Dynamic Habitat Selection by Two Wading Bird Species with Divergent Foraging Strategies in a Seasonally Fluctuating Wetland Author(s): James M. Beerens, Dale E. Gawlik, Garth Herring, and Mark I. Cook Source: The Auk, 128(4):651-662. 2011. Published By: The American Ornithologists' Union URL: http://www.bioone.org/doi/full/10.1525/auk.2011.10165
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The Auk 128(4):651−662, 2011 © The American Ornithologists’ Union, 2011. Printed in USA.
DYNAMIC HABITAT SELECTION BY TWO WADING BIRD SPECIES WITH DIVERGENT FORAGING STRATEGIES IN A SEASONALLY FLUCTUATING WETLAND JAMES M. BEERENS,1,2,3 DALE E. GAWLIK,1 GARTH HERRING,1,4 2
AND
MARK I. COOK2
1 Department of Biological Sciences, Florida Atlantic University, 777 Glades Road, Boca Raton, Florida 33431, USA; and South Florida Water Management District, Everglades Division, 3301 Gun Club Road, West Palm Beach, Florida 33416, USA
Abstract.—Seasonal and annual variation in food availability during the breeding season plays an influential role in the population dynamics of many avian species. In highly dynamic ecosystems like wetlands, finding and exploiting food resources requires a flexible behavioral response that may produce different population trends that vary with a species’ foraging strategy. We quantified dynamic foraging-habitat selection by breeding and radiotagged White Ibises (Eudocimus albus) and Great Egrets (Ardea alba) in the Florida Everglades, where fluctuation in food resources is pronounced because of seasonal drying and flooding. The White Ibis is a tactile “searcher” species in population decline that specializes on highly concentrated prey, whereas the Great Egret, in a growing population, is a visual “exploiter” species that requires lower prey concentrations. In a year with high food availability, resource-selection functions for both species included variables that changed over multiannual time scales and were associated with increased prey production. In a year with low food availability, resource-selection functions included short-term variables that concentrated prey (e.g., water recession rates and reversals in drying pattern), which suggests an adaptive response to poor foraging conditions. In both years, the White Ibis was more restricted in its use of habitats than the Great Egret. Real-time species–habitat suitability models were developed to monitor and assess the daily availability and quality of spatially explicit habitat resources for both species. The models, evaluated through hindcasting using independent observations, demonstrated that habitat use of the more specialized White Ibis was more accurately predicted than that of the more generalist Great Egret. Received July , accepted July . Key words: Ardea alba, Eudocimus albus, Florida Everglades, foraging strategies, habitat selection, prey availability, resource selection functions, water management.
Selección de Hábitat Dinámica por dos Especies de Aves Zancudas con Estrategias Divergentes de Forrajeo en un Humedal con Fluctuaciones Estacionales Resumen.—La variación estacional y anual en la disponibilidad de alimentos durante la temporada de cría juega un papel importante en la dinámica poblacional de muchas especies de aves. En ecosistemas altamente dinámicos, como los humedales, la búsqueda y explotación de los recursos alimenticios requieren respuestas de comportamiento flexibles, que pueden producir diferentes tendencias poblacionales que varían con la estrategia de forrajeo de cada especie. Cuantificamos la selección de hábitat de forrajeo dinámica por parte de individuos marcados con radiotransmisores de Eudocimus albus y Ardea alba durante el periodo de cría en los Everglades de Florida, donde la fluctuación de recursos alimenticios es pronunciada por los eventos estacionales de inundación y de seca. El ibis E. albus presenta una estrategia de forrajeo de “búsqueda” táctil cuyas poblaciones están en declino y que se especializa en presas con alta concentración, mientras que la garza A. alba, cuyas poblaciones están en crecimiento, es una especie con estrategia de forrajeador visual “explotadora” que requiere concentraciones más bajas de presas. En un año con alta disponibilidad de alimentos, las funciones de selección de recursos para ambas especies incluyeron las variables que cambiaron en escalas de tiempo plurianuales y se asociaron con el aumento de la producción de presas. En un año con baja disponibilidad de alimento, las funciones de selección de recursos incluyeron variables de corto plazo que se relacionaron con la concentración de presas (e.g., tasas de disminución del agua y cambios en el patrón de secado), lo que sugiere una respuesta adaptativa a condiciones de forrajeo en ambientes pobres. En ambos años, el ibis E. albus tuvo un uso de hábitat más restringido que la garza A. alba. Fueron desenvueltos modelos de idoneidad de hábitat en tiempo real, para monitorear y evaluar la disponibilidad 3
Present address: Department of Biological Sciences, Florida Atlantic University, 3200 College Avenue, Davie, Florida 33314, USA. E-mail:
[email protected] 4 Present address: U.S. Geological Survey, Western Ecological Research Center, Davis Field Station, University of California, Davis, One Shields Avenue, Davis, California 95616, USA. The Auk, Vol. , Number , pages −. ISSN -, electronic ISSN -. © by The American Ornithologists’ Union. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press’s Rights and Permissions website, http://www.ucpressjournals. com/reprintInfo.asp. DOI: ./auk..
