive value of three models for each species (described in Methods: Model comparison), measured via the area under the curve (AUC) of the receiver operator characteristic (ROC) plot (Pearce and Ferrier 2000). Within each iteration of the CV procedure, we randomly selected and held back a quarter of sites (n ¼ 27) as the testing dataset (Shao 1993). Observations in the testing dataset (n ¼ 27 3 3 ¼ 81) were predicted using the model fit to the training data, calculated for a given site i during visit j as wˆ i 3 pˆ ij. The AUC was calculated after each iteration, and we performed 50 CV iterations for each model. We found that AUC values for species with particularly low prevalence in the dataset appeared inflated (Lobo et al. 2008, Santika 2011; cf. Pearce and Ferrier 2000). We therefore also report the Precision, or Positive Predictive Value (PPV: TP/[TP þ FP], where TP ¼ true positives, FP ¼ false positives), and Negative Predictive Value (NPV: TN/[TN þ FN ], where TN ¼ true negatives, FN ¼ false negatives) to better characterize the success a model might exhibit in a novel setting. These indices derive directly from confusion matrices, and therefore require a probability cutoff. In each CV iteration we calculated the cutoff as that which minimized the absolute value of the difference between the Sensitivity (TP/P, where P ¼ positive samples) and Specificity (TN/N, where N ¼ negative samples) of predictions made from the testing dataset (Liu et al. 2005). All error metrics were calculated with the R package ROCR (Sing et al. 2009).

Model comparison Our model fitting, selection and evaluation scheme (summarized as Initial Predictor Set ! Refined Set ! Model Selection ! Cross-validation) was repeated two additional times for each species, each with a different initial predictor set (Fig. 3). The first cycle included all relevant predictors for the particular species (Tables 2 and 3; Appendix C: Table C1), called here the Full Predictor Set. Then, another round of our model9


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ing scheme was performed using only those predictors that could be derived from a SLAMM output map. We call this the Map-only Set, and point out that it implicitly assumes complete confidence in both (1) local SLAMM pixel identities and (2) the spatial configuration of surrounding habitat patches. This assumption is unlikely to apply to most forecasting efforts for ecological systems (Guisan and Zimmermann 2000), and the predictive values we report for these models must be regarded as upper bounds. To simulate the case in which confidence could be assumed for local pixel identities but not for their spatial configuration, a third model was fit using only the simple SLAMM identities of the survey points (called the Class Set model in Fig. 3). Up to three of the design variables containing this information (Table 2) were used in the w model, as suggested by the raw occurrence frequency rates for the different habitat types (Table 1). If there was any doubt about the most sensible model to use, we used DICv to select the best model from available combinations of the design variables. In this way, we generated a single appropriate categorical variable to predict occupancy for a given species, without imposing a de facto penalty upon this variable by including habitats where the species should never occur. As an example, the SLAMM class variable used for Seaside Sparrow had three levels: (1) Regularly and (2) Irregularly Flooded marsh, and (3) both freshwater habitats combined. The p model was taken from the first two modeling cycles, or if these differed, the best of the two was chosen via DICv .

these birds will decrease at least as quickly as the area of marshes upon which they depend. We also assume that these species will shift their local ranges in space to track spatial movements and reconfiguration of marshes. We focus reporting of modeling results in the body of the paper to three of these species: Clapper Rail, Seaside Sparrow, and Least Bittern. The Clapper Rail has been used as an indicator species for salt and brackish marshes (Novak et al. 2006), since in much of its range it is an obligate resident of these habitats (Rush et al. 2012), consumes primarily benthic organisms and accumulates environmental toxins (Cumbee et al. 2008) and suffers predictable population declines as a result of marsh destruction or degradation (Powell 2006). The Seaside Sparrow is on the whole even more extreme than the Clapper Rail in its restriction to saline tidal marshes (Post and Greenlaw 2009), and is a bird of conservation concern in the southeastern USA (USFWS 2008). The Least Bittern is also a bird of conservation concern in the southeast (USFWS 2008), and in Georgia suitable habitat is mainly concentrated along the coast (Sauer et al. 2012). Least Bitterns are secretive and hard to detect, even with call playback, and are known to have detection rates that fluctuate widely during the breeding season (Rehm and Baldassarre 2007). Results for the remaining species are given in Appendix C.

