Many wild species are affected by human activities occurring at broad spatial scales. For instance, in South America, habitat loss threatens Greater Rhea (Rhea americana) populations, making it important to model and map their habitat to better target conservation efforts. Spatially explicit habitat modeling is a powerful approach to understand and predict species occurrence and abundance. One problem with this approach is that commonly used land cover classifications do not capture the variability within a given land cover class that might constitute important habitat attribute information. Texture measures derived from remote sensing images quantify the variability in habitat features among and within habitat types; hence they are potentially a powerful tool to assess species-habitat relationships. Our goal was to explore the utility of texture measures for habitat modeling and to develop a habitat suitability map for Greater Rheas at the home range level in grasslands of Argentina. Greater Rhea group size obtained from aerial surveys was regressed against distance to roads, houses, and water, and land cover class abundance (dicotyledons, crops, grassland, forest, and bare soil), normalized difference vegetation index (NDVI), and selected first- and second-order texture measures derived from Landsat Thematic Mapper (TM) imagery. Among univariate models, Rhea group size was most strongly positively correlated with texture variables derived from near infrared reflectance measurement (TM band 4). The best multiple regression models explained 78% of the variability in Greater Rhea group size. Our results suggest that texture variables captured habitat heterogeneity that the conventional land cover classification did not detect. We used Greater Rhea group size as an indicator of habitat suitability; we categorized model output into different habitat quality classes. Only 16% of the study area represented high-quality habitat for Greater Rheas (group size =15). Our results stress the potential of image texture to capture within-habitat variability in habitat assessments, and the necessity to preserve the remaining natural habitat for Greater Rheas.
File: Bellis_etal_EA_2008_0.pdf
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Patterns of association between humans and biodiversity typically show positive, negative, or negative quadratic relationships and can be described by 3 hypotheses: biologically rich areas that support high human population densities co-occur with areas of high biodiversity (productivity); biodiversity decreases monotonically with increasing human activities (ecosystem stress); and biodiversity peaks at intermediate levels of human influence (intermediate disturbance). To test these hypotheses, we compared anthropogenic land cover and housing units, as indices of human influence, with bird species richness and abundance across the Midwestern United States. We modeled richness of native birds with 12 candidate models of land cover and housing to evaluate the empirical evidence. To assess which species were responsible for observed variation in richness, we repeated our model-selection analysis with relative abundance of each native species as the response and then asked whether natural-history traits were associated with positive, negative, or mixed responses. Native avian richness was highest where anthropogenic land cover was lowest and housing units were intermediate based on model-averaged predictions among a confidence set of candidate models. Eighty-three of 132 species showed some pattern of association with our measures of human influence. Of these species approximately 40% were negatively associated, approximately 6% were positively associated, and approximately 7% showed evidence of an intermediate relationship with human influence measures. Naturalhistory traits were not closely related to the direction of the relationship between abundance and human influence. Nevertheless, pooling species that exhibited any relationship with human influence and comparing them with unrelated species indicated they were significantly smaller, nested closer to the ground, had shorter incubation and fledging times, and tended to be altricial. Our results support the ecosystem-stress hypothesis for the majority of individual species and for overall species diversity when focusing on anthropogenic land cover. Nevertheless, the great variability in housing units across the land-cover gradient indicates that an intermediate-disturbance relationship is also supported. Our findings suggest preemptive conservation action should be taken, whereby areas with little anthropogenic land cover are given conservation priority. Nevertheless, conservation action should not be limited to pristine landscapes because our results showed that native avian richness and the relative abundance of many species peaked at intermediate housing densities and levels of anthropogenic land cover
File: Lepczyk_etal_ConsBio_2010.pdf
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The lack of management experience at the landscape scale and the limited feasibility of experiment at this scale have increased the use of scenario modeling to analyze the effects of different management actions on focal species. However, current modeling approaches are poorly suited for the analysis of viability in dynamic landscapes. Demographic (e.g., metapopulation) models of species living in these landscapes do not incorporate the variability in spatial patterns of early successional habitats, and landscape models have not been linked to population viability models. We link a landscape model to a metapopulation model and demonstrate the use of this model by analyzing the effect of forest management options on the viability of the Sharp-tailed Grouse ( Tympanuchus phasianellus) in the Pine Barrens region of northwestern Wisconsin (U.S.A.). This approach allows viability analysis based on landscape dynamics brought about by processes such as succession, disturbances, and silviculture. The landscape component of the model (LANDIS) predicts forest landscape dynamics in the form of a time series of raster maps. We combined these maps into a time series of patch structures, which formed the dynamic spatial structure of the metapopulation component (RAMAS). Our results showed that the viability of Sharp-tailed Grouse was sensitive to landscape dynamics and demographic variables such as fecundity and mortality. Ignoring the landscape dynamics gave overly optimistic results, and results based only on landscape dynamics (ignoring demography) lead to a different ranking of the management options than the ranking based on the more realistic model incorporating both landscape and demographic dynamics. Thus, models of species in dynamic landscapes must consider habitat and population dynamics simultaneously.
