California oak savanna is a habitat of sparse tree canopy that extends from northern Baja California to southern British Columbia and is under threat from land-use pressures such as conversion to agriculture, overgrazing, urban development, and fire suppression. Bird-conservation plans have been drafted for the region's oak woodlands. Yet it is unclear whether birds use California oak savanna at different frequencies than they do neighboring oak habitats. In the foothills of the central and northern Sierra Nevada, California, we explored patterns of avian community structure and habitat occupancy in four habitats: blue oak (Quercus douglasii) savanna with a well-developed grass and forb layer, blue oak savanna with a well-developed shrub layer, and two habitats with a denser canopy, blue oak woodland, and montane hardwood. Additionally, we assessed the effect of habitat characteristics on avian community structure and occupancy. Avian communities were uniquely grouped among the four habitats. Five species of management and conservation concern-the Western Kingbird (Tyrannus verticalis), Western Bluebird (Sialia mexicana), Lark Sparrow (Chondestes grammacus), Western Meadowlark (Sturnella neglecta), and Bullock's Oriole (Icterus bullockii)-were predicted to occupy oak savanna habitats at frequencies higher than in oak woodland or montane hardwood. Shrub cover was the most influential habitat characteristic shaping the avian community and was negatively associated with occupancy of the five savanna-affiliated birds. The distinctive structure and occupancy patterns observed for species of concern in California oak savanna suggest that birds perceive this as unique habitat, highlighting the need for its conservation.
File: Wood_etal_Condor2013.pdf
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For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value ,0.001), and avian species richness among habitats (R2 = 0.54, p-value ,0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value ,0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas.
File: Wood-2013-PLOS-One.pdf
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Land use is driven by socio-economic factors that must be understood in order to mitigate habitat loss. Econometric land-use models describe how land use is affected by socio-economic factors, such as financial returns to different uses of land, and they can be linked to biological models to provide new insight for conservation. Our goal was to evaluate the effects of future land use change on the habitat of forest breeding bird species in northern Wisconsin. Specifically, we estimated the effects of land use change on the amount of habitat available and compared the effects of economic policy scenarios on bird habitat. To do this, we coupled a spatially-explicit econometric model of land use change on private lands with models of northern Wisconsin forest bird potential habitat, comparing a 50-yr baseline projection with a scenario providing incentives for forest growth and a high urban growth scenario. The baseline scenario suggests an average of 438,705 ha of forest lost (10%), with 1.9% of that saved under the Forest Incentive scenario, and a 1.6% greater loss for the Urban Growth scenario. Under baseline projections boreal birds experienced the least amount of habitat loss (2-3%), and deciduous forest birds the most (6-8%). For some species, the projected loss of habitat exacerbates ongoing long-term declining population trend. Coupled economic-ecological models can be used to evaluate alternative incentive programs and to explore the complex interactions between policy, land use change, and broad spatial scale ecological processes that are highly relevant to conservation.
File: Beaudry-etal-ConsBio-2013_0.pdf
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With limited resources for habitat conservation, the accurate identification of high-value avian habitat is crucial. Habitat structure affects avian biodiversity but is difficult to quantify over broad extents. Our goal was to identify which measures of vertical and horizontal habitat structure are most strongly related to patterns of avian biodiversity across the conterminous United States and to determine whether new measures of vertical structure are complementary to existing, primarily horizontal, measures. For 2,546 North American Breeding Bird Survey routes across the conterminous United States, we calculated canopy height and biomass from the National Biomass and Carbon Dataset (NBCD) as measures of vertical habitat structure and used land-cover composition and configuration metrics from the 2001 National Land Cover Database (NLCD) as measures of horizontal habitat structure. Avian species richness was calculated for each route for all birds and three habitat guilds. Avian species richness was significantly related to measures derived from both the NBCD and NLCD. The combination of horizontal and vertical habitat structure measures was most powerful, yielding high R2 values for nationwide models of forest (0.70) and grassland (0.48) bird species richness. New measures of vertical structure proved complementary to measures of horizontal structure. These data allow the efficient quantification of habitat structure over broad scales, thus informing better land management and bird conservation. Received 10 January 2013, accepted 30 September 2013.