RESULTS Site ordinations Ordination of sites according to SLAMM class representation and plant species’ cover values both show that sites within the estuarine mosaic organize along multiple gradients (Fig. 2). Both NMDS plots are in fact structured similarly, with Axis 1 corresponding roughly to tidal influence and Axis 2 to point-level species richness. As shown in Fig. 2, average water salinity increases from the top right corner to the bottom left. The plant community NMDS better displays the complex relationship between the SLAMM classification of sites and the species they support. The SLAMM representation NMDS, although it contains considerably more information than when sites are classified as points, clusters these

Saltmarsh birds of conservation interest Our set of focal species included several for which coastal marshes constitute the sole or at least the core breeding habitat in the southeastern USA. These species are of particular conservation concern because of potential threats to coastal marshes such as SLR, local climate changes, anthropogenic alterations, and changes to local or basin-wide stream water quality and flow regime (Bertness et al. 2002, Zedler and Kercher 2005, DeLuca et al. 2008, Woodrey et al. 2012). No alternative habitat types are available to these species, and we assume here that their habitat niches will not expand substantially. This implies that the amount of suitable habitat available to v www.esajournals.org

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NUSE ET AL. Table 5. Best covariates, for CLRA, LEBI and SESP occupancy models. Component

Variable name

s¯

Type

Clapper Rail

Species

p w

Least Bittern

p w

Seaside Sparrow

p w

noise sparalte brack comm_2 tide slamm_2 swamp water_ed ba_sum_snag salinity dist_for noise sparalte dist_for rel_elev denscl_circ

0.29 1.00 0.95 0.80 0.90 0.97 0.96 0.79 0.58 0.54 0.53 0.98 1.00 0.97 0.89 0.83

visit field map field visit map map map field field map visit field map field field

had map-based variables chosen as their best predictors, and of these only two had exclusively map-based predictors with s¯ 0.8. Eleven species had a mix of map- and field-based variables with s¯ 0.8, and six had exclusively field-based variables within this set. All birds typical of forested habitats had fieldbased variables as their best predictors, except the Northern Parula (NOPA). All marsh birds had a map-based predictor with s¯ 0.8, and four had a map-based best predictor (Least Bittern [LEBI], King Rail [KIRA], King / Clapper Rail [xxRA], Boat-tailed Grackle [BTGR]). Most of the core variables ended up with s¯ . 0.5 for at least one species. A notable exception, however, was the comm_1 variable, the first axis from the plant community data NMDS, for which s¯ , 0.5 for all species. The second axis, comm_2, had s¯ . 0.5 for seven species, appearing alongside both slamm_1 and slamm_2, and had s¯ 0.8 in five cases. For comparison, slamm_1 had s¯ 0.8 for three species, and slamm_2 for four.

Notes: Remaining species’ covariates are given in Appendix C: Table C2. For the state process model (w), predictors are shown which occurred in more than half the iterations over three MCMC chains, in the second round of variable selection using the refined (uncorrelated) set of predictors. That is, the posterior mean for the associated indicator variable s¯ . 0.5. For the detection process model ( p), the best predictor is given even if s¯ , 0.5.

point-classified sites tightly and with little overlap among groups.

Model selection Considering models derived from the Full set of predictors, only the Least Bittern and the King Rail had best models built solely from map-based variables (Table 6; Appendix C: Tables C3 and C4). Seven species had best models drawn exclusively from field-based predictors, the only marsh bird among them being the Common Yellowthroat (COYE). The remaining ten species

Variable selection The two-step variable selection process we employed allowed us to rank relatively uncorrelated variables in terms of their importance to each species (Table 5; Appendix C: Table C2 and Fig. C1). Of the 19 species considered, only six

Table 6. Best occupancy models, from each round of model selection (Full set, Map-only, and SLAMM Class) for CLRA, LEBI and SESP. Species Clapper Rail

Set

full map class Least Bittern full

Seaside Sparrow

p terms

w terms

Type

Int(þ), noise Int(þ) Int(þ), noise() tide(M)

K

sparalte(þ), brack(þ), comm_2(þ) mixed 6 Int(þ), slamm_1() ... 3 Int(), brack(þ), salt(þ) ... 5 Int(), slamm_2(þ), swamp(), map 5 water_ed(þ) map same as above same model as above ... ... class tide(M) Int(), salt, brack(þ), fresh(þ) ... 7 mixed 7 full Int(þ), noise() Int(), sparalte(þ), dist_for(þ), rel_elev(þ), denscl_circ() map Int(þ), noise() Int(), slamm_1(), dist_for(þ) ... 5 class Int(þ), noise() Int(), brack(þ), salt(þ) ... 5

