Maintaining habitat and its connectivity is amajor conservation goal, especially for large carnivores. Assessments
of habitat connectivity are typically based on the output of habitat suitability models to first map potential habitat,
and then identify where corridors exist. This requires separating habitat from non- habitat, thus one must
choose specific thresholds for both habitat suitability and the minimum patch size that can be occupied. The selection
of these thresholds is often arbitrary, and the effects of threshold choice on assessments of connectivity
are largely unknown. We sought to quantify howhabitat-suitability and patch-size thresholds influence connectivity
assessments for jaguars (Panthera onca) in the Sierra Gorda Biosphere Reserve in central Mexico. We
modeled potential habitat for jaguars using the species distribution modeling algorithm Maxent, and assessed
potential habitat connectivity with the landscape connectivity software Conefor Sensinode. We repeated these
analyses for 45 combinations of habitat suitability based thresholds and minimum patch sizes. Our results indicated
that the thresholds influenced connectivity assessments greatly, and different combinations of the two
thresholds yielded vastly different map configurations of suitable habitat for jaguars.We developed an approach
to identify the pair of thresholds that bestmatched the jaguar occurrence points based on the connectivity scores.
Among the combinations that we tested, a threshold of 0.3 for habitat suitability and 2 km2 for minimum patch
size produced the best fit (area under the curve=0.9). Surprisingly, we found lowsuitable habitat for jaguars in
most of the core areas of the reserve according to our best potential habitatmodel, but highly suitable areas in the
buffer zones and just outside of the reserve. We conclude that the best and most connected potential areas for
jaguar habitat are in the central eastern part of the Sierra Gorda. More broadly, landscape connectivity analyses
appears to be highly sensitive to the thresholds used to identify suitable habitat, and we recommend conducting
sensitivity analyses as introduced here to identify the optimal combination of thresholds.
File: RamirezetaljaguarBioCons2016.pdf
This is a publication uploaded with a php script
In many developing countries, high rates of
deforestation and biodiversity loss make conservation
efforts urgent. Improving existing land-use plans can
be an option for enhancing biodiversity conservation.
We showcase an approach to enhancing an existing
forest land-use plan using widely available data and
spatial tools, focusing on Argentina’s Southern Yungas
ecoregion. We mapped the distribution of wilderness
areas and species and habitats of conservation concern,
assessed their representation in the land-use plan and
quantified potential changes in habitat availability
and forest connectivity. Wilderness comprised 48%
of the study area, and the highest concentrations
of elements of conservation concern were in the
north. In the current land-use plan, wilderness areas
often occur in regions where logging and grazing are
allowed, and a large proportion of the forest with
the highest conservation value (43%) is under some
level of human influence. Furthermore, we found
that deforestation being legally allowed in the landuse
plan could reduce forest connectivity and habitat
availability substantially. We recommend updating
the current land-use plan by considering human
influence and elements of conservation concern. More
broadly, we demonstrate that widely available spatial
datasets and straightforward approaches can improve
the usefulness of existing land-use plans so that they
more fully incorporate conservation goals.
File: Martinuzzi2018_enhancing_biodiversity_conservation_EnvCons.pdf
This is a publication uploaded with a php script
Land-use is transforming habitats across the globe, thereby threatening wildlife. Large mammals are especiall affected because they require large tracts of intact habitat and functioning corridors between core habita areas. Accurate land-cover data is critical to identify core habitat areas and corridors, and medium resolution sensor such as Landsat 8 provide opportunities to map land cover for conservation planning. Here, we used all availabl Landsat 8 imagery from launch through December 2014 to identify large mammal corridors and assess thei quality across the Caucasus Mountains (N700,000 km ). Specifically, we tested the usefulness of seasonal imag composites (spring, summer, fall, and winter) and a range of image metrics (e.g., mean and median reflectanc across all clear observations) to map nine land-cover classes with a Random Forest classifier. Using image composite from all four seasons yielded markedly higher overall accuracy than using single-season composites (8 increase) and the inclusion of image metrics further improved the classification significantly. Our final land-cove map had an overall accuracy of 85%. Using our land-cover map, we parameterized connectivity models for thre generic large mammal groups and identified wildlife corridors and bottlenecks within corridors with cost-distanc modeling and circuit theory. Corridors were numerous (in total, 85, 131, and 132 corridors for our thre mammal groups, respectively), but often had bottlenecks or high average cost along the least-cost path, indicatin limited functioning. Our findings highlight the potential of Landsat 8 composites to support connectivity analyse across large areas, and thus to contribute to conservation planning, and serve as an early warning system fo biodiversity loss in areas where on-the-ground monitoring is challenging, such as in the Caucasus.