File: Akcakaya_etal_ConsBio2004.pdf
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We tested image texture as a predictor of bird species richness in a semi-arid landscape of New Mexico. Bird species richness was summarized from 10-min point counts conducted at 12 points within 42 plots (108 ha each) from 1996 to 1998. We calculated 14 first- and second-order texture measures in eight different window sizes on a set of digital orthophotos acquired in 1996. For each of the 42 plots, we summarized mean and standard deviation of each texture value within multiple window sizes. The relationship between image texture and average bird species richness was assessed using linear regression models. Single image texture measures such as the standard deviation described up to 57% of the variability in species richness. Coupling multiple measures of texture or coupling elevation with a single texture measure described up to 63% of the variability in bird species richness. Models incorporating two measures of texture and coarse habitat type described 76% of the variability in bird species richness. These results show that image texture analysis is a very promising tool for characterizing habitat structure and predicting patterns of species richness in semi-arid ecosystems. This method has several advantages over methods that rely on classified imagery, including cost-effectiveness, incorporation of within-habitat vegetation variability, and elimination of errors associated with boundary delineation.
File: st-louis-rse-2006.pdf
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Habitat quality is an important consideration when identifying source and sink habitat and setting priority areas for avian conservation. The problem is that different measures may lead to different conclusions about habitat quality, and may also vary in the resources required to estimate them. Individual level measures, such as nest success, and fecundity, will often identify different high quality habitats than population level measures, such as abundance or the number of fledglings produced per unit area. We tested measures of fitness in the Black-throated Sparrow both at the individual and at the population level for six habitats in the northern Chihuahuan Desert, to explore their value as indicators of habitat quality. We compared clutch size, number of nestlings per nest, number of fledglings per successful nest, nest density, nest success, daily nest survival rate, season-long fecundity, number of fledglings produced per 100 ha, and adult abundance, in each habitat type. We also modeled source-sink dynamics to estimate the scale at which they operate, to infer survival rates, and to ascertain the relative source potential of each habitat. We found that fecundity is the best indicator of individual level habitat quality but a poor indicator of population level habitat quality. Nest success (or fecundity, if resources are available to adequately estimate it) plus nest density provide the most robust indicator of population level habitat quality, which is the level at which priority habitats for conservation should be identified. Mesa grassland and black grama grassland functioned as source habitats most consistently, and mesquite was consistently a sink but also probably a reservoir of individuals available to occupy other habitats.