D

DICv

AUC PPV NPV

cut

233.3 242.3 231.4 117.0

258.2 256.6 251.0 158.0

0.87 0.84 0.86 0.88

0.76 0.73 0.74 0.36

0.82 0.80 0.83 0.97

0.66 0.68 0.65 0.16

... ... 120.3 176.4 116.7 132.0

... 0.83 0.95

... 0.34 0.77

... 0.96 0.95

... 0.10 0.51

114.1 138.1 120.3 164.1

0.94 0.89

0.77 0.65

0.94 0.91

0.41 0.43

Notes: Remaining species’ models given in Appendix C: Tables C3 and C4. Species are ordered by AUC of their best-scoring models. Parenthetical indicators of effect direction are shown when the 90% credible interval (CrI) for that model term did not include zero. ‘‘Int’’ represents the intercept for a model component, and is only shown when its 90% CrI did not cross zero. For Full Set models, the Type column indicates whether the w terms were Field- or Map-derived, or a mix of both. K is the number of model parameters, D the mean deviance, AUC the median AUC values from the cross-validation procedure (note these are probably inflated for rare species), PPV and NPV the median positive and negative predictive values; the cut column shows the median threshold for determining presence over the 50 iterations of cross-validation.


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had best models containing a mix of map- and field-based predictors. Among the best models derived from the Map set of predictors, all but one species (KIRA) had predictors derived from the neighborhood scale, mostly the SLAMM NMDS axes. Eleven species had one of the broad-scale spatial variables (dist_dev, dist_for, dist_upl ) in their Map set best model. Eight species’ best Map set models contained categorical variables derived from SLAMM class at the sampling point.

roughly equivalent (DAUC , 0.03; NOPA, Redbellied Woodpecker [RBWO], xxRA), (2) those which experienced a decline in AUC (DAUC . 0.03) from the Full predictor set to the Map set, and from the Map to the Class set (BTGR, Summer Tanager [SUTA]), (3) those for which AUC was roughly equivalent for Full and Map sets, but suffered a decline (DAUC . 0.03) with the use of the Class set (RWBL, Seaside Sparrow [SESP], Yellow-throated Warbler [YTWA], COYE, Hooded Warbler [HOWA], Yellow-bellied Cuckoo [YBCU], Pileated Woodpecker [PIWO], PROW, LEBI, Downy Woodpecker [DOWO]), and (4) those that saw a decline (DAUC . 0.03) from the Full set, but not from the Map to the Class set (Marsh Wren [MAWR], Red-eyed Vireo [REVI]). There was also a species for which the Class set was apparently better than the Map set (CLRA), and one for which the Map set was best and the Full set was the worst (KIRA). Compared to their Full set models, several species also suffered reductions in PPV with the use of the Map set (REVI, HOWA) and from the Class set (SESP, COYE, HOWA, PROW). Because the Map predictor set represents the maximum information available from a SLAMM predictive map, we point out that all our focal species had Map set AUC scores considered either ‘‘good’’ (0.8 , AUC , 0.9) or ‘‘excellent’’ (AUC . 0.9).

Model validation and comparison We were able to build models for all our focal species that received good median AUC scores (range: [0.84, 0.98]; Table 6; Appendix C: Tables C3 and C4). However, the AUC values for species that were rarer in our dataset are apparently inflated. Species varied in their degree of representation in the dataset, and models for rarer species needed only to accurately predict absences to receive good AUC scores. For example, the best Full predictor set model for the Prothonotary Warbler (PROW) received an ‘‘excellent’’ AUC score of 0.93, but a PPV score of only 0.40, meaning that of all observations for which presence was predicted, only 40% on average proved correct (given our method of selecting a threshold probability). We suggest that such a model would hardly be called ‘‘excellent’’ under most applications. The Positive Predictive Value is thus an important additional guide to a model’s predictive ability when species prevalence is low. Median PPVs were highly correlated with median cut-off values (r ¼ 0.90, p ,, 0.001), and this relationship was maintained within each of the predictor sets. Median AUC scores, in contrast, were not correlated with median cut-off values (r ¼ 0.07, p ¼ 0.62). The highest median NPVs were above 0.9 for all species but three (Clapper Rail [CLRA], BTGR, Red-winged Blackbird [RWBL]). Although debate surrounds the use of AUC as an absolute measure of model predictive value (e.g., Lobo et al. 2008, cf. Santika 2011), we were most interested in the change to median AUC values that resulted from the use of different predictor sets (Fig. 4). Based upon the magnitude of this change, we classified species into four main categories: (1) those for which best models from each of the three predictor sets used seemed v www.esajournals.org