File: Bleyhl_etal_2017_RSE.pdf
This is a publication uploaded with a php script
How organisms respond to climate change during the winter depends on snow cover, because the subniviu (the insulated and thermally stable area between snowpack and frozen ground) provides a refuge for plants, animals and microbes. Satellite data characterizing either freeze/thaw cycles or snow cover are both available, bu these two types of data have not yet been combined to map the subnivium. Here, we characterized global pattern of frozen ground with and without snow cover to provide a baseline to assess the effects of future winte climate change on organisms that depend on the subnivium. We analyzed two remote sensing datasets: th MODIS Snow Cover product and the NASA MEaSUREs Global Record of Daily Landscape Freeze/Thaw Statu dataset derived from SSM/I and SSMIS. From these we developed a new 500-m resolution dataset that capture global patterns of the duration of snow-covered ground (Dws) and the duration of snow-free frozen groun (Dwos) from 2000 to 2012. We also quantified how Dws and Dwos vary with latitude. Our results show that bot mean and interannual variation in Dws and Dwos change with latitude and topography. Mean Dws increase with latitude. Counter-intuitively though, Dwos has longest duration at about 33°N, decreasing both northwar and southward, even though the duration of frozen ground (either snow covered or not) was shorter than tha at higher latitudes. This occurs because snow cover in mid-latitudes is low and ephemeral, leaving longer period of frozen, snow-free ground. Interannual variation in Dws increased with latitude, but the slopes of this relationshi differed among North America, Europe, Asia, and the Southern Hemisphere. Overall, our results show that for organisms that rely on the subnivium to survive the winter, mid-latitude areas could be functionally colde than either higher or lower latitudes. Furthermore, because interannual variation in Dwos is greater at high latitudes we would expect organisms there to be adapted to unpredictability in exposure to freezing. Ultimately the effects of climate change on organisms during winter should be considered in the context of the subnivium when warming could make more northerly areas functionally colder in winter, and changes in annual variation i the duration of snow-free but frozen conditions could lead to greater unpredictability in the onset and end o winter.
File: Zhu_etal_2017_RSE.pdf
This is a publication uploaded with a php script
Context Connectivity assessments typically rely o resistance surfaces derived from habitat models, assumin that higher-quality habitat facilitates movement This assumption remains largely untested though, and i is unlikely that the same environmental factors determin both animal movements and habitat selection potentially biasing connectivity assessments Objectives We evaluated how much connectivit assessments differ when based on resistance surface from habitat versus movement models. In addition, w tested how sensitive connectivity assessments are wit respect to the parameterization of the movemen models. Methods We parameterized maximum entropy model to predict habitat suitability, and step selectio functions to derive movement models for brown bea (Ursus arctos) in the northeastern Carpathians. W compared spatial patterns and distributions of resistanc values derived from those models, and location and characteristics of potential movement corridors Results Brown bears preferred areas with high fores cover, close to forest edges, high topographic complexity and with low human pressure in both habita and movement models. However, resistance surface derived from the habitat models based on predictor measured at broad and medium scales tended t underestimate connectivity, as they predicted substantiall higher resistance values for most of the stud area, including corridors. Conclusions Our findings highlighted that connectivit assessments should be based on movemen information if available, rather than generic habita models. However, the parameterization of movemen models is important, because the type of movemen events considered, and the sampling method o environmental covariates can greatly affect connectivit assessments, and hence the predicted corridors.
File: Ziolkowska_etal_2016_LandscapeEcology.pdf
This is a publication uploaded with a php script
Avian populations can respond dramatically to extreme weather such as droughts and heat waves, yet patterns o response to weather at broad scales remain largely unknown. Our goal was to evaluate annual variation in abundance o 14 grassland bird species breeding in the northern mixed-grass prairie in relation to annual variation in precipitation an temperature. We modeled avian abundance during the breeding season using North American Breeding Bird Surve (BBS) data for the U.S. Badlands and Prairies Bird Conservation Region (BCR 17) from 1980 to 2012. We used hierarchica Bayesian methods to fit models and estimate the candidate weather parameters standardized precipitation index (SPI and standardized temperature index (STI) for the same year and the previous year. Upland Sandpiper (Bartrami longicauda) responded positively to within-year STI (b ¼ 0.101), and Baird’s Sparrow (Ammodramus bairdii) responde negatively to within-year STI (b ¼ 0.161) and positively to within-year SPI (b ¼ 0.195). The parameter estimates wer superficially similar (STI b ¼ 0.075, SPI b ¼ 0.11) for Grasshopper Sparrow (Ammodramus savannarum), but the bestselecte model included an interaction between SPI and STI. The best model for both Eastern Kingbird (Tyrannu tyrannus) and Vesper Sparrow (Pooecetes gramineus) included the additive effects of within-year SPI (b ¼0.032 and b ¼0.054, respectively) and the previous-year’s SPI (b¼0.057 and0.02, respectively), although for Vesper Sparrow the la effect was insignificant. With projected warmer, drier weather during summer in the Badlands and Prairies BCR, Baird’ and Grasshopper sparrows may be especially threatened by future climate change
File: Gorzo_etal_2016_Condor.pdf
This is a publication uploaded with a php script
Aim Land use change is one major threat to freshwater biodiversity, and landuse change scenarios can help to assess threats from future land use change,thereby guiding proactive conservation decisions. Our goal was to identifywhich range-restricted freshwater fish species are most likely to be affected byland use change and to determine where threats to these species from futureland use change in the conterminous United States are most pronounced.Location United States of America.Methods We focused on range-restricted freshwater fish species, identifiedwhich of these species are considered threatened based on either the Interna-tional Union for the Conservation of Nature (IUCN)’s Red List or the Endan-gered Species Act (ESA), and compared their distributions to patterns of futureland use changes by 2051 under three scenarios.Results We found that 14% of the range-restricted species had >30% of theirdistribution area occupied by intensive land use in 2001, and this numberincreased from 27 to 58% by 2051 depending on the land use scenario. Amongthe 57 species most likely to be strongly affected by intensive land use, only26% of these species are currently listed as threatened on the IUCN Red List,and 12% are listed as threatened under the ESA.Main conclusions Our approach demonstrates the value of considering futureland use change scenarios in extinction risk assessment frameworks and offersguidelines for how this could be achieved for future assessments.