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diaria y la calidad de los recursos de hábitat espacialmente explícitos para ambas especies. Los modelos, evaluado a través de análisis a posteriori mediante observaciones independientes, demostraron que el uso del hábitat de la especie más especializada, el Ibis E. albus, fue predicho con mayor exactitud que el de la especie más generalista A. albus. Seasonal variation in food availability plays an influential role in the population dynamics of many avian species (Beals , Newton ), the guild structure of avian communities (Nudds ), and how species adjust their behavior throughout the annual cycle (Hahn ). Food availability may be particularly limiting during the breeding season (Skutch , Lack , Ricklefs ) because of increased energetic and nutritional demands for egg production (Drent and Daan ), incubation (Heaney and Monaghan , Reid et al. ), and chick rearing (Erikstad et al. ). Adaptations to food limitation include increased search and travel time, delayed initiation of breeding, suboptimal colony-site selection, and decreased foraging-site tenacity (Newton , Kushlan ). Theory predicts that predators will change their niche breadth according to environmental variation (Emlen , MacArthur and Pianka ). When food is plentiful, optimal foraging theory predicts that birds will specialize on the most profitable resources and converge on resource use, and that niche overlap will be high (Root , McKnight and Hepp ). Overlap is expected to decrease as food availability declines and individuals utilize food resources (or microhabitats) to which they are most adapted, often causing the niche to contract under increasing competition (Root , Baker and Baker ). However, in highly dynamic ecosystems like wetlands, behaviors to find and exploit food resources are likely to be more important adaptations than competitive ability once resources are located, but we have only limited evidence that species can rapidly adjust distributions in real time to rapidly changing environments. New insights have been made possible by novel modeling approaches and fine-resolution, real-time environmental data that improve the description of temporal change in niche components and overlap (Pearman et al. ). A large, dynamic wetland habitat is a model ecosystem to observe varying behavioral responses to food limitation because wetland-dependent foraging species such as wading birds (Pelecaniformes) must adjust to food resources that change daily. Hydrological cycles and geo- and microtopography determine the spatial and temporal availability of aquatic prey and the rate of replenishment of foraging patches after they are depleted. In large subtropical and tropical wetlands, food availability is driven by the seasonal wet and dry periods and short-term rainfall events. In wetlands that are highly regulated by humans, such as the Florida Everglades, water management and nutrient inputs also are drivers of the spatial and temporal distribution of foraging patches (Gawlik ). Prey abundance in this system is largely affected by the duration of marsh inundation, vegetation structure, and nutrient levels (Gawlik ). Prey populations become available to wading birds when processes such as water depth and water-level recession rate concentrate prey and interact with vegetation structure to provide open foraging habitat (Bancroft et al. , Russell et al. ). Because wading birds respond behaviorally to spatial and temporal variation in the quality, quantity, and availability of their food resource, foraging-selection patterns can be used to assess the effects of these transient conditions (Erwin ). Relating habitat-selection patterns to changes in
food availability, using hydrological change as a surrogate, allows for a quantitative understanding of this dynamic relationship and its effects on populations of wading birds. Gawlik () showed that vulnerability and density of prey did not always elicit a similar foraging response or strategy across a suite of wading bird species. These results and those of other studies (Herring ; Herring et al. a, b) suggest that differences in nesting success and physiological condition during the breeding season could be linked in part to species-specific foraging strategies (searchers vs. exploiters) and prey availability across the landscape. The members of the searcher–exploiter wadingbird guild exhibit a wide range of foraging behaviors and their preference in foraging-site selection, water-depth use, and diet may shift in response to changes in prey availability. The dynamic nature of the environment requires a broad foraging niche, but species must also possess adaptations that allow coexistence— for instance, by remaining a generalist in poorer conditions or by increasing breeding effort when conditions are good. Species that exhibit foraging strategies at opposite ends of the searcher– exploiter continuum perceive patch payoffs very differently as patch quality changes. Searchers like the White Ibis (Eudocimus albus; hereafter “ibis”) are highly social, select high-quality patches, and abandon them quickly (high giving-up density). On the other hand, exploiters like the Great Egret (Ardea alba; hereafter “egret”) are less social and use foraging areas until prey densities are quite low (low giving-up density; Gawlik ). Moreover, egrets tend to be opportunistic (McCrimmon et al. ) whereas ibises tend to be more restricted in their selection of foraging sites (Gawlik ). The egret and ibis were selected as subjects for this study because they are representative of the exploiter and searcher foraging strategies, respectively, and have dissimilar population trends. Ibis populations declined ~%, whereas egret populations increased ~%, from the s to across the Everglades (Crozier and Gawlik ). These disparate population trends suggest that these species have responded differently to long-term changes in habitat quality and prey availability, a notion supported by recent short-term studies (Herring et al. a, b). We studied the foraging-habitat selection responses of radiotagged ibises and egrets during years, and , with different hydrological conditions and prey availability. This approach provided a natural framework for examining how the two species potentially adjusted their habitat selection in response to varying food availability. Hydrological conditions in (hereafter the “good” year) were characterized by a long period of inundation preceding the dry season, a rapid water-level recession, few reversals in the recession, and above-average prey availability (Gawlik et al. ). These conditions are considered favorable for nesting by both egrets and ibises (Gawlik ). In addition, a supplementary-feeding experiment suggested that environmental food availability did not limit ibis nestling growth and survival (Cook and Herring ) or physiological condition (Herring et al. a). By contrast, (hereafter termed the “poor” year), the dry season was preceded by a short period of marsh inundation, a rapid water-level recession rate, and more reversals in the drying pattern. These conditions led to lower prey availability (Gawlik et al.