Saltmarsh birds of conservation interest Our best Full predictor set model for Clapper Rail occupancy included a mix of important field- and map-based variables. Occupancy was positively related to average cover of Spartina alterniflora (field-measured), positively related to a categorical indicator of brackish marsh (mapderived, local-scale), and positively related to the second plant community NMDS axis (field). This set of covariates represents a useful hypothesis regarding proximate factors governing Clapper Rail occupancy within the coastal marsh complex; however, it is remarkable that the best Class set model, composed of a single three-level categorical variable indicating saltmarsh, brackish marsh, or other, performed equally as well as the Full set model. We interpret this equivalence to mean that Clapper Rails are spread throughout the salt and brackish marshes of our study area (Fig. 5). 12


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Fig. 4. Summary of model cross-validation results from Table 6, and Appendix C: Tables C3 and C4. Species are sorted according to the largest of their three median PPV values. In order to show the lack of overall pattern in median AUC values with declining PPV, values from the Full set models are connected by solid gold lines; from Map set models by dashed green lines; and from Class set by long-dash blue lines. Colored bands indicate 10th to 90th percentile ranges for each set of 50 cross-validation iterations: Full, gold, offset left; Map, green, centered; Class, blue, offset right.

side Sparrows and Red-winged Blackbirds (A. J. J. Lehmicke, personal communication). Whatever the cause, Seaside Sparrow breeding in our study area was restricted primarily to a core section of marsh (Fig. 5), removed from surrounding upland habitats and associated with both high relative elevation of the marsh platform and abundant short-form Spartina alterniflora. It is notable, however, that the best Map set model performed almost as well in our cross-validation routine; it contained the distance to forest variable and also the first SLAMM NMDS axis. We suggest this equivalence in predictive ability is a result of the close correspondence between the habitat niche space occupied by our Seaside Sparrows, and actual geographic space. Because of this correspondence, the map-based model was equally sufficient. Our best Class set model,

In the Full set model, Seaside Sparrow occupancy was positively related to cover of Spartina alterniflora (field), distance to forest (map, broadscale), and relative elevation (field, lidar); and negatively related to horizontal vegetation density in the mid- or high-marsh (field). We suggest that these variables represent a hypothesis regarding the Seaside Sparrow’s distribution that is consistent with the conclusions of Greenberg et al. (2006), including that sparrows of tidal marshes are subject to dual but perhaps compensatory pressures of nest flooding and nest predation. Indeed, occupancy of both Redwinged Blackbirds and Boat-tailed Grackles was negatively related to distance to upland and forest, respectively. This seems a provocative pattern, given that there is some anecdotal evidence for agonistic interactions between Seav www.esajournals.org

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Fig. 5. (A) Predicted response of Seaside Sparrow occupancy probability to distance to forest, using the best Full model in Table 6: p(noise) w(sparalte þ dist_for þ rel_elev þ denscl_circ). All predictors other than dist_for are held at their mean values. (B) Predicted response of Clapper Rail occupancy probability to SLAMM class, using the best Class model in Table 6: p(noise) w(brack þ salt). The noise covariate is held at its mean value.

(water_ed ) were important to predictive ability. Because of its relative rarity and sparse encounter histories, the Least Bittern models had very low PPVs (0.34–0.36). These models are actually quite poor at predicting Bittern presence, and the absolute AUC values are probably not very useful as a guide to model quality.

however, suffered a reduction in AUC and PPV, indicating the relative insufficiency of knowledge of the local habitat type, compared with neighborhood- and broad-scale information. Our naive estimate of detection probability for the Least Bittern was 0.54, calculated as the average over all sites where Bitterns were observed at least once, of VB/VT, where VB is the number of visits during which Bitterns were observed and VT is the number visits to that site. For comparison, the naive estimate of detectability for Seaside Sparrows was 0.82; for Clapper Rails it was 0.73. In our analysis, tide was the most important predictor of Bittern detection, which was negatively affected by middling water levels. Least Bittern occupancy in our study area (at least in 2010) is positively related to the second SLAMM NMDS axis and water-wetland edge density; it is negatively related to the categorical predictor indicating tidal swamp. The Full and Map set models were identical, indicating that using field-based variables provided no advantage over what could be derived from a SLAMM predictive map. However, the Class model saw a reduction in AUC, implying that neighborhood-scale information about pixel identities (slamm_2) and local configuration v www.esajournals.org

DISCUSSION The effects of thematic resolution on the predictive capacity of species distribution models under novel climate or landcover scenarios has not been thoroughly investigated, despite recognition of the potential importance of thematic resolution in interpreting distribution models (Castilla et al. 2009, Liang et al. 2013). Here we report on the influence of thematic resolution on the predictive capacity of species distribution models for an entire class of animals (birds) inhabiting coastal wetland ecosystems that will undergo complex changes from sea level rise. The spatial information in a thematically-coarse SLAMM map appears to be almost as valuable as field-gathered habitat data in predicting patterns of wetland bird occupancy, with a few notable exceptions. The prevalence of NMDS axes in our 14


