Birds select habitat characteristics, such as variability in habitat structure, across multiple spatial scales (grain and extent). Measuring habitat variability at multiple scales can better capture factors that influence avifauna communities than focusing on one scale only. One valuable tool in assessing habitat heterogeneity is the cumulative dynamic habitat index (DHI), which is derived from satellite data and captures temporal variability in vegetation productivity. Our goals were to develop new habitat measures from the cumulative DHI at multiple scales based on scalograms, and to test their performance in models of bird abundance. We counted birds at 188 plots during three breeding seasons (2007–2009) at Fort McCoy military installation, USA, to assess the abundance of forest (ovenbird), shrubland (indigo bunting), and grassland (grasshopper sparrow) bird specialists. We then calculated NDVI based on PlanetScope (3 m), Sentinel-2 (10 m), Landsat-8 (30 m), and MODIS (250 m) data to quantify cumulative DHI. We summarized the averaged NDVI cumulative DHI within multiple extents around each bird survey and developed 11 new habitat measures to test their predictive power in models of bird abundance. We found positive relationships between cumulative DHI at different extents and the abundances of both ovenbirds and indigo buntings, a forest and a shrubland specialist, respectively; and a negative relationship with grasshopper sparrows, a grassland specialist. In multiple linear regression models that incorporated single- and multi-grain predictors, the scalogram habitat measures explained moderate to high levels of variability in bird abundance, with R2 = 0.77, 0.37, and 0.75 for our forest, shrubland, and grassland specialists, respectively. Our results show that scalograms are an effective tool for capturing multiscale habitat configuration, because they capture the variability of habitat conditions in forests, shrublands, and grasslands. The scalogram habitat measures that we developed can be computed using our new R package ‘scalogram’.
File: Ecography-2025-Silveira-Scalogram-habitat-measures-as-predictors-of-bird-abundance.pdf
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
In order to support rare species, we need to understand the threats to them. To identify the threats faced by non-breeding Spotted (Nordmann's) Greenshank Tringa guttifer we visited coastal sites throughout the Gulf of Thailand. The Inner Gulf of Thailand supports approximately 20–30% of the East Asian-Australasian Flyway global population of 1,500–2,000 Spotted Greenshanks. Identifying the specific threats they face in this area is therefore critical to develop measures to prevent further decline. We assessed the conservation situation at four ‘hotspots’ for Spotted Greenshank, areas supporting >1% of the global population. We identified three major threats: habitat loss, disturbance, and illegal netting. Each of these threats require place-based management interventions if long-term conservation of Spotted Greenshank, and other EAAF waterbirds, is to be accomplished.
File: WS-1313-Maleko.pdf
Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian diversity. To examine how direct measurements of canopy structural characteristics explain bird diversity across North America, we analyzed 18 GEDI metrics from 2019 to 2022, along with corresponding Breeding Bird Survey (BBS) counts and AVONET morphological data, analyzing effects across broad regions and at varying spatial extents. We grouped 440 bird species into 20 ecological guilds under six guild categories and employed random forest algorithms to model avian diversity across eight spatial extents (1, 2, 3, 4, 5, 10, 20, and 39.2 km). The models predicted six diversity indices, including species richness (sRich), functional richness (fRich), evenness (fEve), dispersion (fDis), divergence (fDiv), and redundancy (fRed) across eight spatial extents. The best-predicted guilds varied for each diversity index. The most accurate models were sRich (pseudo-R2 = 0.71, RMSE = 4.28) and fRed (pseudo-R2 = 0.60, RMSE = 0.13) for forest specialists guilds; fRich (pseudo-R2 = 0.55, RMSE = 0.18) for urban guilds; fEve (pseudo-R2 = 0.28, RMSE = 0.08) for insectivore guilds; and fDiv (pseudo-R2 = 0.38, RMSE = 0.12) and fDis (pseudo-R2 = 0.53, RMSE = 0.87) for short distance migrants guilds. Our results highlight the critical role of canopy structure, including its horizontal and vertical distribution and variation, in predicting avian diversity, as measured by the mean number of detected modes (num_detectedmodes), the standard deviation of foliage height diversity (FHD), num_detectedmodes, canopy cover, and plant area index (PAI) across the spatial extents centered on BBS routes. Therefore, we recommend incorporating the GEDI metrics into avian diversity modeling and mapping across North America, thereby potentially enhancing bird habitat management and conservation efforts.
