Anecdotal evidence suggests that socioeconomic shocks strongly affect wildlife populations, bu quantitative evidence is sparse. The collapse of socialism in Russia in 1991 caused a major socioeconomi shock, including a sharp increase in poverty. We analyzed population trends of 8 large mammals in Russi from 1981 to 2010 (i.e., before and after the collapse). We hypothesized that the collapse would first caus population declines, primarily due to overexploitation, and then population increases due to adaptation o wildlife to new environments following the collapse. The long-term Database of the Russian Federal Agenc of Game Mammal Monitoring, consisting of up to 50,000 transects that are monitored annually, provide an exceptional data set for investigating these population trends. Three species showed strong declines i population growth rates in the decade following the collapse, while grey wolf (Canis lupus) increased by mor than 150%. After 2000 some trends reversed. For example, roe deer (Capreolus spp.) abundance in 201 was the highest of any period in our study. Likely reasons for the population declines in the 1990s includ poaching and the erosion of wildlife protection enforcement. The rapid increase of the grey wolf populations i likely due to the cessation of governmental population control. In general, the widespread declines in wildlif populations after the collapse of the Soviet Union highlight the magnitude of the effects that socioeconomi shocks can have on wildlife populations and the possible need for special conservation efforts during suc times.
File: Bragina_et_al_CB_2015.pdf
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Drivers of biodiversity loss are increasingly broad in scale, requiring conservation planning to move towards range-wide assessments. This is especially challenging for migratory species, such as reindeer or caribou (Rangifer tarandus), which use only a small portion of their range at a given point in time, and for which some parts of their range, such as calving grounds, may be much more important than others. Our aim was to identify potential calving ground habitat of wild tundra reindeer populations throughout Russia, where scarce knowledge about seasonal reindeer habitat is an obstacle for conservation planning, and to assess possible impacts from oil and gas development and climate change.
File: Kuemmerle_etal_2014_DiversityandDistributions_0.pdf
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Assessing landscape connectivity is important to understand the ecology of landscapes and to evaluate alternative conservation strategies. The question is though, how to quantify connectivity appropriately, especially when the information available about the suitability of the matrix surrounding habitat is limited. Our goal here was to investigate the effects of matrix representation on assessments of the connectivityamong habitat patches and of the relative importance of individual patches for the connectivity within a habitat network. We evaluated a set of 50 9 50 km2 test areas in the Carpathian Mountains and considered three different matrix representations (binary, categorical and continuous) using two types of connections among habitat patches (shortest lines and least-cost paths). We compared connections, and the importance of patches, based on (1) isolation, (2) incidencefunctional, and (3) graph measures. Our results showed that matrix representation can greatly affect assessments of connections (i.e., connection length, effective distance, and spatial location), but not patch prioritization. Although patch importance was not much affected by matrix representation, it was influenced by the connectivity measure and its parameterization. We found the biggest differences in the case of the integral index of connectivity and equally weighted patches, but no consistent pattern in response to changing dispersal distance. Connectivity assessments in more fragmented landscapes were more sensitive to the selection of matrix representation. Although we recommend using continuous matrix representation whenever possible, our results indicated that simpler matrix representations can be also used as a proxy to delineate those patches that are important for overall connectivity, but not to identify connections among habitat patches.
File: Ziolkowska_etal_LandscapeEcol_2014.pdf
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The wildland-urban interface (WUI) is the area in which human settlements adjoin or intermix with ecosystems. Although research on the WUI has been focused on wildfire risk to settlements, we argue here that there is a need to quantify the extent of areas in which human settlements interact with adjoining ecosystems, regardless of their ability to support fire spread. Besides wildfires, human settlements affect neighboring ecosystems through biotic processes, including exotic species introduction, wildlife subsidization, disease transfer, landcover conversion, fragmentation, and habitat loss. The effects of WUI settlements on ecosystems are two tiered, starting with habitat modification and fragmentation and progressing to various diffusion processes in which direct and indirect effects of anthropogenic activities spread into neighboring ecosystems at varying scales. New scientific, management, and policy tools are needed in order to better understand the WUI as a unique social-ecological zone and to mitigate negative consequences of its continued growth.