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models shows an effect of the neighborhood of a site, and a SLAMM map with reduced confidence in spatial configuration (simulated by our Class predictor set) would induce a decrease in predictive ability. The most important consequence of this finding is that distributions of birds in coastal wetlands should generally not be expected to track simple areal changes in coarsely defined wetland habitats. One of the exceptions to this conclusion is the Clapper Rail, for which the simple SLAMM habitat classification, devoid of any spatial information, was roughly equivalent to the use of the Full predictor set. In terms of population prediction under scenarios of SLR, this result constitutes an advantage over most of the other species we considered since it allows the use of a straightforward calculation of habitat change. Most of the species we considered were matched with both field- and map-based covariates by our variable selection scheme, with several forest birds favoring exclusively fieldbased predictors (four species, at the s¯ . 0.6 level). We interpret this pattern, in concert with the reductions in model predictive ability that result from removing spatial information, to mean that both local habitat features and spatial properties of the coastal wetland mosaic are important to most breeding birds in tidal wetlands of the Altamaha River estuary. The importance of landscape spatial structure appears to be generally greater among marsh birds (LEBI, CLRA, KIRA, xxRA, MAWR, SESP, RWBL, BTGR) than forest species, however. Although salinity formed the primary habitat gradient in our survey, it is remarkable that it did not appear in any of the best models and was among the favored variables for only three species (LEBI, YBCU, RBWO). Salinity displays considerable year-to-year variation in the Altamaha estuary (Di Iorio and Castelao 2013), and the fact that our measurements during a single year generally provided less information than aspects of the plant community is perhaps unsurprising. Nevertheless, salinity does not seem to provide significant intra-year control over patterns of occupancy, and this suggests a stability of bird assemblages in the face of yearly salinity fluctuations. Indeed, the importance of Spartina alterniflora coverage to several of the saltmarsh species (CLRA, MAWR, SESP) indiv www.esajournals.org

cates a closer relationship to fine-scale details of plant community and vegetation physiognomy than to salinity or marsh type. For many forest birds, various measurements of species richness showed up in the best models (YBCU, RBWO, PIWO, REVI, HOWA), as did physiognomic characteristics (xxRA, YBCU, DOWO, REVI, PROW, COYE, HOWA, PROW, YTWA, SUTA); and plant community structure, as represented by the community NMDS axes, was important to species in all parts of the salinity range (CLRA, COYE, NOPA, BTGR). We conclude from these results, and because our best Full set models all had at least ‘‘good’’ AUC values, that the plant community and vegetation structure collectively either (1) directly control patterns of occupancy for many bird species in the coastal wetland mosaic, or (2) express average environmental conditions that directly inform these patterns. The field-based variables in our analysis, although measured with high precision, are unavailable in a SLAMM predicted landscape. But we wish to emphasize that the map-based variables are subject to considerable uncertainty. From a territorial bird’s perspective (Kristan 2006), we conceive of three primary scales of uncertainty for sites in a future landscape, those being uncertainty as to (1) the habitat type of a particular pixel, (2) the identities and spatial configuration of other pixels in a local neighborhood (e.g., SLAMM NMDS axes, channel width) and (3) spatial properties at broader scales (e.g., distance to forest or upland). Of these uncertainty types, we expect the second to be the largest in magnitude and most difficult to address. This is because the second type relies explicitly on spatial information, compounding the uncertainty of multiple surrounding pixels. In contrast, the first type concerns only a single pixel, or a sum across many pixels with regard to total area only; and the third incorporates spatial relationships to upland habitats at broad scales, probably far less dynamic than the local neighborhood of wetlands. Yet many of our species had variables of the second type in their best Full or Map models. For models containing any map-based variables, we contend that the predictive measures we obtained by cross-validation represent upper bounds, and that models containing variables derived from local neighborhoods may be particularly unsuitable for use with predictive 15


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maps for which uncertainty regarding the spatial properties of habitat patches is high. We suggest that models containing variables of the first and third kinds above may be more robust when applied to forecast landscapes.