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), reduced nesting effort by wading birds, and signs that ibis nestlings were in poor physiological condition because of limited food availability (Cook and Herring , Herring et al. a). On the basis of the different hydrological conditions and prey availability between years, we expected () both species to exhibit more restrictive selection for short-term variables that increase prey concentration in a year with low prey availability. Furthermore, given the contrasting searcher-exploiter foraging strategy, we expected () egrets to display greater behavioral flexibility (realized niche breadth) than ibises. M ETHODS Field methods.—Egrets and ibises were captured prior to the initiation of breeding, from January to March, and , using a net gun and a modified flip trap (Herring et al. ). Radiotransmitters ( g) were attached using a figure- harness around the legs and lower back of each bird. All captured birds were banded with federal aluminum bands. Sample sizes totaled radiotagged birds ( egrets and ibises) in and birds ( egrets and ibises) in . A subset of radiotagged ibises and egrets, selected by detection on aerial flight-line transects, were located three to four times a week (. ± . [SE] birds−day) from a plane with strut-mounted dual four-element antennas and a null system. After a flight, frequencies of located birds were deleted from the telemetry receiver, and after two flights all frequencies were reentered into the telemetry receiver such that birds were never located more than twice in a week. There was a minimum of days between each successive radiolocation of an individual to ensure independence, because there is some evidence that wading birds may forage overnight (Beerens ). When a bird (or flock) was visually determined to be foraging, its location or the location at the center of its flock was recorded with a global positioning system. Aerial photographs were taken to subsequently confirm remote-sensed data used to classify habitat parameters. Variables.—Foraging-site locations (n = ,) were plotted on a map of the Everglades using a geographic information system (ARCMAP, version .; ESRI, Redlands, California). We also selected daily random locations (n = ,) to represent available habitat within the study area. Hydrological variables were estimated at daily time steps throughout the breeding season using the Everglades Depth Estimation Network (EDEN), a landscape-level, nearly real-time hydrological model (Telis ). This hydrological monitoring tool estimates daily water depths across most of the Everglades using an integrated network of real-time water-level monitoring, ground-elevation modeling, and watersurface modeling at a spatial scale of × m. The model accurately estimated water depths to within cm (Liu et al. ). Available foraging habitat was defined as the northern Everglades of Florida (Arthur R. Marshall Loxahatchee National Wildlife Refuge and Water Conservation Areas and ). This area encompassed the foraging habitat for the majority of radiotagged birds and was within the spatial domain of the EDEN model. In ARCMAP, each foraging location and each random location was classified by five hydrological variables, four vegetation classes, vegetation diversity, and soil phosphorus. We chose these variables, which represent processes that occur over a wide range of temporal scales (daily to decadal), to capture scale-dependent
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habitat-selection responses. Daily changes were calculated for water depth, recession rate, days since drydown and dry-to-wet reversal, all variables calculated over unique time intervals within a maximum -year hydrological time-frame. All other variables (hydroperiod, vegetation, vegetation diversity, and soil phosphorus) change more slowly, and their availability was assumed to be constant within the study period. Selection for water depth was modeled as a quadratic function (e.g., Bancroft et al. , Russell et al. ) because wading birds do not use habitat with highly negative (below ground) or large values of depth. Because EDEN cell values represent the average depth for a × m cell, cells with a depth of less than zero may still have surface water present. A quadratic term for phosphorus was also included in the models because we thought that there could be a similar optimal range at moderate values because of increased nutrient enrichment before cattails (Typha spp.) became established. Although wading birds do not directly respond to increased phosphorus loading, they could be affected through indirect pathways that influence () prey abundance by changing the food web and () prey vulnerability by altering the vegetation structure (Davis et al. , McCormick et al. ). The shift from diverse floral communities to communities dominated by a few species that are highly competitive in eutrophic conditions (e.g, cattails) is a well-known response to increased phosphorus levels in the Everglades (Davis et al. , Newman et al. , McCormick et al. ). The hydrological variables recession rate, days since drydown, and hydroperiod were calculated as continuous variables for each date and EDEN grid cell. Daily recession rate was obtained by subtracting the water depth in a cell on a given day from the water depth week prior and weeks prior and dividing by and days, respectively. Positive recession rates indicate that water was receding, and negative rates indicate that water levels were rising. Days since drydown was calculated by counting the number of consecutive days since the water depth in a cell was greater than zero. Hydroperiod was obtained by calculating the mean number of days per year, out of the last years, that water depth was greater than zero. Dry-to-wet reversal was calculated as a binary variable for each unique combination of date and EDEN cell. This variable was given a value of if a cell had gone dry and was rewetted within a dry season, whereas the variable had a value of zero if the cell was still wet when the reversal occurred. Hydrographs of the study area were produced to display daily surface water dynamics (i.e., water depths, recession rate, reversals) for , , and an -year mean from to (Fig. ). Vegetation type and vegetation diversity were classified using Habitat and Land cover raster data (Florida Fish and Wildlife Conservation Commission ). This vegetation map uses Landsat Thematic Mapper satellite imagery from and was categorized into vegetation and land cover types at × m resolution. The five most common vegetation types in the study area were Jamaica Swamp Sawgrass (Cladium jamaicense) marsh (%), freshwater marsh and wet prairie (%), shrub and hardwood swamp (%), cattail (%), and open water and high-impact urban (%). Open water and high-impact urban vegetation classes represent areas with deeper water (e.g., canals) and topographical depressions (e.g., airboat trails) with little vegetative cover. Vegetation classes were binary variables based on the majority type within a -m buffer surrounding the observed point location.
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FIG. 1. Hydrograph depicting Everglades Depth Estimation Network (EDEN) mean water depths (cm) during the water years of 2005–2006 and 2006–2007, and the 8-year mean from 2000 to 2007.
