Context Approaches estimating landscape effects
on biodiversity frequently focus on a single extent,
finding one ‘optimal’ extent, or use narrow extents.
However, species perceive the environment in different
ways, select habitat hierarchically, and respond to
multiple selection pressures at extents that best predict
each pressure.
Objective We aimed to assess multi-scale relationships
between primary productivity and species
occurrences and abundances.
Methods We used a multi-scale approach, called
‘scalograms’, to assess landscape level effects of primary
productivity, in the form of Dynamic Habitat
Indices (DHIs) on the occurrences and abundances of
100 Argentinian forest bird species. We used average
DHI values within multiple extents (3 × 3 to 101 ×
101 pixels; 30 m resolution), and 11 ‘scalogram’ metrics
as environmental inputs in occurrence and abundance
models.
Results Average cumulative DHI values in extents
81 × 81 to 101 × 101 pixels (5.9 – 9.2 km2)
and maximum
cumulative DHI across extents were in the top
three predictors of species occurrences (included in
models for 41% and 18% of species, respectively).
Average cumulative DHI values in various extents
contributed ~ 1.6 times more predictive power to
occurrence models than expected. For species abundances,
average DHI values and scalogram measures
were in the top three predictors for < 2% of species
and contributed less model predictive power than
expected, regardless of DHI type (cumulative, minimum,
variation).
Conclusions Argentinian forest bird occurrences,
but not abundances, respond to high levels of primary
productivity at multiple, broad extents rather than a
single ‘optimal’ extent. Factors other than primary
productivity appear to be more important for predicting
abundance.
File: Olah-et-al_2025_Argentina_DHIs_scalograms_bird-occurence_abundance.pdf
Biodiversity science requires effective tools to predict patterns of species diversity at multiple temporal and
spatial scales. The Dynamic Habitat Indices (DHIs) are remotely sensed indices that summarize aboveground
vegetation productivity in a way that is ecologically relevant for biodiversity assessments. Existing global DHIs,
derived from MODIS at 1-km resolution, predict species richness at broad scales well, but that resolution is coarse
relative to the grain at which many species perceive their habitat. With the much finer spatial resolution of
Sentinel-2 and Landsat data, plus Landsat’s longer data record, it is possible to track potential changes of
vegetation and its impacts on biodiversity at a finer grain over longer periods. Here, our main goals were to
derive the DHIs from 10-m Sentinel-2, 30-m Landsat, and 250-m MODIS data for the conterminous US and
compare all DHIs at two spatial extents, and to evaluate the ability of these DHIs to predict bird species richness
in 25 National Ecological Observatory Network terrestrial sites. In addition, we derived the Landsat DHIs for
1991–2000 and investigated how they changed by 2011–2020. We found that the Sentinel-2, Landsat, and
MODIS DHIs were highly correlated when summarized by ecoregion (Spearman correlation ranging from 0.89 to
0.99), indicating good agreement between them and that we were able to overcome the lower temporal resolution
of Sentinel-2 and Landsat. Sentinel-2 and Landsat DHIs outperformed MODIS in modeling species richness
for all bird guilds, explaining up to 49% of variance of grassland affiliates in linear regression models.
Furthermore medium-resolution DHIs (10–30 m resolution) captured spatial heterogeneity much better than
MODIS DHIs. We observed considerable changes in Landsat DHIs from 1991–2000 to 2011–2020, such as
increased cumulative DHI along the West Coast, in mountain ranges, and in the South, but lower cumulative DHI
in the Midwest. Our newly derived DHIs for the conterminous US have great potential for use in biodiversity
science and conservation.
File: Razenkova-etal-2025-DHIs-from-Landsat-and-Sentinal2.pdf
Aim: On-the-ground conservation efforts require managers to balance various and sometimes conflicting conservation goals. For instance, areas important for conserving threatened and endangered species may have little spatial agreement with high functional redundancy. Using prioritization tools can further complicate conservation prioritizations if conflicting diversity metrics identify different high-priority areas. We compared five community-level diversity metrics for birds across the conterminous US to identify how much agreement existed between each before and after using a prioritization framework. Location: Contiguous US. Methods: We examined spatial agreement among metrics before (a priori) and after (a posteriori) prioritization using integer linear programming. We compared a posteriori outputs for 10% and 30% conservation goals. We also assessed data layer correlation and agreement (i.e., overlap) a priori and a posteriori. Results: As expected, the a priori diversity metrics were poorly to moderately correlated (median = 0.31, range = 0.11–0.71), but all a posteriori solutions had areas of agreement. Accordingly, our a posteriori metrics identified different areas as high priority for conservation, none aligning well with the current protected areas (mean = 13%–15% agreement). However, the a posteriori approach allowed us to include a continuity constraint (identify adjacent important pixels) and easily find areas of high-priority agreement. Main Conclusions: Metric agreement depended on a priori or a posteriori evaluation, highlighting managers' challenges when deciding where and how to enact conservation. Given these challenges, a posteriori solutions best support multiple-objective, complex and large planning conservation problems. Importantly, all of our a posteriori maps agreed in areas, suggesting aggregates of several metrics could instill certainty in decision-making if prioritization solutions were obtained at different times. Overall, our results underscore the critical importance of generating maps and metrics useful for on-the-ground management, carefully selecting biodiversity metrics that best reflect conservation goals and employing prioritization software for generating conservation solutions.