File: culbert-etal-Auk-2013.pdf
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Model averaging is gaining popularity among ecologists for making inference and predictions. Methods for combining models include Bayesian model averaging (BMA) and Akaike's Information Criterion (AIC) model averaging. BMA can be implemented with different prior model weights, including the Kullback-Leibler prior asso- ciated with AIC model averaging, but it is unclear how the prior model weight affects model results in a predictive context. Here, we implemented BMA using the Bayesian Information Criterion (BIC) approximation to Bayes factors for building predictive models of bird abundance and occurrence in the Chihuahuan Desert of New Mexico. We examined how model predictive ability differed across four prior model weights, and how averaged coef?cient esti- mates, standard errors and coef?cients' posterior probabil- ities varied for 16 bird species. We also compared the predictive ability of BMA models to a best single-model approach. Overall, Occam's prior of parsimony provided the best predictive models. In general, the Kullback-Leibler prior, however, favored complex models of lower predictive ability. BMA performed better than a best single-model approach independently of the prior model weight for 6 out of 16 species. For 6 other species, the choice of the prior model weight affected whether BMA was better than the best single-model approach. Our results demonstrate that parsimonious priors may be favorable over priors that favor complexity for making predictions. The approach we present has direct applications in ecology for better pre- dicting patterns of species' abundance and occurrence.
File: St-Louis_etAl_Oecologia_Bayesian_Priors.pdf
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Since European settlement, hardwood dominated forests of the Upper American Midwest have under- gone compositional changes due to ?re suppression and changes in land use. It is not clear how these changes affect songbirds during spring migration. In 2009 and 2010, we quanti?ed foraging behavior by migratory songbirds during spring migration and collected data on tree and sapling diversity in the Kickapoo Valley Reserve in southwestern Wisconsin. Furthermore, we compared the 1840s distribution of tree species (from Public Land Survey System witness tree records) with current (2010) and estimated future (sapling) tree-composition to better understand how historic and future changes in tree composi- tion may impact migratory songbirds at spring migration stopover sites. Six tree species were selected as foraging substrates in higher proportion than they were available by eight migratory songbirds, including trees adapted to moderate shade such as northern red oak (Quercus rubra), white oak (Quercus alba), American elm (Ulmus americana), and slippery elm (Ulmus rubra), and shade-intolerant species such as big-tooth aspen (Populus grandidentata), and paper birch (Betula papyrifera). Whereas three shade-toler- ant tree species were selected in far lower proportion than they were available by eight migratory song- birds, including sugar maple (Acer saccharum), red maple (Acer rubrum), and basswood (Tilia americana). We found evidence that food accessibility, as measured by a novel approach relating a bird's attacks and search efforts to the average leaf petiole length of a tree species, was strongly inversely related with a bird's foraging success (q = =0.96, p-value <0.001). Although tree-species composition changed considerably from the 1840s to 2010, in both time periods the forest was dominated by a mix of sugar maple and oak species. However, sugar maple saplings currently form a nearly continuous layer in the understory and there is very low recruitment of shade-intolerant or moderately shade-tolerant species, suggesting a future shift towards dominance by shade-tolerant species. Our results suggest the current trajectory of forest succession may result in future conditions that provide lower quality foraging for migratory songbirds during spring migration than they currently experience in the Upper American Midwest.
