Globally, deforestation continues, and although protected areas effectively protect forests, the
majority of forests are not in protected areas. Thus, how effective are different management regimes to
avoid deforestation in non-protected forests? We sought to assess the effectiveness of different national forestmanagement
regimes to safeguard forests outside protected areas. We compared 2000–2014 deforestation
rates across the temperate forests of 5 countries in the Himalaya (Bhutan, Nepal, China, India, and Myanmar)
of which 13% are protected. We reviewed the literature to characterize forest management regimes in each
country and conducted a quasi-experimental analysis to measure differences in deforestation of unprotected
forests among countries and states in India. Countries varied in both overarching forest-management goals
and specific tenure arrangements and policies for unprotected forests, from policies emphasizing economic
development to those focused on forest conservation. Deforestation rates differed up to 1.4% between countries,
even after accounting for local determinants of deforestation, such as human population density, market access,
and topography. The highest deforestation rates were associated with forest policies aimed at maximizing
profits and unstable tenure regimes. Deforestation in national forest-management regimes that emphasized
conservation and community management were relatively low. In India results were consistent with the
national-level results. We interpreted our results in the context of the broader literature on decentralized,
community-based natural resource management, and our findings emphasize that the type and quality of
community-based forestry programs and the degree to which they are oriented toward sustainable use rather
than economic development are important for forest protection. Our cross-national results are consistent with
results from site- and regional-scale studies that show forest-management regimes that ensure stable land
tenure and integrate local-livelihood benefits with forest conservation result in the best forest outcomes.
File: Brandt2017_forestHimalaya_ConsBio.pdf
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The process of vegetation burning is an essential component in the dynamics of grassy arid ecosystems. An understanding of the impact of fires on various components of the arid ecosystem is required for scientific, environmental, and management tasks, and it should be assessed with a high spatial and temporal resolution. This paper presents a method and description of data to be used in such an assessment of fire dynamics. The spatiotemporal dynamics of fires in the Chernye Zemli area is described. It shows the abundance of fires, their high interannual variability, clusterization in a territory, and the dominance of large fires.
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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
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Fine-scale information about urban vegetation and social-ecological relationships
is crucial to inform both urban planning and ecological research, and high spatial resolution
imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing
have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation
cover with sub-meter (<1 m) resolution aerial imagery, and identify social-ecological relationships
of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto
Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution
(0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3)
investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental
factors. We found that 61% of the San Juan Metropolitan Area was green and that
our combination of high spatial resolution imagery and object-based classification was highly
successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition,
simple spatial pattern analysis allowed us to separate residential from non-residential vegetation
with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly
across the city. Both socioeconomic (e.g., population density, building age, detached homes)
and environmental variables (e.g., topography) were important in explaining variations in vegetation
cover in our spatial regression models. However, important socioeconomic drivers found
in cities in temperate zones, such as income and home value, were not important in San Juan.
Climatic and cultural differences between tropical and temperate cities may result in different
social-ecological relationships. Our study provides novel information for local land use
planners, highlights the value of high spatial resolution remote sensing data to advance ecological
research and urban planning in tropical cities, and emphasizes the need for more studies in
tropical cities.
File: Martinuzzi2018_VegCover_EcoApps.pdf
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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
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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
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Green-leaf phenology describes the development of vegetation throughout a growing season and greatl affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characteriz phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficul because of low numbers of cloud-free images in a single year. One way to overcome data availability limitation is to merge multi-year imagery into one time series, but this requires accounting for phenologica differences among years. Here we present a new approach that employed a time series of a MODIS vegetatio index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenologica differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 201 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-u (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm) We tested our approach in eight locations across the United States that represented forests of differen types and without signs of recent forest disturbance. We compared Landsat-based phenological transitio dates to those derived from MODIS and ground-based camera data from the PhenoCam-network The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season an maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) an dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlatio coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the ne Landsat time series, the confidence intervals of the estimated keydates were substantially lower than i case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imager and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatia resolution than previously possible, highlighting the potential for fine scale phenology maps using th rich Landsat data archive over large areas.
File: Baumann_etal_2017_IJAEOG.pdf
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Wetland loss is a global concern because wetlands are highly diverse ecosystems that provide important good and services, thus threatening both biodiversity and human well-being. The Paraná River Delta is one of the larges and most important wetland ecosystems of South America, undergoing expanding cattle and forestry activitie with widespread water control practices. To understand the patterns and drivers of land cover change in th Lower Paraná River Delta, we quantified land cover changes and modeled associated factors. We developed lan cover maps using Landsat images from 1999 and 2013 and identified main land cover changes. We quantified th influence of different socioeconomic (distance to roads, population centers and human activity centers), lan management (area within polders, cattle density and years since last fire), biophysical variables (landscap unit, elevation, soil productivity, distance to rivers) and variables related to extreme system dynamics (floodin and fires) on freshwater marsh conversion with Boosted Regression Trees. We found that one third of the freshwate marshes of the Lower Delta (163,000 ha) were replaced by pastures (70%) and forestry (18%) in only 14 years. Ranching practices (represented by cattle density, area within polders and distance to roads) were th most important factors responsible for freshwater marsh conversion to pasture. These rapid and widesprea losses of freshwater marshes have potentially large negative consequences for biodiversity and ecosystem services A strategy for sustainable wetland management will benefit from careful analysis of dominant land use and related management practices, to develop an urgently needed land use policy for the Lower Delta
File: Sica_etal_2016_Science of the Total Environment.pdf
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File: Alix-Garcia_etal_2016_LandUsePolicy.pdf
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Weather and climate affect many ecological processes, making spatiall continuous yet fine-resolution weather data desirable for ecological research and predictions Numerous downscaled weather data sets exist, but little attempt has been made t evaluate them systematically. Here we address this shortcoming by focusing on four majo questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperatur and precipitation estimates? (2) Are there significant regional differences in accuracy amon data sets? (3) How accurate are their mean values compared with extremes? (4) Does thei accuracy depend on spatial resolution? We compared eight widely used downscaled dat sets that provide gridded daily weather data for recent decades across the United States We found considerable differences among data sets and between downscaled and weathe station data. Temperature is represented more accurately than precipitation, and climat averages are more accurate than weather extremes. The data set exhibiting the best agreemen with station data varies among ecoregions. Surprisingly, the accuracy of the dat sets does not depend on spatial resolution. Although some inherent differences amon data sets and weather station data are to be expected, our findings highlight how muc different interpolation methods affect downscaled weather data, even for local comparison with nearby weather stations located inside a grid cell. More broadly, our results highligh the need for careful consideration among different available data sets in terms of whic variables they describe best, where they perform best, and their resolution, when selectin a downscaled weather data set for a given ecological application.
File: Behnke_etal_2016_EcologicalApplications.pdf
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