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. Highspatial 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.
File: 1-s2.0-S0034425723002122-main.pdf
Aim: Predicting biodiversity responses to global changes requires good models of
species' distributions. Both environmental conditions and human activities determine
population density patterns. However, quantifying the relationship between
wildlife population densities and their underlying environmental conditions across
large geographical scales has remained challenging. Our goal was to explain the
abundances of mammal species based on their response to several remotely sensed
indices including the Dynamic Habitat Indices (DHIs) and the novel Winter Habitat
Indices (WHIs).
Location: Russia, the majority of regions.
Taxon: Eight mammal species.
Methods: We estimated average population densities for each species across Russia
from 1981 to 2010 from winter track counts. The DHIs measure vegetative productivity,
a proxy for food availability. Our WHIs included the duration of snow-free
ground,
duration of snow-covered
ground and the start, end and length of frozen season. In
models, we included elevation, climate conditions, human footprint index. We parameterized
multiple linear regression and applied best-subset
model selection to determine
the main factors influencing population density.
Results: The DHIs were included in some of the top-twelve
models of every species,
and in the top model for moose, wild boar, red fox and wolf, so they were important
for species at all trophic levels. The WHIs were included in top models for all species
except roe deer, demonstrating the importance of winter conditions. The duration of
frozen ground without snow and the end of frozen season were particularly important.
Our top models performed well for all the species (R2
adj 0.43–0.87).
Main Conclusions: The combination of the DHIs and the WHIs with climate and
human-related
variables resulted in high explanatory power. We show that vegetation
productivity and winter conditions are key drivers of variation in population density
of eight species across Russia.
File: Razenkova-et-al-Abundance-patterns-of-mammals-across-Russia-explained-by-remotely-sensed.pdf
Global biodiversity loss is most pronounced in the tropics. Monitoring of broad-scale patterns of habitat is essential for biodiversity conservation. Image texture measures derived from satellite data are proxies for habitat heterogeneity, but have not been tested in tropical forests. Our goal was to evaluate image texture to predict tropical forest bird distributions across Thailand for different guilds. We calculated a suite of texture measures from cumulative productivity (1-km fPAR-MODIS data) for Thailand's forests, and assessed how well texture measures predicted distributions of 86 tropical forest bird species in relation to body size, and nesting guild. Finally, we compared the predictive performance of combining (a) satellite image texture measures, (b) habitat composition, and (c) habitat fragmentation. We found that texture measures predicted occurrences of tropical forest birds well (AUC = 0.801 ± 0.063). Second-order homogeneity was the most predictive texture measure. Our models based on texture were significantly better for birds with larger body size (p < 0.05), but did not differ among nesting guilds (p > 0.05). Models that combined texture with habitat composition measures (AUC = 0.928 ± 0.038) outperformed models that combined fragmentation with habitat composition measures (AUC = 0.905 ± 0.047) (p < 0.05). The incorporation of texture, composition, and fragmentation variables significantly improved model accuracy over texture-only models (AUC = 0.801 ± 0.063 to AUC = 0.938 ± 0.034; p < 0.05). We suggest that texture measures are a valuable tool to predict bird distributions at broad scales in tropical forests.
File: Suttidate-et-al_2023_Biological-Conservation.pdf
Wolf predation on domestic animals is a main reason for human-wolf conflict throughout the global range of wolves (Canis lupus). We conducted research in Daursky State Nature Biosphere Reserve and Valley of Dzeren Nature Refuge to evaluate the extent of wolf-livestock conflict. We documented 64 cases of livestock predation by wolves between 2015 and 2019 and analyzed the patterns of conflicts and people’s attitude towards wolves. A total of 283 livestock were killed by wolves, with an annual mean of 55.4 (SD = ± 1.44) animals/yr and a mean frequency of attack of 12.8 ± 3.89 attacks/yr. Sheep were the main prey of wolves, comprising 77.4 % of the total number of livestock killed. Among cattle and horses, wolves preferred to kill juveniles rather than adults (χ2 = 140.2, df = 2, P < 0.001). We found no significant difference between type of livestock killed by wolves by season (χ2 = 5.53, df = 3, P = 0.4776). Predations only occurred in situations where there were no protective actions, like a shepherd or protective corral. The mean annual rate of livestock lost by predation was 0.281 % (SD = ± 0.007). Attitudes towards wolves were mostly neutral to negative. The higher the level of income estimated by respondents (y = -0.242x + 0.98; R2 = 0.99; F = 203.3; P = 0.005), the less negative the attitude towards wolf. To effectively reduce depredation, we suggest improving management actions, especially increased surveillance. We also discuss other management measures to mitigate livestock depredation.