File: Januchowski-Hartley_etal_2016_Diversity and Distributions.pdf
This is a publication uploaded with a php script
Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long- term climate averages. However, long- term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short- term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short- term climate variability or on long- term climate averages. We parameterized species distribution models (SDMs) based on either short- term variability or long- term average climate covariates for 320 bird species in the conterminous USA and tested whether any life- history trait- based guilds were particularly sensitive to short- term conditions. Models including short- term climate variability performed well based on their cross- validated area- under- the- curve AUC score (0.85), as did models based on long- term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short- term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species’ potential distributions may have already shifted due recent climate change. However, long- term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short- term climate variability are not available, long- term climate information is a sufficient predictor of species distributions in many cases. However, short- term climate variability data may provide information not captured with long- term climate data for use in SDMs.
File: Bateman_et_al-2016-Ecological_Applications.pdf
This is a publication uploaded with a php script
Climate change may drastically alter patterns of species distributions and richness, but predicting future species pat-terns in occurrence is challenging. Significant shifts in distributions have already been observed, and understanding these recent changes can improve our understanding of potential future changes. We assessed how past climate change affected potential breeding distributions for landbird species in the conterminous United States. We quantified the bioclimatic velocity of potential breeding distributions, that is, the pace and direction of change for each species’ suitable climate space over the past 60 years. We found that potential breeding distributions for landbirds have shifted substantially with an average velocity of 1.27 km yr1, about double the pace of prior distribution shift esti-mates across terrestrial systems globally (0.61 km yr1). The direction of shifts was not uniform. The majority of species’ distributions shifted west, northwest, and north. Multidirectional shifts suggest that changes in climate conditions beyond mean temperature were influencing distributional changes. Indeed, precipitation variables that were proxies for extreme conditions were important variables across all models. There were winners and losers in terms of the area of distributions; many species experienced contractions along west and east distribution edges, and expansions along northern distribution edges. Changes were also reflected in the potential species richness, with some regions potentially gaining species (Midwest, East) and other areas potentially losing species (Southwest). How-ever, the degree to which changes in potential breeding distributions are manifested in actual species richness depends on landcover. Areas that have become increasingly suitable for breeding birds due to changing climate are often those attractive to humans for agriculture and development. This suggests that many areas might have sup-ported more breeding bird species had the landscape not been altered. Our study illustrates that climate change is not only a future threat, but something birds are already experiencing.
File: Bateman_et_al-2016-Global_Change_Biology.pdf
This is a publication uploaded with a php script
Animal movement patterns in space and time are a central aspect of animal ecology. Remotely-sensed environmental indices can play a key role in understanding movement patterns by providing contiguous, relatively fine-scale data that link animal movements to their environment. Still, implementation of newly available remotely-sensed data is often delayed in studies of animal movement, calling for a better flow of information to researchers less familiar with remotely-sensed data applications. Here, we reviewed the application of remotely-sensed environmental indices to infer movement patterns of animals in terrestrial systems in studies published between 2002 and 2013. Next, we introduced newly available remotely-sensed products, and discussed their opportunities for animal movement studies. Studies of coarse-scale movement mostly relied on satellite data representing plant phenology or climate and weather. Studies of small-scale movement frequently used land cover data based on Landsat imagery or aerial photographs. Greater documentation of the type and resolution of remotely-sensed products in ecological movement studies would enhance their usefulness. Recent advancements in remote sensing technology improve assessments of temporal dynamics of landscapes and the three-dimensional structures of habitats, enabling near real-time environmental assessment. Online movement databases that now integrate remotely-sensed data facilitate access to remotely-sensed products for movement ecologists. We recommend that animal movement studies incorporate remotely-sensed products that provide time series of environmental response variables. This would facilitate wildlife management and conservation efforts, as well as the predictive ability of movement analyses. Closer collaboration between ecologists and remote sensing experts could considerably alleviate the implementation gap. Ecologists should not expect that indices derived from remotely-sensed data will be directly analogous to field-collected data and need to critically consider which remotely-sensed product is best suited for a given analysis.
File: Neumann_etal_MovementEcology_2015.pdf
This is a publication uploaded with a php script