Buffer size was determined by calculating the average error associated with all telemetry points. Vegetation diversity was calculated by summing the number of vegetation or land-use types within each buffer. System-wide soil phosphorus concentrations were obtained from a separate study that mapped soil phosphorus levels across the landscape in (see Corstanje et al. ). Statistical analyses.—Habitat variables were measured at observed foraging locations and compared with habitat variables measured at random locations that were available to birds on a given day. Because egrets and ibises nested at similar locations, we assumed a similar local foraging habitat for the two species. Models were constructed for each species × year combination separately with the a priori expectation that ibises would be more restricted in their use of habitats than egrets. The decision to compare species in separate models was made prior to sampling because of disparate population trends, whereas the decision to compare years in separate models was made after sampling but before the analyses. Correlation analyses were performed on all pairs of hydrological variables to assess colinearity. Variables were retained if r < .. Habitat selection by egrets and ibises in and was modeled using resource selection functions (RSFs) with multiple explanatory variables (RSFs yield values proportional to the probability of use of a resource unit; Manly et al. ). We estimated RSFs for both species in and using a stratified Cox proportional-hazards likelihood-maximization routine (PROC PHREG; SAS Institute ), which allows available resource units to change daily. The discrete choice model was estimated by maximizing the multinomial logit likelihood (Manly et al. ). The PHREG procedure performs regression analysis based on the Cox proportional hazards model, expressed as λij(t) = λ(t;Zi) = λo(t)exp(Z′ijβ) where λo(t) is a baseline hazard function, Zi is the string of explanatory variables for the jth individual, and β is the string of coefficients associated with the explanatory variables (Cox , Andersen and Gill , SAS Institute ). Partial likelihood
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functions were used to estimate β for each model variable (SAS Institute , Allison ). This model allows available resource units to change daily by incorporating a new set of hydrological variables for each daily time step. Hazard ratios were used to determine the magnitude of effect for each variable. We used the BRESLOW option in PHREG to handle ties because tied data were rare (Allison ). Four global models for foraging-site selection, one for each combination of species and year, included terms for depth + depth , - and -week recession rate, dry-to-wet reversal, days since drydown, hydroperiod, four vegetation classes (sawgrass marsh, freshwater marsh and wet prairie, cattail marsh, open water–high-impact urban), vegetation diversity, phosphorus, and phosphorus. Models were constructed using an information-theoretic model-selection approach (Burnham and Anderson ). Akaike’s information criterion for small sample sizes (AICc) was utilized to evaluate which a priori models were most parsimonious. Delta AIC (Δi) and AIC weights (wi) were calculated from AICc values. Models with the lowest AICc value were considered the best explanatory models, although additional competing models with ΔAICc < were considered equally plausible given the data (Burnham and Anderson ). Models with ΔAICc > were considered to have little to no support (Burnham and Anderson ). Model-averaged coefficients and standard errors (SE) were calculated for each parameter by averaging all models containing the variable in proportion to the AIC weight. For several variables known to have high importance for wading birds (e.g., water depth, recession rate), we conducted more detailed post hoc exploratory analyses to determine whether a change in selection patterns was due to a change in habitat use or habitat availability. We used two-way analysis of variance (ANOVA; Type III) in SAS, version . (SAS Institute ), to test for significant differences in habitat-selection parameters between species and years. Model evaluation.—For management purposes, a singlespecies habitat-suitability model that is robust with regard to hydrological conditions is preferable to one that applies only to specific hydrological conditions, although the latter may be desirable if the goal is to provide a mechanistic understanding. Thus, we developed a second set of species models using combined data from and . The accuracy of the combined models was then evaluated using independent observations of the presence or absence of wading-bird foraging distributions from systematic reconnaissance flights (SRFs; see Hoffman et al. , Bancroft et al. , Russell et al. ) during the dry seasons of –. Changes in the realized niche of each species were examined by fitting the models with occurrence data from one period. We then evaluated the performance of the models through hindcasting (Pearman et al. ). Data used to evaluate model predictions came from aerial wading-bird SRFs that documented the abundance, flock composition, and spatiotemporal distribution of foraging wading birds across the entire Everglades system. The surveys were conducted monthly during most of the dry season (January–June). Presence was defined as one or more birds of the target species (egret or ibis) detected in a given EDEN cell. Bird observations on SRF surveys were compared with predicted probability of use for the date at the midpoint of the three-day survey. Confusion matrices were
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calculated over a range of probability-of-use thresholds to generate receiver operating characteristic (ROC) plots (Deleo ), which provide a way to assess model usefulness. An ROC plot is obtained by plotting all sensitivity values (true positive fraction) on the y-axis against their equivalent ( – specificity) values (false positive fraction) for all available thresholds on the x-axis. The area under this curve (AUC) is an important index because it provides a single measure that is not dependent on a particular threshold (Deleo , Fielding and Bell ). An AUC value of . indicates no difference between the scores of used and unused groups, the range .–. is considered a model with useful application (Swets ), and a value of indicates no overlap in the distribution of group scores. We calculated AUC scores for each year of the evaluation to determine whether model performance was influenced by annual variability in hydrology. A multiple regression (PROC GLM in SAS) was conducted using mean habitat score, recession rate, and EDEN depth as predictor variables to determine the relationship between the hydrology of the sampling date and the predictive value of the model (AUC score). R ESULTS Habitat data from egret and ibis foraging locations were used in the analyses. We generated ~, random locations per day for days within the study period. Mean water depth was higher in and lower in than the mean of the prior years (Fig. ), supporting the development of four models, one for each species × year combination, to compare habitat-selection