niche conservatism to considerations of global climate change. However, their focus is restricted to Hutchinson’s (1957) fundamental niche. We posit that changes to a species’ realized niche are also of immediate importance, in forecasting population effects due to decadal changes in climate or sea level. This is because empirical habitat relationships depend upon the fundamental niche, the realized niche, and the actual spatial distribution of a species (Austin 1987), which may be determined by competition (Chase and Leibold 2003); stochastic, behavioral, or disturbance processes (de Swart et al. 1994, Hanski 1999); or the source-sink organization of a population (Pulliam 2000). Where real patterns of species demography are at issue, the realized niche is of principal importance, as the more proximal antecedent of actual fine-scaled geographic distribution (Soberon and Nakamura 2009). We may consider niche conservatism of the realized niche, then, to be a natural focus of conservation science. The basic question raised by Wiens and Graham (2005), namely, ‘‘Is niche conservatism strong enough to be of consequence?’’ is applicable to species’ realized niches as well, albeit on ecological rather than evolutionary timescales. If species’ realized niches are not conserved, but are instead altered as the landscape changes through time, then the prospect of making consistently accurate species-level forecasts is probably fairly dim. This problem could be exacerbated by the advent of no-analog conditions (Williams and Jackson 2007, Fitzpatrick and Hargrove 2009). How, then, can the realized habitat niche of a species be assessed? We suggest two avenues by which to address this question. First, assessment of the variation in a species’ habitat relationships over multiple study areas is perhaps the most viable method of determining the nature of its realized niche, and in particular the amount of variability along important niche axes. This is because a particular study, bounded in space and time, cannot observe the realized niche—which is still a theoretical property of a taxon (Soberon and Nakamura 2009)—but only sample from it. Even with perfect estimation of population parameters such as occupancy, abundance or recruitment, the result of one study represents the description of a single instance of the realized niche, the apparent shape of which

Thematic resolution In general, our best Class set models had reduced predictive value, relative to models that included field-measured or spatial information. In essence, this reduction results from a difference in the resolution of habitat classification, between the actual and the predicted landscape. Individual species will depend to a greater or lesser degree upon those details of their habitat landscape that are unavailable in the predicted one. This issue has been raised in a general form by Lawler et al. (2004), who found that models using a fine-resolution classification scheme had generally better explanatory power than when coarser classes were used. However, the benefit of increased classification, or thematic, resolution was species- and locale-dependent. Thematic resolution is also closely related to the spatial grain of habitat maps. Finer grain allows for, but does not necessarily imply, finer thematic resolution; however, coarsening the spatial grain of a map does implicitly coarsen the classification of the landscape. These separate issues for species predictive modeling are considered by Gottschalk et al. (2011), who point out that tests of the effect of spatial grain on habitat models are more common in the landscape ecology literature than comparisons of classification resolution. They found that smaller-grain habitat maps led to models with better explanatory power, but that this was largely because of increased ability to resolve important habitat features (such as hedgerows and woodlots). Increased thematic resolution generally had a positive but weak effect on model explanatory power. We conclude, however, that improving the thematic and spatial resolution of predictive habitat maps, or otherwise increasing their ability to resolve important habitat features, is probably the most direct method by which to improve species prediction accuracy.

Realized and empirical niche Wiens and Graham (2005) address the utility of v www.esajournals.org

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may fluctuate in space and time (Hanski 1999). We call an actual sample from the realized niche, corresponding to the actual distribution of a species in geographic space, the empirical niche of that species. In making species-level predictions based upon real data, we feel it is necessary to understand the relationship between the empirical and the realized niche, and to recognize that the primary interest rests with the latter. A strategy to acquire this understanding could take the form of a coordinated survey program (e.g., SHARP 2014) or via meta-analysis of habitat studies (Johnson 2002, Huettmann and Gottschalk 2011). Indeed we intend—and our modeling approach implies—that the models and variables we found to be important in our area should be considered hypotheses to be tested in novel systems. Consistent habitat relationships across multiple populations would suggest a close correspondence between actual fine-scale geographical distribution and the realized niche. Widely varying habitat relationships would suggest a weaker correspondence between distribution and niche, and the potential for a more malleable response to a changing landscape. This conclusion assumes that populations differ only in their expression of the species’ realized niche, not because genotypic peculiarity has caused their fundamental niches to diverge (Holt 2009). Efforts to determine whether local constraints on spatial distribution correspond to boundaries of the fundamental or realized niche, should also be pursued. And the use of space-fortime substitution holds much promise for prediction of population changes due to SLR (Wenger and Olden 2012, Blois et al. 2013). A second approach to assessing the realized niche of a species is through increased attention to the mechanisms that drive local distributions (Buckley et al. 2010, Marion et al. 2012). These might include details of environmental conditions and processes (Vallecillo et al. 2009), resource availability, or species interactions that constrain habitat selection or limit population growth (Cabral and Kreft 2012). For instance, Seaside Sparrows in our study area tend to nest in saltmarsh that is far from adjacent forest. Is this because the marsh platform tends to be higher away from upland areas, and nests are less likely to be flooded (a fundamental niche component)? Or because of the threat of nest v www.esajournals.org