File: Pidgeon_etal_BioCons_2006.pdf
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In the United States, housing density has substantially increased in and adjacent to forests. Our goal in this study was to identify how housing density and human populations are associated with avian diversity. We compared these associations to those between landscape pattern and avian diversity, and we examined how these associations vary across the conterminous forested United States. Using data from the North American Breeding Bird Survey, the U.S. Census, and the National Land Cover Database, we focused on forest and woodland bird communities and conducted our analysis at multiple levels of model specificity, first using a coarse-thematic resolution (basic models), then using a larger number of fine-thematic resolution variables (refined models). We found that housing development was associated with forest bird species richness in all forested ecoregions of the conterminous United States. However, there were important differences among ecoregions. In the basic models, housing density accounted for ,5% of variance in avian species richness. In refined models, 85% of models included housing density and/or residential land cover as significant variables. The strongest guild response was demonstrated in the Adirondack-New England ecoregion, where 29% of variation in richness of the permanent resident guild was associated with housing density. Model improvements due to regional stratification were most pronounced for cavity nesters and short-distance migrants, suggesting that these guilds may be especially sensitive to regional processes. The varying patterns of association between avian richness and attributes associated with landscape structure suggested that landscape context was an important mediating factor affecting how biodiversity responds to landscape changes. Our analysis suggested that simple, broadly applicable, land use recommendations cannot be derived from our results. Rather, anticipating future avian response to land use intensification (or reversion to native vegetation) has to be conditioned on the current landscape context and the species group of interest. Our results show that housing density and residential land cover were significant predictors of forest bird species richness, and their prediction strengths are likely to increase as development continues.
File: pidgeon_etal_ecap_07.pdf
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Aim To investigate the relationships between bird species richness derived from the North American Breeding Bird Survey and estimates of the average, minimum, and the seasonal variation in canopy light absorbance (the fraction of absorbed photosynthetically active radiation, fPAR) derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). Location Continental USA. Methods We describe and apply a 'dynamic habitat index' (DHI), which incorporates three components based on monthly measures of canopy light absorbance through the year. The three components are the annual sum, the minimum, and the seasonal variation in monthly fPAR, acquired at a spatial resolution of 1 km, over a 6-year period (2000-05). The capacity of these three DHI components to predict bird species richness across 84 defined ecoregions was assessed using regression models. Results Total bird species richness showed the highest correlation with the composite DHI [R2 = 0.88, P < 0.001, standard error of estimate (SE) = 8 species], followed by canopy nesters (R2 = 0.79, P < 0.001, SE = 3 species) and grassland species (R2 = 0.74, P < 0.001, SE = 1 species). Overall, the seasonal variation in fPAR, compared with the annual average fPAR, and its spatial variation across the landscape, were the components that accounted for most (R2 = 0.55-0.88) of the observed variation in bird species richness. Main conclusions The strong relationship between the DHI and observed avian biodiversity suggests that seasonal and interannual variation in remotely sensed fPAR can provide an effective tool for predicting patterns of avian species richness at regional and broader scales, across the conterminous USA.
File: Coops_2009_JBioGeog_0.pdf
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Conservation of biodiversity requires information at many spatial scales in order to detect and preserve habitat for many species, often simultaneously. Vegetation structure information is particularly important for avian habitat models and has largely been unavailable for large areas at the desired resolution. Airborne LiDAR, with its combination of relatively broad coverage and ?ne resolution provides existing new opportunities to map vegetation structure and hence avian habitat. Our goal was to model the richness of forest songbirds using forest structure information obtained from LiDAR data. In deciduous forests of southern Wisconsin, USA, we used discrete-return airborne LiDAR to derive forest structure metrics related to the height and density of vegetation returns, as well as composite variables that captured major forest structural elements. We conducted point counts to determine total forest songbird richness and the richness of foraging, nesting, and forest edge-related habitat guilds. A suite of 35 LiDAR variables were used to model bird species richness using best-subsets regression and we used hierarchical partitioning analysis to quantify the explanatory power of each variable in the multivariate models. Songbird species richness was correlated most strongly with LiDAR variables related to canopy and midstory height and midstory density (R 2 =0.204, pb0.001). Richness of species that nest in the midstory was best explained by canopy height variables (R 2 =0.197, pb0.001). Species that forage on the ground responded to mean canopy height and the height of the lower canopy (R 2 =0.149, pb0.005) while aerial foragers had higher richness where the canopy was tall and dense and the midstory more sparse (R 2 =0.216, pb0.001). Richness of edge-preferring species was greater where there were fewer vegetation returns but higher density in the understory (R 2 =0.153, pb0.005). Forest interior specialists responded positively to a tall canopy, developed midstory, and a higher proportion of vegetation returns (R 2 =0.195, pb0.001). LiDAR forest structure metrics explained between 15 and 20% of the variability in richness within deciduous forest songbird communities. This variability was associated with vertical structure alone and shows how LiDAR can provide a source of complementary predictive data that can be incorporated in models of wildlife habitat associations across broad geographical extents.