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responses. Thirty-six candidate models were evaluated for each species × year combination and the combined year models. The top models for each group did not have a competing model with a ΔAICc < (Table ). Good habitat conditions ().—Probability of use for egrets was related to water depth, days since drydown, soil phosphorus level, dry-to-wet reversal, and presence of cattail vegetation (Table ). Analysis of water depth at foraging locations indicated selection of foraging sites with shallow water. Overall, the peak probability of use (>%) for depth was between – and cm (Fig. ). In addition, egrets selected foraging sites with increased days since drydown and also preferred sites that had gone dry and rewetted within the dry season, using these cells in higher proportion (%) than their availability in the landscape (%). The sign of the phosphorus coefficient revealed that sites with low soilphosphorus concentrations were used more often than their availability in the landscape. The hazard ratio of . indicates that for every mg kg– increase in phosphorus concentrations over the range to , mg kg–, the chance of the site being used was % less (Table ). Egrets selected sparse cattail as the main vegetation class (Table ); however, these sites were not in monotypic cattail stands associated with high soil phosphorus, except in the case of artificial openings (e.g., airboat trails). Probability of use for ibises in was related to water depth, soil phosphorus levels, -week recession rate, days since drydown, dry-to-wet reversal, and freshwater marsh and wet-prairie-dominated habitat (Table ). Analysis of water depth at foraging locations indicated selection of foraging sites with
TABLE 1. Resource-selection function analyses for wading-bird foraging in the Florida Everglades: Great Egret models in 2006 and 2007, White Ibis models in 2006 and 2007, and Great Egret and White Ibis combined-year models. Other variables include number of parameters (K), Akaike’s information criterion (adjusted for small sample sizes; AICc) scores, and Akaike weights (wi ). Models presented include only those that were within 7 AICc values of the top model (ΔAICc = 0). Model Great Egret 2006 Depth, reversal, days since drydown, cattail, P Global model Depth, 2-week recession rate, reversal, days since drydown, marsh, P2 Depth, reversal, days since drydown, P2 White Ibis 2006 Depth, 2-week recession rate, reversal, days since drydown, marsh, P2 Global model Great Egret 2007 Depth, 1-week recession rate, 2-week recession rate, reversal, marsh, cattail, high-impact–urban, P2 Global model Depth, 2-week recession rate, days since drydown, marsh, cattail, high-impact–urban, P2 White Ibis 2007 Depth, 2-week recession rate, reversal, hydroperiod, vegetation diversity, high-impact–urban, P 2 Global model Depth, 2-week recession rate, reversal, hydroperiod, vegetation diversity, cattail, high-impact–urban, P Depth, 2-week recession rate, reversal, hydroperiod, vegetation diversity, sawgrass, high-impact–urban, P Great Egret 2006 and 2007 Depth, 2-week recession rate, days since drydown, marsh, cattail, high-impact–urban, P2 Depth, 1-week recession rate, 2-week recession rate, reversal, marsh, cattail, high-impact–urban, P2 Depth, 2-week recession rate, reversal, marsh, cattail, high-impact–urban, P Global model White Ibis 2006 and 2007 Depth, marsh, high-impact–urban, vegetation diversity, P2
K
AICc
ΔAICc
wi
6 14 7 5
2,075.16 2,078.38 2,080.66 2,081.93
0.00 3.21 5.50 6.76
0.762 0.153 0.048 0.026
7 14
6,795.30 6,799.01
0.00 3.71
0.857 0.134
9 14 8
3,922.73 3,925.85 3,929.23
0.00 3.12 6.50
0.794 0.167 0.031
8 14 9 9
4,032.67 4,035.52 4,038.51 4,038.68
0.00 2.85 5.84 6.01
0.731 0.176 0.040 0.036
8 9 8 14
6,062.64 6,067.21 6,067.93 6,068.23
0.00 4.56 5.29 5.58
0.788 0.080 0.056 0.048
8
10,964.26
0.00
0.925
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TABLE 2. Model-averaged coefficient estimates, standard error (SE), and hazard ratios of top models (ΔAICc < 2) for habitat variables selected or avoided by Great Egrets and White Ibises in the Florida Everglades in good habitat conditions in 2006 (n = 90,710 and 91,066 foraging and random locations, respectively by species) and poor habitat conditions in 2007 (n = 109,713 and 109,725, respectively by species). β
Variable Depth + depth2 Days since drydown Phosphorus Dry-to-wet reversal Cattail marsh Depth + depth2 Phosphorus2 Recession rate (2) Days since drydown Dry-to-wet reversal Freshwater marsh Recession rate (2) Depth + depth2 Cattail marsh Phosphorus2 Open water–urban Dry-to-wet reversal Recession rate (1) Freshwater marsh 2
Depth + depth Recession rate (2) Phosphorus2 Open water–urban Hydroperiod Dry-to-wet reversal Vegetation diversity
SE
Great Egret 2006 –0.0014 0.000 0.0020 0.000 –0.4135 0.135 1.2110 0.346 1.0174 0.439 White Ibis 2006 –0.0030 0.000 –0.0418 0.009 –0.6148 0.121 0.0008 0.000 0.4609 0.149 0.3238 0.135 Great Egret 2006 0.7678 0.135 –0.0005 0.000 1.1866 0.400 –0.0397 0.021 1.1915 0.457 –0.4243 0.157 0.1922 0.088 0.4135 0.290 White Ibis 2007 –0.0012 0.000 0.7717 0.109 –0.0499 0.022 1.4976 0.441 –0.0035 0.001 –0.4525 0.168 0.1063 0.049
Hazard ratio 0.999 1.002 0.674 3.359 2.825 0.997 0.959 0.530 1.001 1.576 1.344 2.121 1.000 2.885 0.970 2.881 0.640 1.223 1.332 0.999 2.176 0.958 3.920 0.996 0.638 1.106
shallow water in a more restricted range than depths selected by egrets in . Overall, the peak probability of use for water depths was between – and cm (Fig. ). The sign of the coefficient revealed that ibises were similar to egrets in selecting for sites that had low phosphorus concentrations in relation to their availability in the landscape. Ibises selected sites where water receded slightly more slowly (. ± . cm day–) than system-wide recession rates (. ± . cm day–), as indicated by a negative RSF coefficient (Table ). Like egrets, ibises selected sites with increased days since drydown and sites that had dried and rewetted within the dry season; however, the effect was less pronounced (Table ) because ibises used foraging sites with fewer days since drydown than those used by egrets. Poor habitat conditions ().—Probability of use for egrets in was related to recession rate, water depth, cattail presence, soil phosphorus levels, open water–high-impact urban vegetation, dry-to-wet reversal, the -week recession rate, and freshwater marsh and wet prairie vegetation (Table ). Selectivity for rapid -week recession rates dramatically increased in ; probability of use increased to >% if water levels at a site receded even slightly. Recession rates were similar in the landscape
FIG. 2. Relative probability of use in relation to Everglades Depth Estimation Network (EDEN) water depth (depth + depth2). EDEN uses an integrated network of real-time water-level monitoring, ground-elevation modeling, and water-surface modeling at a spatial scale of 400 × 400 m. Depth is an average of a 400 × 400 m cell, so cells with negative depths can contain isolated pools that are below the mean ground elevation. Depth selectivity is highest for White Ibises (WHIB) in 2006, followed by Great Egrets (GREG) in 2006, White Ibises in 2007, and Great Egrets in 2007.