File: kirilyuk_2020_JNC.pdf
There are very little data about daily activity patterns of Canis lupus (hereinafter – wolf) living in open arid habitats with low human density in Dauria. Therefore we have studied the influence of human activity, reproduction and weather conditions on daily patterns and duration of the activity of 17 GPS-collared wolves in the Daursky State Nature Biosphere Reserve, Russia, from 2015 to 2020. GPS-collars were equipped with acceleration sensors. Wolves were active 44% (± 0.02 SE) of the day and traveled 1.21 km/h (± 0.10 SE) on average. The mean duration of subsequent activity periods was 7.36 h (± 1.5 SD). The duration of the subsequent, inactivity period was 10.07 h (± 4.2 SD). Travelling speed significantly increased when wolves made extraterritorial forays from their home range to territories of neighbouring packs. The highest activity index corresponds to long-distance dispersing wolves. Weather conditions and human activity did not significantly effect wolves daily activity patterns. Wolves were generally less active and mobile during the cold season. All wolves showed crepuscular movement peaks. Five of the wolves’ movement patterns switched to diurnal eight cases when they conducted an extraterritorial foray crossing territories of neighbouring packs. We conclude that wolves’ daily activity patterns were mainly shaped by a combination of several factors.
File: Kirilyuk_NCR_2021.pdf
Habitat connectivity is crucial for the conservation of species restricted to fragmented populations within human-dominated landscapes. However, identifying habitat connectivity for apex predators is challenging because trophic interactions between primary productivity and prey species influence both the distribution of habitats, and predator movement. Our goal was to assess habitat connectivity for Indochinese tigers (Panthera tigris) in Thailand. We quantified suitable habitat and dispersal corridors based an ensemble species distribution model that included prey distributions, primary productivity, and abiotic variables and was based on camera-trap data from 1996 to 2013 in 15 protected areas. We employed graph theory to evaluate the relative importance of habitat patches and dispersal corridors to the overall connectivity network. We found that tiger occurrence models with and without prey distributions performed well (Area Under the Curve: 0.932–0.954). However, inclusion of prey distributions significantly improved model performance (P < 0.001). Protected areas with tigers at the time of our surveys were highly isolated with high resistance to movement within the dispersal corridors, and four of them have lost their tiger populations since. Potential habitat patches outside of protected areas were also mostly isolated, but it was encouraging to find that there is ample potential habitat that tigers are not occupying. The Huai Kha Kaeng - ThungYai habitat patch and Kaeng Krachan dispersal corridor were the most important for overall habitat connectivity. Generally, integrating prey distributions into assessments of connectivity is a promising approach that can be widely applied to predict species occurrence and delineate dispersal corridors, thereby supporting conservation planning of tigers and other large carnivores.
File: Suttidate-GEC_2021.pdf
Protected areas are cornerstones of conservation efforts worldwide. However, protected areas do not act in isolation because they are connected with surrounding, unprotected lands. Few studies have evaluated the effects of protected areas on wildlife populations inhabiting private lands in the surrounding landscapes. The lowland tapir Tapirus terrestris is the largest terrestrial mammal of the Neotropics and is categorized as Vulnerable on the IUCN Red List. It is necessary to understand the influence of landscape characteristics on the tapir’s habitat use to enable effective conservation management for this species. Our objectives were to () determine the potential distribution of the lowland tapir’s habitat in the Southern Yungas of Argentina, and () evaluate the role of protected areas and other covariates on tapir habitat use in adjacent private lands. We used records of lowland tapirs to model the species’ potential distribution and determined habitat use with occupancy modelling. Based on the covariates found to be significant in our models, we constructed predictive maps of probability of habitat use and assessed the area of potential habitat remaining for the species. Probability of habitat use was higher in the vicinity of two national parks and small households than further away from them. We found that in % of the lowland tapir’s potential distribution the probability of habitat use is high (..). These areas are near the three national parks in the study area. The probability of detecting lowland tapirs increased with distance to roads. We conclude that national parks play a key role in the persistence of lowland tapir populations on adjacent private lands.
