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Wildfires are a major threat to people and property in Wildland Urban Interface (WUI) communities worldwide, but while the patterns of the WUI in North America, Europe and Oceania have been studied before, this is not the case in Latin America. Our goals were to a) map WUI areas in central Argentina, and b) assess wildfire exposure for WUI communities in relation to historic fires, with special emphasis on large fires and estimated burn probability based on an empirical model. We mapped the WUI in the mountains of central Argentina (810,000 ha), after digitizing the location of 276,700 buildings and deriving vegetation maps from satellite imagery. The areas where houses and wildland vegetation intermingle were classified as Intermix WUI (housing density > 6.17 hu/km 2 and wildland vegetation cover > 50%), and the areas where wildland vegetation abuts settlements were classified as Interface WUI (housing density > 6.17 hu/km 2 , wildland vegetation cover < 50%, but within 600 m of a vegetated patch larger than 5 km 2 ). We generated burn probability maps based on historical fire data from 1999 to 2011; as well as from an empirical model of fire frequency. WUI areas occupied 15% of our study area and contained 144,000 buildings (52%). Most WUI area was Intermix WUI, but most WUI buildings were in the Interface WUI. Our findings suggest that central Argentina has a WUI fire problem. WUI areas included most of the buildings exposed to wildfires and most of the buildings located in areas of higher burn probability. Our findings can help focus fire management activities in areas of higher risk, and ultimately provide support for landscape management and planning aimed at reducing wildfire risk in WUI communities. [ABSTRACT FROM AUTHOR]

Economic and social transition periods can have strong negative effects for the environment and for wildlife. The collapse of the Soviet Union in 1991 provides a striking example of social turmoil and transition to a new society. It is unclear, however, how humans affected the environment in the course of the collapse, and if institutions designed to safeguard the environment continued to fulfill their intended role. Our goal was to assess the impact of the collapse of the Soviet Union on forest canopy removal rates in protected areas, and how these rates varied by protected area status and over time. We monitored forest canopy removal within and outside of protected areas using a 1985–2010 time series of Landsat satellite images from the Western Caucasus. On average, we found surprisingly low annual forest canopy removal rates of only 0.03%. The highest canopy removal inside of protected areas of all types occurred after 2000. Among the protected areas, we found the highest canopy removal rates within Sochi National Park, attributable to construction for the Olympic Games and in spite of the Park’s protected status. Overall, it is encouraging that forest canopy removal rates in protected areas in the Western Caucasus are far lower than in other Russian regions. Because many local endemic plant and animal species are found in the Caucasus region, clear cuts are prohibited, and this regulation appears to be effective. However, forest canopy removal within protected areas caused by major social and political events such as the Olympic Games is of concern. [ABSTRACT FROM AUTHOR]

Abstract: 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 100ha, 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. [Copyright &y& Elsevier]

Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such analysis is valid. The objective of our study was to explore the relationship between periodicity in landscape patterns and geostatistical models. We were especially interested in the validity of the assumption that periodicity in geostatistical models indicates periodicity in landscape pattern, and whether the former can characterize frequency and magnitude of the latter. We created maps containing various periodic spatial patterns, derived correlograms from these, and examined periodicity in the correlograms. We also created non-regular maps that we suspected would cause periodicity in correlograms. Our results demonstrate that a) various periodic spatial patterns produce periodicity in correlograms derived from them, b) the distance-lags at which correlograms peak correspond to the average distances between patch centers, c) periodicity is strongest when the diameter of patches is equal to the distance between patch edges, d) periodicity in omni-directional correlograms of complex spatial patterns (such as checkerboards) are combinations of several waves because inter-patch distances differ with direction; multiple directional correlograms can decompose such complexity, and e) periodicity in correlograms can also be caused when the number of patches in a study site is small. These results highlight that correlograms can be used to detect and describe regular spatial patterns. However, it is crucial to ensure that the assumption of stationarity is not violated, i.e., that the study area contains a sufficiently large number of patches to avoid incorrect conclusions. [ABSTRACT FROM AUTHOR]

Increasing residential development around protected areas is a major threat for protected areas worldwide, and human population growth is often the most important cause. However, population is decreasing in many regions as a result of socio-economic changes, and it is unclear how residential development around protected areas is affected in these situations. We investigated whether decreasing human population alleviates pressures from residential development around protected areas, using Puerto Rico—an island with declining population—as a case study. We calculated population and housing changes from the 2000 to 2010 census around 124 protected areas, using buffers of different sizes. We found that the number of houses around protected areas continued to increase while population declined both around protected areas and island-wide. A total of 32,300 new houses were constructed within only 1 km from protected areas, while population declined by 28,868 within the same area. At the same time, 90% of protected areas showed increases in housing in the surrounding lands, 47% showed population declines, and 40% showed population increases, revealing strong spatial variations. Our results highlight that residential development remains an important component of lands surrounding protected areas in Puerto Rico, but the spatial variations in population and housing changes indicate that management actions in response to housing effects may need to be individually targeted. More broadly, our findings reinforce the awareness that residential development effects on protected areas are most likely widespread and common in many socioeconomic and demographic settings. [ABSTRACT FROM AUTHOR]