between years (Fig. ). However, use of rapid-recession sites increased significantly (F = ., df = and , P < .) in the year with poor habitat conditions (Fig. ). In , egrets selected a broad range of water depths, in contrast to the good year, when they were more selective of water depths (Fig. ). There was a significant (F = ,., df = and ,, P < .) decline in mean landscape water depths (. ± . cm) in comparison to the good year (. ± . cm; Fig. ), and water depths used by egrets declined significantly (F = ., df = and , P < .; Fig. ). Overall, depths between – and cm had the highest probability of use by egrets (Fig. ). Egrets avoided sites that underwent a dry-to-wet reversal in the poor year. In , .% of available sites fulfilled the criterion for a dry-to-wet reversal, but only .% of used locations met that criterion. The sign of the coefficient revealed that sites low in phosphorus concentrations were again used in higher proportion than their availability in the landscape. Low soil-phosphorus concentration was selected in both the good and the poor year, which indicates its importance in the model regardless of variation in hydrological conditions between years. Egrets also selected open water–high-impact urban habitat, rapid -week recession rates, and cattail presence (Table ). Probability of use for ibises in was related to water depth, -week recession rate, soil phosphorus levels, open water– high-impact urban vegetation, hydroperiod, dry-to-wet reversal, and vegetation diversity (Table ). Selectivity for rapid recession rates increased dramatically in . Similar to the egret model in the poor year (without the added selection for -week recession rates), probability of use increased to >% when water levels at a site receded even slightly. Use of areas with rapid recessions increased significantly (F = ., df = and , P < .) in comparison with the good year (Fig. ). Like egrets, ibises were less
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FIG. 3. Yearly mean (± SE) water-level recession rates at sites used by Great Egrets (GREG) in 2006 (n = 147) and 2007 (n = 273); at sites used by White Ibises (WHIB) in 2006 (n = 503) and 2007 (n = 285); and at random sites available in the landscape. Asterisks indicate significance level of P ≤ 0.05.
FIG. 4. Yearly mean (± SE) Everglades Depth Estimation Network (EDEN) depths at sites used by Great Egrets (GREG) in 2006 (n = 147) and 2007 (n = 273); at sites used by White Ibises (WHIB) in 2006 (n = 503) and 2007 (n = 285); and at random sites in the landscape. Asterisks indicate significance level of P ≤ 0.05.
selective of optimal water depths in the poor year than in (Fig. ). Analysis of water depth at foraging locations indicated selection of foraging sites similar to depths selected by egrets in the same year; however, ibises showed higher selectivity for depth. The water depths that ibises used also declined significantly (F = ., df = and , P < .) in the poor year (Fig. ). Overall, the range of water depths with the highest probability of use (>%) was between – and cm (Fig. ). Ibises avoided sites that underwent a dry-to-wet reversal, an opposite pattern from the good year. In the landscape, .% of sites fulfilled the criterion for a dry-to-wet reversal, whereas only .% of used locations met the criterion. Ibises also selected short-hydroperiod foraging sites. The coefficient revealed that sites with low phosphorus content were used in higher proportion than their availability in the landscape. In all models, high soil-phosphorus concentrations were avoided, which indicates an importance regardless of variation in hydrological conditions between years. Ibises also selected areas with open water–high-impact urban vegetation and high vegetation diversity (Table ). Model evaluation.—The combined final model using and for the egret included terms for depth, -week recession rate, soil phosphorus, cattail marsh vegetation class, open water– high-impact urban vegetation class, days since drydown, and freshwater-marsh and wet-prairie vegetation class (Table ). The AUC scores ranged from . to . in the years of the model evaluation, which suggests that the model weakly corresponded to historical distribution data. Mean EDEN water depth was the best predictor of model fit, with mean deeper EDEN depths corresponding to a higher AUC value. The regression indicates that at a mean EDEN depth below – cm the model lost its predictive power (AUC < .; regression equation: AUC = . + . [± .] * depth; n = , r = ., P < .). The combined final model for the ibis included terms for depth, soil phosphorus, open water–high-impact urban vegetation class, freshwater-marsh and wet-prairie vegetation class, and vegetation diversity (Table ). The AUC scores ranged from
. to . in the years of the model evaluation, which suggests that this model had better predictive power than the egret model. Mean water depth was the best predictor of model fit, with high AUC values corresponding to deeper water. The regression indicated that when water depth was less than – cm the model lost its predictive power (AUC < .; regression equation: AUC = . – . [± .] * depth; n = , r = ., P < .). D ISCUSSION Surface water dynamics.—Fluctuation of resources in the Everglades ecosystem is particularly pronounced because of periodic drying and flooding. Water depth is an important predictor of differential habitat selection in wading birds (Gawlik ) and of relative reproductive success (Frederick and Spalding , Herring TABLE 3. Model-averaged coefficient estimates, standard error (SE), and hazard ratios of top models (ΔAICc < 4) for habitat variables selected or avoided by Great Egrets (n = 200,423 foraging and random locations) and White Ibises (n = 200,791) in 2006 and 2007 in the Florida Everglades. Variable Depth + depth2 Recession rate (2) Phosphorus2 Cattail marsh Open water–urban Days since drydown Freshwater marsh Depth + depth2 Phosphorus2 Open water–urban Freshwater marsh Vegetation diversity
β Great Egret –0.0006 0.5725 –0.0301 1.1001 1.0929 0.0003 0.2881 White Ibis –0.0020 –0.0406 1.4167 0.1983 0.0670
SE
Hazard ratio
0.000 0.100 0.008 0.262 0.334 0.000 0.138
0.999 1.800 0.967 2.958 2.950 1.000 1.292
0.000 0.005 0.330 0.083 0.031
0.998 0.960 4.105 1.214 1.072