predation or destruction, e.g., from Red-winged Blackbirds, which tend to prefer marsh nearer to forest (realized niche component)? Or for some other reason? The answer clearly has consequences for both prediction and conservation efforts, since fundamental niche boundaries are probably easier to model or manage in future landscapes than those of the realized niche (e.g., van de Pol et al. 2010). Either way, niche boundaries derived from demographic mechanisms should be generally more transferable to new landscapes than proposed boundaries based upon habitat association. Mechanistic predictions of species’ distributions are not exempt from the general issue we raise here, however, since they too may depend closely upon predictive maps of environmental conditions or biotopes. Nevertheless, they probably offer a general improvement over correlative models (Kearney 2006, Buckley et al. 2010; but see Robertson et al. 2003, Kearney et al. 2010), their major drawbacks being (1) the increased effort required to build them, and (2) their often extreme simplicity. We posit that mechanistic models as described by Kearney (2006) are best suited for describing distributional constraints due to fundamental niche boundaries. As previously mentioned, such constraints may not always be relevant or interesting for a real population, or they may be insufficient to describe local distributions (van de Pol et al. 2010).

Habitat niche types As previously discussed, we conceive of three hierarchical levels of a taxon’s niche: the fundamental and the realized niches (Hutchinson 1957), and the empirical niche, a particular instance of the realized niche manifested in real geographic space. Apparent habitat associations for a species in a landscape are a description of the empirical niche, which is itself an instance of the realized niche. We will refer to such habitat associations collectively as the habitat niche. We now propose three conceptual types of habitat niche, as a general tool for assessing the likely quality of map-based distributional predictions for a species. Supposing a habitat classification system such as is used by SLAMM, the first of these types describes a habitat niche that corresponds well to one or more such habitat classes. A species with a habitat niche of this 17


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type, which we name a filler, would be found at relatively even densities throughout a habitat class. Such a species might be called a habitat generalist; but since use of this term is highly context-dependent we choose to avoid it altogether (a ‘‘saltmarsh specialist’’ might be considered a generalist within saltmarsh, for example). Within our modeling exercise, a filler species would be indicated by relatively good predictive ability of the Class set model. Density could be high or low, but no field-based model, nor one including spatial characteristics of the site’s neighborhood, could improve upon a simple categorical variable indicating the SLAMM class of the pixel. Among our focal species, Clapper Rails fit this description quite well, since they are likely to be encountered in any area of salt or brackish marsh. We suggest that map-based predictions of distributional shifts should be relatively accurate and straightforward for a filler species. As a demonstrative example, Craft et al. (2009) used SLAMM (version 5) to predict, for example, a 45% reduction in saltmarsh (from 1106 to 610 km2) along the Georgia coast with an 82-cm rise in sea level by the year 2100, and a stable amount of brackish marsh (416–412 km2). We will ignore the ‘‘transitional saltmarsh’’ class in this example, although it increased dramatically under this simulation (34–306 km2). Because the best Class set model for the Clapper Rail has a predictive ability very similar to the best Full set model, we conclude that if other potential changes are ignored (e.g., anthropogenic marsh degradation, effects of increased evapotranspiration) it is reasonable to expect that the available amount of Clapper Rail habitat will approximately track the trajectory of areal changes to salt and brackish marshes. This would imply an expected 33% reduction in Clapper Rail habitat (maximum 58%). Of course, confidence in this prediction would be greatly improved by tests of our models in other south Atlantic coast estuaries (see Discussion: Realized and empirical niche). A species with the second habitat niche type we describe does not correspond well with available habitat classes, but instead occupies a niche that is nested within one or more of these classes. That occupied region of niche space could correspond closely to geographic space, but it need not; in either case, species with this v www.esajournals.org