Wetland loss is a global concern because wetlands are highly diverse ecosystems that provide important goods and services, thus threatening both biodiversity and human well-being. The Paraná River Delta is one of the largest and most important wetland ecosystems of South America, undergoing expanding cattle and forestry activities with widespread water control practices. To understand the patterns and drivers of land cover change in the Lower Paraná River Delta, we quantified land cover changes and modeled associated factors. We developed land cover maps using Landsat images from 1999 and 2013 and identified main land cover changes. We quantified the influence of different socioeconomic (distance to roads, population centers and human activity centers), land management (area within polders, cattle density and years since last fire), biophysical variables (landscape unit, elevation, soil productivity, distance to rivers) and variables related to extreme system dynamics (flooding and fires) on freshwater marsh conversion with Boosted Regression Trees. We found that one third of the freshwater 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 the most important factors responsible for freshwater marsh conversion to pasture. These rapid and widespread 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 uses and related management practices, to develop an urgently needed land use policy for the Lower Delta. [ABSTRACT FROM AUTHOR]

Animals select habitat at multiple spatial scales, suggesting that biodiversity modeling, for example of species richness, should be based on environmental data gathered at multiple spatial scales, and especially multiple grain sizes. Different satellite sensors collect data at different spatial resolutions and therefore provide opportunities for multi-grain habitat measures. The dynamic habitat indices (DHIs), which are derived from satellite data, capture patterns of vegetative productivity and predict bird species richness well. However, the DHIs have only been analyzed at single resolutions (e.g., 1-km), and have not yet been derived from high-resolution satellite data (< 10 -m). Our goal was to predict bird species richness based on measures of vegetation productivity (DHIs, NDVI median and NDVI percentile 90th) across a range of spatial resolutions both from different sensors, and from resampled high-resolution imagery. We analyzed bird species richness within 215 forest, grassland and shrubland plots (56.25 ha) located at 26 terrestrial field sites of the National Ecology Observatory Network (NEON), in the continental US. To obtain our multi-resolution measures of vegetation productivity, we acquired data from Planetscope (3-m), RapidEye (5-m), Sentinel-2 (10-m), Landsat-8 (30-m) and MODIS (250-m) from 2017 to 2020, generated time series of NDVI, calculated the three DHIs (cumulative, minimum and variation), NDVI median and the 90th percentile NDVI and calculated 1st and 2nd order texture measures. We evaluated the performance of the derived measures to predict bird species richness of habitat specialist guilds based on (i) univariate models (ii) multivariate models with single-resolution measures and (iii) multivariate models with multi-resolution measures. Single-spatial resolution measures predicted bird species richness moderately well (R2 up to 0.51) and the best performing spatial resolution and measure differed among bird species guilds. High-spatial resolution (3–5 m) measures outperformed medium-resolution measures (10–250 m). Models for all guilds performed best when incorporating multiple resolutions, including for all species richness (R2 = 0.63) and for forest (R2 = 0.72), grassland (R2 = 0.53) and shrubland specialists (R2 = 0.46). In addition, models based on multi-resolution data from different sensors performed better than models based on resampled high-resolution data for any of the guilds. Our results highlight, first, the value of the DHIs derived from high-resolution satellite data to predict bird species richness and, second, that remotely-sensed vegetation productivity measures from multiple spatial resolutions offer great promise for quantifying biodiversity. • Single-spatial resolution measures predicted bird richness moderately well. • Best-performing spatial resolution and measure differed among bird species guilds. • High-resolution measures performed better than medium-resolution measures. • Multi-grain habitat models performed best for all bird species guilds. • Models based on original data performed better than resampled data models. [ABSTRACT FROM AUTHOR]

In the former “Eastern Bloc” countries, there have been dramatic changes in forest disturbance and forest recovery rates since the collapse of the Soviet Union, due to the transition to open-market economies, and the recent economic crisis. Unfortunately though, Eastern European countries collected their forest statistics inconsistently, and their boundaries have changed, making it difficult to analyze forest dynamics over time. Our goal here was to consistently quantify forest cover change across Eastern Europe since the 1980s based on the Landsat image archive. We developed an algorithm to simultaneously process data from different Landsat platforms and sensors (TM and ETM +) to map annual forest cover loss and decadal forest cover gain. We processed 59,539 Landsat images for 527 footprints across Eastern Europe and European Russia. Our results were highly accurate, with gross forest loss producer's and user's accuracy of > 88% and > 89%, respectively, and gross forest gain producer's and user's accuracy of > 75% and > 91%, based on a sample of probability-based validation points. We found substantial changes in the forest cover of Eastern Europe. Net forest cover increased from 1985 to 2012 by 4.7% across the region, but decreased in Estonia and Latvia. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Timber harvesting was the main cause of forest loss, accompanied by some insect defoliation and forest conversion, while only 7.4% of the total forest cover loss was due to large-scale wildfires and windstorms. Overall, the countries of Eastern Europe experienced constant levels or declines in forest loss after the collapse of socialism in the late 1980s, but a pronounced increase in loss in the early 2000s. By the late 2000s, however, the global economic crisis coincided with reduced timber harvesting in most countries, except Poland, Czech Republic, Slovakia, and the Baltic states. Most forest disturbance did not result in a permanent forest loss during our study period. Indeed, forest generally recovered fast and only 12% of the areas of forest loss prior to 1995 had not yet recovered by 2012. Our results allow national and sub-national level analysis and are available on-line ( http://glad.geog.umd.edu/europe/ ) to serve as a baseline for further analyses of forest dynamics and its drivers. [ABSTRACT FROM AUTHOR]