File: Lesaketal.pdf
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The federally endangered Karner blue butterfly (Lycaeides samuelis) is the focal species for a conservation plan designed to create and maintain barrens habitats. We investigated whether habitat management for Karner blue butterflies influences avian community structure at Fort McCoy Military Installation in Wisconsin, USA. From 2007 through 2009 breeding bird point count and habitat characteristic data were collected at 186 sample points in five habitat types including two remnant barrens types, barrens habitat restored from woodland and managed specifically for the Karner blue butterfly, and two woodland habitat types. Although the bird community of managed barrens was not identical to the communities of remnant barrens, the Field Sparrow (Spizella pusilla), a species of conservation concern, and sparse canopy associated bird species, such as the Baltimore Oriole (Icterus galbula) and Eastern Bluebird (Sialia sialis) were predicted to occupy managed barrens and remnant barrens in similar proportions. Adjacent habitat was the most influential factor in determining the community of bird species using the managed barrens. In Wisconsin, and likely throughout the range of the Karner blue butterfly, management for the butterfly creates habitat that attracts a bird community similar to that of remnant barrens, and benefits several avian species of conservation concern. Additionally, the landscape context surrounding the managed habitat influences avian community composition. Managed barrens that are adjacent to remnant barrens, rather than adjacent to woodland habitats, have the highest potential for conserving barrens breeding birds.
File: Wood2011.pdf
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Heat waves are expected to become more frequent and severe as climate changes, with unknown consequences for biodiversity. We sought to identify ecologically-relevant broad-scale indicators of heat waves based on MODIS land surface temperature (LST) and interpolated air temperature data and assess their associations with avian community structure. Speci ? cally, we asked which data source, time periods, and heat wave indices best predicted changes in avian abundance and species richness. Using mixed effects models, we analyzed associations between these indices and data from the North American Breeding Bird Survey in the central United States between 2000 and 2007 in four ecoregions and ? ve migratory and nesting species groups. We then quanti?ed avian responses to scenarios of severe, but commonly-occurring early, late, and summer-long heat waves. Indices based on MODIS LST data, rather than interpolated air temperatures, were more predictive of avian community structure. Avian communities were more related to 8-day LST exceedances (positive anomalies only); and were generally more sensitive to summer-long heat waves. Across the region, abundance, and to a lesser extent, species richness, declined following heat waves. Among the ecoregions, relationships were most consistently negative in the southern and montane ecoregions, but were positive in a more humid northern ecoregion. Among migratory groups, permanent resident species were the most sensitive, declining in abundance following a summer-long heat wave by 19% and 13% in the montane and southern ecoregions, respectively. Ground-nesting species, which declined in the south by 12% following a late summer heat wave, were more sensitive than avifauna overall. These results demonstrate the value of MODIS LST data for measuring ecologically-relevant heat waves across large regions. Ecologically, these ? ndings highlight the importance of extreme events for avian biodiversity and the considerable variation in response to environmental change associated with different functional groups and geographic regions. The magnitude of the relationships between avian abundance and heat waves reported here raises concerns about the impacts of more frequent and severe heat waves in a warming climate.
File: Albright_et_al_2011_Heat_waves_and_bird_communities_0.pdf
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