File: Woodetal2012.pdf
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File: Woolly-necked_Stork_Yunnan.pdf
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Avian biodiversity is threatened, and in order to prioritize limited conservation resources and conduct effec- tive conservation planning a better understanding of avian species richness patterns is needed. The use of image texture measures, as a proxy for the spatial structure of land cover and vegetation, has proven useful in explaining patterns of avian abundance and species richness. However, prior studies that modeled habitat with texture measures were conducted over small geographical extents and typically focused on a single habitat type. Our goal was to evaluate the performance of texture measures over broad spatial extents and across multiple habitat types with varying levels of vertical habitat structure. We calculated a suite of texture measures from 114 Landsat images over a study area of 1,498,000 km^2 in the Midwestern United States, which included habitats ranging from grassland to forest. Avian species richness was modeled for several functional guilds as a function of image texture. We subsequently compared the explanatory power of texture-only models with models ?tted using landscape composition metrics derived from the National Land Cover Dataset, as well as models ?tted using both texture and composition metrics. Measures of image texture were effective in modeling spatial patterns of avian species richness in multiple habitat types, explaining up to 51% of the variability in species richness of permanent resident birds. In comparison, landscape composition metrics explained up to 56% of the variability in permanent resident species richness. In the most heavily forested ecoregion, texture-measures outperformed landscape metrics, and the two types of measurements were complementary in multivariate models. However, in two out of three ecoregions examined, landscape composition metrics consistently performed slightly better than texture measures, and the variance explained by the two types of measures overlapped considerably. These results show that image texture measures derived from satellite imagery can be an important tool for modeling patterns of avian species richness at broad spatial extents, and thus assist in conservation planning. However, texture measures were slightly inferior to landscape composition metrics in about three-fourths of our models. Therefore texture measures are best considered in conjunction with landscape metrics (if available) and are best used when they show explanatory ability that is complementarity to landscape metrics.
File: Culbert_RSE_Texture_0.pdf
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Changes in land use and land cover have affected and will continue to affect biological diversity worldwide. Yet, understanding the spatially extensive effects of land-cover change has been challenging because data that are consistent over space and time are lacking. We used the U.S. National Land Cover Dataset Land Cover Change Retrofit Product and North American Breeding Bird Survey data to examine land-cover change and its associations with diversity of birds with principally terrestrial life cycles (landbirds) in the conterminous United States. We used mixed-effects models and model selection to rank associations by ecoregion. Land cover in 3.22% of the area considered in our analyses changed from 1992 to 2001, and changes in species richness and abundance of birds were strongly associated with land-cover changes. Changes in species richness and abundance were primarily associated with changes in nondominant types of land cover, yet in many ecoregions different types of land cover were associated with species richness than were associated with abundance. Conversion of natural land cover to anthropogenic land cover was more strongly associated with changes in bird species richness and abundance than persistence of natural land cover in nearly all ecoregions and different covariates were most strongly associated with species richness than with abundance in 11 of 17 ecoregions. Loss of grassland and shrubland affected bird species richness and abundance in forested ecoregions. Loss of wetland was associated with bird abundance in forested ecoregions. Our findings highlight the value of understanding changes in nondominant land cover types and their association with bird diversity in the United States.
File: Rittenhouse_etal_2012_BioInvasions.pdf
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Landscape pattern metrics are widely used for predicting habitat and species diversity. However, the relationship between landscape pattern and species diversity is typically measured at a single spatial scale, even though both landscape pattern, and species occurrence and community composition are scale-dependent. While the effects of scale on landscape pattern are well documented, the effects of scale on the relationships between spatial pattern and species richness and composition are not well known. Here, our main goal was to quantify the effects of cartographic scale (spatial resolution and extent) on the relationships between spatial pattern and avian richness and community structure in a mosaic of grassland, woodland, and savanna in central Wisconsin. Our secondary goal was to evaluate the effectiveness of a newly developed tool for spatial pattern analysis, multiscale contextual spatial pattern analysis (MCSPA), compared to existing landscape metrics. Landscape metrics and avian species richness had quadratic, exponential, or logarithmic relationships, and these patterns were generally consistent across two spatial resolutions and six spatial extents. However, the magnitude of the relationships was affected by both resolution and extent. At the finer resolution (10-m), edge density was consistently the best predictor of species richness, followed by an MCSPA metric that measures the standard deviation of woody cover across extents. At the coarser resolution (30-m), NDVI was the best predictor of species richness by far, regardless of spatial extent. Another MCSPA metric that denotes the average woody cover across extents, together with percent of woody cover, were always the best predictors of variation in avian community structure. Spatial resolution and extent had varying effects on the relationships between spatial pattern and avian community structure. We therefore conclude that cartographic scale not only affects measures of landscape pattern per se, but also the relationships among spatial pattern, species richness, and community structure, often in complex ways, which reduces the efficacy of landscape metrics for predicting the richness and diversity of organisms.
File: BarMassada_Ecography2012.pdf
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