File: Riveraetal2020.pdf
Aim: The aim was to derive global indices of winter conditions and examine their relationships with species richness patterns outside of the tropics. Location: All extratropical areas (>25° N and 25° S latitudes), excluding islands. Time period: 2000– 2018.Major taxa studied: Amphibians, birds and mammals. Methods: We mapped three global indices of winter conditions [number of days of frozen ground (length of frozen ground winter); snow cover variability; and lack of subnivium (below-snow refuge)] from satellite data, then used generalized additive models to examine their relationships with species richness patterns derived from range data. Results: Length of frozen ground winter was the strongest predictor of species rich-ness, with a consistent cross-taxonomic decline in species richness occurring beyond 3 months of winter. It also often outperformed other environmental predictors of species richness patterns commonly used in biodiversity studies, including climate variables, primary productivity and elevation. In areas with ≥3 months of winter conditions, all three winter indices explained much of the deviance in amphibian, mammal and resident bird species richness. Mammals exhibited a stronger relationship with snow cover variability and lack of subnivium than the other taxa. Species richness of fully migratory species of birds peaked at c. 5.5 months of winter, coinciding with low species richness of residents. Main conclusions: Our study demonstrates that winter structures latitudinal and elevational gradients of extratropical terrestrial species richness. In a rapidly warming world, tracking the seasonal dynamics of frozen ground and snow cover will be essential for predicting the consequences of climate change on species, communities and ecosystems. The indices of winter conditions we developed from satellite imagery provide an effective means of monitoring these dynamics into the future.
File: GudexCross-Global-Ecology-and-Biogeography-2022-Winter-conditions-structure-extratropical-patterns-of-species.pdf
Hundreds of millions of hectares of cropland have been abandoned globally since 1950 due to demographic, economic, and environmental changes. This abandonment has been seen as an important opportunity for carbon sequestration and habitat restoration; yet those benefits depend on the persistence of abandonment, which is poorly known. Here, we track abandonment and recultivation at 11 sites across four continents using annual land-cover maps for 1987–2017. We find that abandonment is largely fleeting, lasting on average only 14.22 years (SD = 1.44). At most sites, we project that >50% of abandoned croplands will be recultivated within 30 years, precluding the accumulation of substantial amounts of carbon and biodiversity. Recultivation resulted in 30.84% less abandonment and 35.39% less carbon accumulated by 2017 than expected without recultivation. Unless policy-makers take steps to reduce recultivation or provide incentives for regeneration, abandonment will remain a missed opportunity to reduce biodiversity loss and climate change.
File: Crawford_SciAdv_2022.pdf
Detailed maps of forest structure attributes are crucial for sustainable forest management, conservation, and forest ecosystem science at the landscape level. Mapping the structure of broad heterogeneous forests is chal lenging, but the integration of extensive field inventory plots with wall-to-wall metrics derived from synthetic aperture radar (SAR) and optical remote sensing offers a potential solution. Our goal was to map forest structure attributes (diameter at breast height, basal area, mean height, dominant height, wood volume and canopy cover) at 30-m resolution across the diverse 463,000 km2 of native forests of Argentina based on SAR Sentinel-1, vegetation metrics from Sentinel-2 and geographic coordinates. We modelled the forest structure attributes based on the latest national forest inventory, generated uncertainty maps, quantified the contribution of the predictors, and compared our height predictions with those from GEDI (Global Ecosystem Dynamics tion) and GFCH (Global Forest Canopy Height). We analyzed 3788 forest inventory plots (1000 m2 each) from Argentina’s Second Native Forest Inventory (2015–2020) to develop predictive random forest regression models. From Sentinel-1, we included both VV (vertical transmitted and received) and VH (vertical transmitted and horizontal received) polarizations and calculated 1st and 2nd order textures within 3 ×3 pixels to match the size of the inventory plots. For Sentinel-2, we derived EVI (enhanced vegetation index), calculated DHIs (dynamic habitat indices (annual cumulative, minimum and variation) and the EVI median, then generated 1st and 2nd order textures within 3 ×3 pixels of these variables. Our models including metrics from Sentinel-1 and 2, plus latitude and longitude predicted forest structure attributes well with root mean square errors (RMSE) ranging from 23.8% to 70.3%. Mean and dominant height models had notably good performance presenting relatively low RMSE (24.5% and 23.8%, respectively). Metrics from VH polarization and longitude were overall the most important predictors, but optimal predictors differed among the different forest structure attributes. Height predictions (r =0.89 and 0.85) outperformed those from GEDI (r =0.81) and the GFCH (r =0.66), suggesting that SAR Sentinel-1, DHIs from Sentinel-2 plus geographic coordinates provide great opportunities to map multiple forest structure attributes for large areas. Based on our models, we generated spatially-explicit maps of multiple forest structure attributes as well as uncertainty maps at 30-m spatial resolution for all Argentina’s native forest areas in support of forest management and conservation planning across the country.
File: Silveira_RSE_2022.pdf