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et al. b) among species. Ibises always showed higher selectivity for water depth than egrets (Fig. ), which is consistent with the hypothesis that there is a greater cost to foraging in deeper water for searchers than for exploiters (Gawlik ). The greater range of water depths used by egrets could be explained by a combination of factors, including morphological adaptations (i.e., leg and bill length; Powell ), broad diet (Smith ), lower habitatspecificity (Beerens ), and greater physiological tolerance (Herring ). Both species showed higher selectivity for water depth during a good year than in a poor year (Fig. ), exercising a higher degree of specialization when conditions were more favorable and prey availability was high. These findings suggest that a range of water depths or habitat elevations may be necessary to support the entire wading-bird community in South Florida. Water-level recession rate is highly correlated with wadingbird distribution (Russell et al. ), number of breeding attempts (Kahl , Frederick and Spalding ), and location and nesting chronology of ibis colonies (Kushlan ). A rapid recession rate functions as a short-term process that can concentrate prey and provide a continual supply of new foraging patches as the drying edge of the marsh moves down the elevation gradient of the landscape. Temporary disruptions to the drying process, usually from dry-season rainfall, can lead to prey dispersal, reduced rates of capture (Gawlik ), increased search and travel times (Bancroft et al. ), and, ultimately, colony abandonment (Frederick and Collopy , Frederick and Spalding ). We have demonstrated for the first time that interannual variability in prey availability can result in a flexible response to recession rates. Although the landscape experienced similar recession rates in both years (~. cm day–), both species selected sites with more rapid recession rates in than in . Their behavioral response of selecting habitats with rapid recession rates in the poor year may have allowed access to new patches when prey was limited (i.e., “recession selectivity model”), a particularly important response because flocks may have rapidly depleted prey in the few foraging areas available. This evidence provides support for the “water recession model” (Kushlan , Frederick and Spalding , Russell et al. ), expanded here to reflect the increased importance of recession in a poor year. The water recession model is based on the premise that the intermediate depth provided by rapidly receding water concentrates aquatic prey items for wading birds and increases their availability in the landscape. Faster recession rates may concentrate more prey in shallow pools, but the mobility of each prey type plays an important role in prey availability and accessibility to wading birds, and potentially in foraging-habitat selection. Crayfish (Procambarus spp.) biomass was found to be much higher at a subset of ibis foraging sites in comparison with egret sites during the study period (J. M. Beerens unpubl. data). Crayfish often burrow as an adaptation to receding water levels and appear to concentrate less than fish and shrimp (Jordan ). The effect of crayfish burrowing to avoid desiccation could decrease ibises’ reliance on rapid recession rates for prey concentration. This point is also supported by recent research showing that the recession rate in the Everglades influenced the nest success of egrets but not that of ibises (Herring et al. b). Russell et al. () found that drying rate (i.e., recession rate) had a larger effect on the distribution of most wader species, including ibises, than either water depth or disruption of drying. Frederick and Spalding () suggested that this relationship
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was an artifact of long-term decadal-scale declines in prey populations arising from human effects on the landscape, and not necessarily an historical requirement of successful breeding. This notion is consistent with the observed annual-scale pattern of increased selectivity for rapid recession rates in years with low prey availability. A sudden increase in water depth, such as from rainfall during the dry season, can cause a greater reduction in prey density when water level is low than when water level is high (Garrett ). Thus, water conditions in previous months may be a better predictor of bird distribution immediately following a reversal (Bancroft et al. ) than more recent hydropatterns. Results indicate that the selection of sites that had undergone a reversal differed between years and may be associated with prey availability influenced by antecedent water conditions. For example, there was moderate nest failure following reversals in , despite dry conditions (Cook and Herring ). This was likely a consequence of the short period of inundation that preceded the dry season, which limited prey production. During periods of rising water, recently dried sites are often the only areas with foraging sites of appropriate water depths. Recently reflooded sites have lower fish densities than sites not dried recently (Trexler et al. ). A dry-to-wet-reversal variable was included in the models to draw the distinction between processes that change prey density of a patch versus processes that cause complete prey depletion. Although our definition of when an EDEN cell dried means that some areas within a cell may still have had standing water, prey populations in a cell are effectively zero because of prey mortality from physiological heat stress or desiccation (Chick et al. , Ruetz et al. ). Sites that had undergone a dry-to-wet reversal were avoided by both species in but were selected in , which suggests that both species were more tolerant of site rewetting in , possibly because of higher prey populations that year. Days since drydown and hydroperiod.—Habitat selection by egrets and ibises was highly dependent on present and past surface water conditions. Both influence the distribution (Trexler et al. ), demographics (Loftus and Eklund , Chick et al. ), and availability of prey species (Kushlan , ). Specifically, prey abundance is largely influenced by days since drydown (yearly scale; Trexler et al. ) and hydroperiod (decadal scale; Loftus and Eklund , Chick et al. ), whereas prey vulnerability to capture is influenced by surface water dynamics occurring within a single year (Gawlik ). This is a vital distinction, because one or more of the components of prey availability can limit populations of wading birds in any given year. Hydrological data from the present study suggest that many regions in dried down for the first time in several years. It appears that the egrets’ broader depth tolerance allowed them to forage in sites with more days since drydown and have access to a more abundant and diverse prey base (Loftus and Eklund , Trexler et al. ) with larger body sizes (Chick et al. ) than were available in sites within the narrower depth range of ibises. Conversely, it appears that prey populations in did not have enough time to recover, and neither species selected sites with a greater number of days since drydown. Most cells had gone dry in and were not rewetted until well after the onset of the rainy season. This effect is evident in the much lower mean (± SE) number of days since drydown in (. ± . days) than in ( ± . days).
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FIG. 5. Predicted habitat-suitability maps for 1 May and 27 May 2009, based on the combined year models for the Great Egret and White Ibis. Green represents the mean (+ 1 SD) probability of use of cells used by Great Egrets (n = 15,570) and White Ibises (n = 6,933) during the breeding seasons of 2002–2005. Orange represents probability of use + 1–2 SD from the mean, and red represents probability of use greater >2 SD from the mean. The lighter blue represents probability of use up to 1 SD below the mean, and dark blue represents the lowest probability of use. From 1 to 27 May, there was a 22.3-cm increase in mean water depth. Great Egret probability of use declined because of the change in recession rate; however, White Ibis probability of use was relatively unaffected. Great Egrets selected rapid recession rates, whereas this variable was absent from the White Ibis model.