type of empirical habitat niche pose a prediction problem. They utilize habitats that are subsets of the classes described by LCMs, and we therefore call them splitter species. In a predicted landscape, uncertainty as to whether or not a particular pixel represents the favored subhabitat could be extremely high, and there may be no way to quantify that uncertainty. This habitat niche type is indicated by a reduction in the predictive ability of Class set model. Such a species could occur at high (SESP, RWBL) or low densities (LEBI), and could have a distribution with identifiable geographic structure (SESP, RWBL), or one more dependent upon local conditions (REVI). The third habitat niche type we describe is one that is difficult to analyze because the apparent distribution of the species changes over relatively short timescales. We call such a species itinerant. An example in our study was the Least Bittern, which in 2010 appeared to be a splitter species. However, observations at a subset of sites in 2011 and 2012 showed very high rates of colonization and extinction (B. L. Nuse, unpublished data). If it be sustained over time, such a shifting local distribution could indicate a sink population (Pulliam 1988), and that the area under consideration is potentially outside the species’ fundamental niche (Pulliam 2000). However, it does not follow that the observed population is unimportant to the persistence of the local metapopulation (Falcy and Danielson 2011). For an itinerant species, many years of data may be necessary to resolve its general habitat requirements, which may depend on a complex interplay of abiotic and biotic factors. Even with this information, prospects for precisely predicting future distributions are probably even poorer than for a splitter species, unless a mechanism for the apparent distributional shifts can be discovered and modeled. These nuances point to the importance of long-term monitoring combined with experimentation to determine the drivers of complex ecological dynamics (Brown et al. 2001), the understanding of which can be especially important for making effective conservation decisions under changing conditions. Our modeling approach, in which we use the same set of sites for all species, is somewhat unrealistic from an autecological perspective. By including the same set of survey sites for all 18


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species we admit of a possibility, for instance, of forest birds to occur in saltmarsh habitats. However, our primary goal was to allow comparison among species that occur throughout the coastal wetland habitat mosaic. And as shown by our two ordination summaries, habitats within this mosaic can be difficult to classify. We do acknowledge that efforts to find a habitat model for a single species would naturally exclude habitats from consideration that do not typically support it. For less common or detectable species, such a focused analysis would likely result in a lower proportion of absences in the dataset, fewer models having strongly negative w intercepts, and generally higher PPVs and lower NPVs and AUC values (Santika 2011). Nevertheless, we believe that the reductions we see in model predictive ability within a particular species, with increasingly restrictive sets of candidate predictors, are informative and relevant to the kinds of species prediction likely to follow from forecasting exercises involving SLR LCMs such as SLAMM. We likewise acknowledge that our crossvalidation scheme is somewhat narrow in its scope. Because we did not conduct model selection using the training data during each CV iteration, our conclusions regarding predictive ability cannot be applied outright to the predictor sets we identified (Full, Map, Class), but rather the best possible model from each set, conditional upon the full dataset. We propose that our DAUC values are conservative, however, and that our results represent an upper bound on the performance that could be expected of these predictor sets when used to forecast species’ occupancy patterns under SLR. Models with terms allowed to vary to fit each training dataset in a series of CV iterations should on average be worse at predicting the test data than a model fit to the full dataset.

more plausible. For species that react to plant species composition, or vegetation structure tied to that composition, efforts to use niche models to predict shifts of individual plant species may prove fruitful (Visser et al. 2013). And recent advances in remote sensing and widespread application of lidar elevation mapping have allowed efforts to resolve important vegetation features of saltmarsh ecosystems (Hladik et al. 2013), representing a major step toward predicting their place in a future landscape. It may be that for some habitat classes, however, finer resolution is not possible without considerably increasing levels of uncertainty in the resulting predictive maps. Should this prove to be the case, there may be no immediate remedy for the resolution mis-match problem we have described.

ACKNOWLEDGMENTS Field hands Dan B. Breen, Stephanie E. Sears, O. Stribling Stuber, Richard A. Milligan, Jr. and Ronald A. Nuse are owed B. L. Nuse’s gratitude for making the surveys possible. Funding for B. L. Nuse’s work on the study was provided by a NOAA National Estuarine Research Reserve (NERR) System Graduate Research Fellowship at the Sapelo Island NERR, and by the Georgia Ornithological Society. Guidance and transportation to some sites was provided by the Georgia Coastal Ecosystems Long-term Ecological Research site. B. L. Nuse would also like to thank Gordon Rogers, James Holland, Dorset Hurley, and Mary C. Freeman for their kind interest, advice and encouragement. We also thank two anonymous reviewers whose comments improved the manuscript.

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Conclusion We conclude that improving the thematic and spatial resolution of predictive habitat maps, or otherwise increasing the ability to resolve important habitat features, is probably the most direct method by which to improve prediction accuracy of species distribution models in systems such as coastal wetlands. Fortunately, there is reason to believe such improvements are becoming ever v www.esajournals.org

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SUPPLEMENTAL MATERIAL ECOLOGICAL ARCHIVES Appendices A–C are available online: http://dx.doi.org/10.1890/ES15-00385.1.sm


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