File: BarMassada_etal_2014_BioScience.pdf
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Infrastructure is increasingly part of wildlife habitats. However, it is not clear how infrastructure affects habitat quality for wildlife adapted to natural disturbances. While potentially providing suitable habitat such as early-successional forest, infrastructure also enables human access, which may modify animal' movements, especially where hunted species are concerned. To investigated the effect of infrastructure for moose (Alces alces, n = 138), a heavily harvested species, we modelled circadian distances and movement rates over the year as a function of moose' distance to the nearest road, house and power line in different human-modified landscapes in Sweden (latitude 57-67). Distances between moose and roads followed a circadian pattern. Animals were more likely to be closer to roads between 18:00 in the evening and 6:00 in the morning (i.e., during times when traffic volumes are generally lower). Moose moved relatively faster when 125 m or closer to a road, or alternatively, were closer to roads when more active. We did not find these relationships between moose and houses or power lines. With respect to roads, our results suggest that moose may make a temporal adjustment. During hours when humans are less active, road-near habitats may be sought out. We suggest considering different resolutions to study the impact of different infrastructure types. We recommend future research to investigate animal movement and behaviour in relation to infrastructure to understand the utilization of human-modified habitats over time, and thus providing key information for wildlife management and conservation, particularly for species that are adapted to disturbed landscapes.
File: Neumann_etal_2013_LUP.pdf
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Biodiversity conservation requires prioritization to be effective. Biodiversity hotspots and conservation planning identify where to focus conservation efforts, but it is unclear when conservation is most successful. Our goals were to: (a) investigate if hot moments for conservation occur, (b) calculate how important and prevalent they are, and (c) discuss what may catalyze hot moments for conservation. We analyzed the worldwide network of protected areas since inception, analyzing both all countries, and those 35 countries that contained at least 1% of either the total count or the total area protected globally. The evidence for hot moments for conservation was very strong. Among all countries, 44% protected more than half of their protected area in 1 year, and 61% did so in one 5-year period. The 35 countries that contain most of the protected area globally (77%) protected 23% and 49%, respectively, within 1 or 5 years. Hot moments often coincided with societal upheaval such as the collapse of the USSR or the end of colonialism. Conservationists need to account for hot moments for conservation to be most effective
File: Radeloff_etal_2013_ConsLetters.pdf
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Most of our knowledge of reproduction of wild parrots in the Neotropics comes from studies of tropical lowland species, with few studies addressing species of high-altitude forests. We studied the reproductive biology of Tucuman Parrots (Amazona tucumana) in north-western Argentina between 2004 and 2009. We obtained data on reproductive output for 86 nests and on causes of mortality for 94 nests. Mean clutch-size per nesting attempt was 3.6 eggs 1.0 (s.d.). Hatching success (proportion of eggs laid that hatch) was 0.77 0.17. Fledging success (proportion of nestlings that fledge) was 0.83 0.13. The overall breeding success (mean number of fledglings per laying female per year) was 2.3 0.8. Overall finite nesting success (daily survival rate to the power of the nesting length) was 0.53 0.27, and chick finite nesting success rate was 0.74 0.22. We did not find differences in reproductive rate between Tucuman Parrots and other species of Amazona parrot from lowland habitats. Productivity and nesting success of Tucuman Parrots had high values in some years and low values in others. This was probably related to fruiting events of Podocarpus parlatorei - a critical food item. The main causes of nesting failure were predation (16%) and abandonment (12%). Our results suggest that for several species of Amazona in lowland habitats, predation and poaching may be the main limiting factors whereas climatic factors and food availability may contribute most to nesting failure at higher altitudes.
File: Rivera_etal_2013_Emu.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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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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