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Our results are consistent with the hypothesis that ibises are dependent on shorter-hydroperiod wetlands during the breeding season (Kushlan , Hoffman et al. , Ogden ) and that they potentially rely heavily on prey species that rapidly colonize (i.e., shrimp and small fish) or emerge from burrows (i.e., crayfish) in newly flooded areas. Although days since drydown functioned mainly to increase prey abundance, it is the diverse array of hydroperiods in the landscape that provides continuous forging habitat for wading birds throughout the breeding season (Fleming et al. , Gawlik ). To sustain populations of wading birds, shorthydroperiod areas must have been inundated long enough to become populated with prey (> months; DeAngelis et al. ) and be available early in the dry season or later in extremely wet years, whereas long-hydroperiod areas must be available late in the dry season or earlier in particularly dry years. Phosphorus and vegetation.—The Everglades is an oligotrophic wetland in which phosphorus is the main nutrient limiting primary and secondary productivity (Harper ), which then affects aquatic-fauna community composition and fish biomass (Turner et al. ). Increases in phosphorus can be traced back to agricultural fertilizer runoff exacerbated by water-management practices and are associated with cattail monoculture invasion (McCormick et al. ). All models in the present study suggested that egrets and ibises avoid high soil phosphorus. However, it is likely that dense cattail invasion and the resulting loss of slough (i.e., open water) was avoided, not the high soil phosphorus itself. Bancroft et al. () found a positive association of area of slough and wading-bird abundance. Interestingly, Bancroft et al. () also found that egret abundance was positively associated with cattails, likely because of the increase in prey biomass from nutrient enrichment (Turner et al. , Trexler et al. ) coupled with artificial access to the prey base from airboat trails that provide open-water habitat (Bancroft et al. , Crozier and Gawlik ). This relationship was not exhibited by ibises. However, similar to egrets in (poor year), ibises selected open water–high-impact urban habitat, cells that are highly associated with artificial openings (e.g., areas surrounding semipermanent built structures utilized for recreational purposes and their associated network of airboat trails). Low food availability strongly influenced habitat selection by both species, particularly selection for short-term explanatory variables involved in the concentration of prey (e.g., recession rates and site reversal). In years when food is limited by prey production, as in , birds may be more dependent on recession rate to increase prey density through a concentration effect and more likely to avoid concentration-diluting reversals (i.e., recession selectivity model). However, in years when prey production is high, variables that affect prey concentrations would be less important than long periods of inundation, which increase fish density without concentrations. The differential selection of these features between years suggests that the species have flexible selection patterns based on food availability. This behavioral mechanism would be particularly advantageous in a highly dynamic system like the Everglades. The avoidance of high soil-phosphorus concentrations remained constant for both species through both years and, therefore, did not vary with changes in resource availability. Model evaluation and application.—The evaluation of the species–habitat suitability models showed that despite the
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dynamic nature of this study system, the ibis model was a useful application because its mean AUC score exceeded . (Swets ). In all years, the ibis model exhibited greater accuracy than the egret model (AUC ~.), conceivably a result of the narrower behavioral niche of ibises over a range of habitat conditions. The RSF model evaluation indicated that model performance for both species was consistent throughout a wide range of annual hydrological conditions. However, regardless of year, both models better predicted habitat use in poorer habitat (i.e., deeper water) when less niche overlap was predicted. Our results corroborate prior experimental work describing the ibis as more restricted in its use of habitats than the egret (Gawlik ). If egrets are better able to exploit food resources in a wider range of habitat conditions than ibises, we might expect an increased divergence of population trends between the two species as the number of poor habitat years increase. Further studies of the response of other wading-bird species along the searcher–exploiter continuum to fluctuating levels of food availability are needed to determine the utility of this classification framework. A strength of the present study is that habitat selection was assessed daily as availability changed within a breeding season. Because input for the hydrological variables can be obtained from the EDEN in real time, this allows for timely, spatially explicit management recommendations on the suitability of the landscape for two species of wading birds with different foraging strategies (for sample maps, see Fig. ). In addition, these models could allow for the evaluation of restoration scenarios if the input hydrological data were derived from the output of the hydrological models used for restoration planning. The range of species-specific habitat conditions generated from restoration scenarios could then be used to reduce the number of poor years for the most ecologically sensitive species. This approach could be used throughout wetland systems as a way of assessing long-term habitat quality of avian guilds with flexible habitat selection and overlapping niche structure. ACKNOWLEDGMENTS Funding for this research project was provided by the U.S. Fish and Wildlife Service (USFWS) to D.E.G. and G.H. Data on prey availability were provided through a grant to D.E.G. from the South Florida Water Management District (SFWMD). The SFWMD also supported efforts to validate final models. A grant from the Army Corps of Engineers helped support publication of this manuscript. We are grateful to T. Dean at the USFWS for help in implementing this study, M. Barrett at the Arthur R. Marshall Loxahatchee National Wildlife Refuge, S. Newman at SFWMD, and B. Botson at Florida Atlantic University. M. Kobza, D. Marx, M. Merrill, E. Noonburg, Z. Xie, and two anonymous reviewers provided valuable comments on drafts of the manuscript. We thank our field research crews and fellow researchers that assisted in collection of field data: T. Anderson, T. Beck, J. Benfield, H. Herring, N. Hill, A. Horton, B. Imdieke, and S. Lantz. Research techniques were approved by the Florida Atlantic University Institutional Animal Care and Use Committee (protocol A), and conducted under USFWS Research Permit and Florida Fish and Wildlife Conservation Commission, Scientific Research Permit WX.
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