File: Diversity-and-Distributions-2025-Carroll-Biodiversity-Metric-Selection-and-Their-Applications-for-Spatial.pdf
Habitat loss and fragmentation in tropical regions are major threats to the persistence of endangered Malay tapir (Tapirus indicus). The Malay tapir distribution is largely constrained to fragmented habitats inside protected areas. However, it is unclear how the spatial patterns of habitat fragmentation affect its relative abundance. Here, we investigated the effects of habitat fragmentation on Malay tapir relative abundance in Thailand. We first quantified the spatial patterns of habitat fragmentation within nine of Thailand’s protected areas. Second, we assessed the relationship of fragmentation metrics and relative abundance of Malay tapirs. Third, we identified the relative importance of the fragmentation metrics in explaining relative abundance. We found that tapir abundance remained unexpectedly high in the Southern forest complex despite the fact that tapir habitats were significantly more fragmented there than in the protected area in the western forest complex (p < 0.05). Additionally, we found a significantly negative relation with clumpiness index (R2 = 0.51, p < 0.05). This suggests that other factors may also be influencing their populations, so that the Southern protected areas provide preferred habitat with higher relative proportions of moist evergreen forest, large habitat patch size, precipitation, and elevation. It highlights the importance of interconnected habitat for tapirs, and the benefit of conservation efforts in small, less recognized protected areas.
File: Suwannaphong-s2.0-S2351989424003901-main.pdf
Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients.
File: Schug-1-s2.0-S0034425724002785-main.pdf
Assemblages in seasonal ecosystems undergo striking changes in species composition and diversity across the annual cycle. Despite a long-standing recognition that seasonality structures biogeographic gradients in taxonomic diversity (e.g., species richness), our understanding of how seasonality structures other aspects of biodiversity (e.g., functional diversity) has lagged. Integrating seasonal species distributions with comprehensive data on key morphological traits for bird assemblages across North America, we find that seasonal turnover in functional diversity increases with the magnitude and predictability of seasonality. Furthermore, seasonal increases in bird species richness led to a denser packing of functional trait space, but functional expansion was important, especially in regions with higher seasonality. Our results suggest that the magnitude and predictability of seasonality and total productivity can explain the geography of changes in functional diversity with broader implications for understanding species redistribution, community assembly and ecosystem functioning.
File: Ecography-2022-Keyser-Snow-cover-dynamics-an-overlooked-yet-important-feature-of-winter-bird-occurrence-and.pdf
The Atlantic meridional overturning circulation (AMOC) has caused significant climate changes over the past 90 000 years. Prior work has hypothesized that these millennial-scale climate variations effected past and contemporary biodiversity, but the effects are understudied. Moreover, few biogeographic models have accounted for uncertainties in palaeoclimatic simulations of millennial-scale variability. We examine whether refuges from millennial-scale climate oscillations have left detectable legacies in the patterns of contemporary species richness in eastern North America. We analyse 13 palaeoclimate estimates from climate simulations and proxy-based reconstructions as predictors for the contemporary richness of amphibians, passerine birds, mammals, reptiles and trees. Results suggest that past climate changes owing to AMOC variations have left weak but detectable imprints on the contemporary richness of mammals and trees. High temperature stability, precipitation increase, and an apparent climate fulcrum in the southeastern United States across millennial-scale climate oscillations aligns with high biodiversity in the region. These findings support the hypothesis that the southeastern United States may have acted as a biodiversity refuge. However, for some taxa, the strength and direction of palaeoclimate-richness relationships varies among different palaeoclimate estimates, pointing to the importance of palaeoclimatic ensembles and the need for caution when basing biogeographic interpretations on individual palaeoclimate simulations.
Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, community-level maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992and 2019. We developed ensemble models for each species at three spatial resolutions—0.5, 2.5, and 5 km—across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-kmresolution. However, the species we could model represent only 28%–56% of species found in the conterminous US BBSs across resolutions owing to data limitations. We found that stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States and support community-level conservation when used in tandem with guild maps.
File: Ecological-Applications-2023-Carroll-Mapping-multiscale-breeding-bird-species-distributions-across-the-United-States.pdf
Temperate woodlands are biodiverse natural communities threatened by land use change and fire suppression. Excluding historic disturbance regimes of periodic groundfires from woodlands causes degradation, resulting from changes in the plant community and subsequent biodiversity loss. Restoration, through prescribed fire and tree thinning, can reverse biodiversity losses, however, because the diversity of woodland species spans many taxa, efficiently quantifying biodiversity can be challenging. We assessed whether soundscapes in an eastern North American woodland reflect biodiversity changes during restoration measured in a concurrent multitrophic field study. In five restored and five degraded woodland sites in Wisconsin, USA, we sampled vegetation, measured arthropod biomass, conducted bird surveys, and recorded soundscapes for five days of every 15-day period from May to August 2022. We calculated two complementary acoustic indices: Soundscape Saturation, which focuses on all acoustically active species, and Acoustic Complexity Index (ACI), which was developed to study vocalizing birds. We used generalized additive models to predict both indices based on Julian date, time of day, and level of habitat degradation. We found that restored woodlands had higher arthropod biomass, and higher richness and abundance of breeding birds. Additionally, soundscapes in restored sites had higher mean Soundscape Saturation and higher mean ACI. Restored woodland acoustic indices exhibited greater magnitudes of daily and seasonal peaks. We conclude that woodland restoration results in higher soundscape saturation and complexity, due to greater richness and abundance of vocalizing animals. This bio-acoustic signature of restoration offers a promising monitoring tool for efficiently documenting differences in woodland biodiversity.
File: Persche-et-al-2024_soundscapes_restored-woodlands.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: Rivera-et-al-2021_National-Parks-influence-habitat-use-of-lowland-tapirs_Southern-Yungas-of-Argentina.pdf