File: Xu-1-s2.0-S0034425724004723-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
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
There is ongoing debate among conservationists regarding the value of small habitat patches to sustain wild populations in farmlands. Our goal was to assess bird abundance in riparian forests differing in terms of size, configuration, landscape conditions and degradation level, to both inform the debate and to identify conservation strategies to maintain diverse agricultural landscapes. We conducted bird point-counts in 91 sites in 2016 across an agricultural valley in Chile. Using models that accounted for imperfect detection, we assessed variation in bird densities in riparian forests with different sizes and configuration, landscapes, and habitat characteristics. We found support in univariates models for our prediction that bird densities varied across riparian forest of various sizes and configuration for 10 of 16 bird species. However, when we added landscape and habitat characteristics to the model, we found that the densities of many of the birds were best explained by forest cover around their local (1 ha) and broader (50 ha) landscape combined with forests characteristics (e.g., invasive tree abundance). For example, Black-throated huet-huet and Chucao Tapaculo were positively associated with forest cover at the broader landscape (50 ha), but showed no response to number of patches, patch-size and Euclidean distance. Our results showed no evidence of negative fragmentation effect per se (i.e., after controlling for habitat area). While agricultural landscapes provide habitat for some species that use small forest patches, conservation strategies focusing on maintaining high level of forest cover and native vegetation are required to secure populations of forest affiliated species.
File: Rojas_et_all_BioConservation_2024_Riparian_forest_patches.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
Heterogeneous vegetation supports higher species richness than homogenous vegetation, which is why efficiently identifying heterogenous vegetation can be useful for biodiversity conservation. Satellite remote-sensing data provide an opportunity to generate vegetation heterogeneity metrics and to explore the phenology of vegetation patterns. Phenoclusters are vegetation types with similar phenological characteristics, and valuable for capturing vegetation habitat heterogeneity patterns. Our goal was to map phenoclusters for Wisconsin, USA, at 10-m spatial resolution based on land surface phenology metrics from EVI (Enhanced Vegetation Index) Sentinel-2 data. We characterized each phenocluster based on landcover composition and structure, phenology timing, and environmental factors, and compared them to bird species richness. We also calculated the diversity of phenoclusters at multiple spatial extents. We identified 14 phenoclusters in Wisconsin, each with distinct landcover composition and structure, and unique phenological characteristics. Our remotely-sensed phenoclusters effectively captured environmental gradients, with elevation and temperature emerging as the most important driving variables. Furthermore, the phenoclusters successfully captured bird biodiversity patterns, especially richness of forest and grassland specialist. Our results identified phenological patterns among Wisconsin’s forests, shrublands, and grasslands, capturing phenological timing both among and within the same tree species. Phenoclusters are a valuable tool for capturing vegetation habitat heterogeneity, phenology diversity and biodiversity patterns, as well as climate change effects.
File: Silveira-et-al_2024_WisconsinPhenoclusters.pdf
Snow cover dynamics (i.e. depth, duration and variability) are dominant drivers of ecological processes during winter. For overwintering species, changes and gradients in snow cover may impact survival and population dynamics (e.g. facilitating survival via thermal refugia or limiting survival via reduced resource acquisition). However, snow cover dynamics are rarely used in species distribution modelling, especially for over-wintering birds. Currently, we lack understanding of how snow cover gradients affect overwintering bird distributions and which functional traits drive these associations at regional and continental scales. Using observations from eBird, a global community science network, we explored the effects of snow cover dynamics on continental pat-terns of occurrence and counts for 150 bird species. We quantified the relative impor-tance, species-specific responses and trait-based relationships of bird occurrence and abundance patterns to ecologically relevant snow cover dynamics across the United States. Snow cover dynamics were important environmental predictors in species dis-tributions models, ranking within the top three predictors for most species occurrence (> 90%) and count (> 79%) patterns across the contiguous United States. Species exhibited a gradient of responses to snow cover from snow association to snow avoid-ance, yet most birds were limited by long, persistent snow seasons. Duration of winter and percent frozen ground without snow structured species distributions in the east-ern USA, whereas snow cover variability was a stronger driver in the western USA. Birds associated with long, persistent snow seasons had traits associated with greater dispersal capacity and dietary diversity, whereas birds inhabiting regions with variable snow cover were generally habitat generalists. Our results suggest that various snow cover dynamics are important ecological filters of species distributions during winter. Global climate change is rapidly degrading key characteristics of seasonal snow cover. A changing cryosphere may elicit variable distributional changes for many overwinter-ing birds, potentially accelerating range shifts and novel community assemblages.
File: Ecography-2022-Keyser-Snow-cover-dynamics-an-overlooked-yet-important-feature-of-winter-bird-occurrence-and.pdf
