Grassland birds have exhibited dramatic and widespread declines since the mid-20th century. Greater prairie chickens (Tympanuchus cupido pinnatus) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We used data collected during 2007–2009 and 2014–2015 to investigate effects of land use and grassland management practices on habitat selection and survival rates of greater prairie chickens in central Wisconsin, USA. We examined habitat, nest-site, and brood-rearing site selection by hens and modeled effects of land cover and management on survival rates of hens, nests, and broods. Prairie chickens consistently selected grassland over other cover types, but selection or avoidance of management practices varied among life-history stages. Hen, nest, and brood survival rates were influenced by different land cover types and management practices. At the landscape scale, hens selected areas where brush and trees had been removed during the previous year, which increased hen survival. Hens selected nest sites in hay fields and brood-rearing sites in burned areas, but prescribed fire had a negative influence on hen survival. Brood survival rates were positively associated with grazing and were highest when home ranges contained ≈15%–20% shrub/tree cover. The effects of landscape composition on nest survival were ambiguous. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life-history stages and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high-quality habitat for greater prairie chickens. Brush and tree removal, grazing, hay cultivation, and prescribed fire may be especially beneficial for prairie chickens in central Wisconsin, but trade-offs among life-history stages and the timing of management practices must be considered carefully.File: ece3.6805.pdf
The seasonal dynamics of snow cover strongly affect ecosystem processes and winter habitat, making them an important driver of terrestrial biodiversity patterns. Snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites can capture these dynamics over large spatiotemporal scales, allowing for the development of indices with specific application in ecological research and predicting biodiversity. Here, our primary objective was to derive winter habitat indices (WHIs) from MODIS that quantify snow season length, snow cover variability, and the prevalence of frozen ground without snow as a proxy for subnivium conditions. We calculated the WHIs for the full snow year (Aug-Jul) and winter months (Dec-Feb) across the contiguous US from 2003/04 to 2017/18 and validated them with ground-based data from 797 meteorological stations. To demonstrate the potential of the WHIs for biodiversity assessments, we modeled their relationships with winter bird species richness derived from eBird observations. The WHIs had clear spatial patterns reflecting both altitudinal and latitudinal gradients in snow cover. Snow season length was generally longer at higher latitudes and elevations, while snow cover variability and frozen ground without snow were highest across low elevations of the mid latitudes. Variability in the WHIs was largely driven by elevation in the West and by latitude in the East. Snow season length and frozen ground without snow were most accurately mapped, and had correlations with station data across all years of 0.91 and 0.85, respectively. Snow cover variability was accurately mapped for winter (r = 0.79), but not for the full snow year (r = 0.21). The model containing all three WHIs used to predict winter bird species richness patterns across the contiguous US was by far the best, demonstrating the individual value of each index. Regions with longer snow seasons generally supported fewer species. Species richness increased steadily up to moderate levels of snow cover variability and frozen ground without snow, after which it steeply declined. Our results show that the MODIS WHIs accurately characterized unique gradients of snow cover dynamics and provided important information on winter habitat conditions for birds, highlighting their potential for ecological research and conservation planning.File: GudexCross_etal_2021_MODIS_WHIs_BirdDiversity.pdf
Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (|r| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range |r| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R2adj = 0.45), than 30 m texture metrics (up to R2adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidar-based canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R2adj = 46%). Lidar-based canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity.File: Farwell-et-al-2021_Sat-image-texture_veg_birds_RemSensEnv.pdf
Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013–2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change.File: Farwell-eta-l-2020_Habitat-heterogeneity-30-m-and-birds_Ecological-Applications.pdf
Context: Resource movements across ecosystem
boundaries are important determinants of the diversity
and abundance of organisms in the donor and recipient
ecosystem. However the effects of cross-ecosystem
movements of materials at broader spatial extents than
a typical field study are not well understood.
Objectives: We tested the hypotheses that (1) variation
in abundance of 57 forest songbird species within
four foraging guilds is explained by modeled emergent
aquatic insect biomass inputs from adjacent lakes and
streams and (2) the degree of association varies across
foraging guilds and species within guilds. We also
sought to determine the importance of emergent
aquatic insects while accounting for variation in local
forest cover and edge.
Methods: We spatially modeled the degree to which
distribution and abundance of songbirds in different
foraging guilds was explained by modeled emergent
aquatic insect biomass. We used multilevel models to
simultaneously estimate the responses of species in
four different insectivorous guilds. Bird abundance
was summarized from point counts conducted over
24 years at 317 points.
Results: Aerial insectivores were more abundant in
areas with high estimated emergent insect biomass
inputs to land (regression coefficient 0.30, P\0.05)
but the overall abundance of gleaners, bark-probers,
and ground-foragers was not explained by estimated
emergent insect abundance. The coursing aerial
insectivores had the strongest association with emergent
insects followed by willow flycatcher, olive-sided
flycatcher, and alder flycatcher.
Conclusions: Modeling cross-ecosystem movements
of materials at broad spatial extents can effectively
characterize the importance of this ecological process
for aerial insectivorous songbirds.
Climate change is altering patterns of resource availability and this may have negative effects on insectivorous forest birds in the US upper Midwest. As invertebrate life cycle phenology shifts due to earlier spring leaf-out, nesting birds are vulnerable to phenological mismatches between food supply and demand. Areas with complex topography, and thus a variety of thermal and humidity conditions, may support a greater variety of plant and invertebrate phenological rates and stages within close proximity than are found in areas with simple topography. However, the extent and magnitude of this phenomenon is unclear, as is the degree to which topographic position may influence the ability of species to persist during extreme conditions. We examined the effects of topographic position on the
phenology of a tri-trophic forest system over two years from spring through mid-summer. We hypothesized that in cool microsites the likelihood of trophic mismatches and late season food shortages is lower than in warm microsites. At 70 sites in the Baraboo Hills, part of the Driftless Area of the US Midwest, we recorded leaf-out timing of over 700 deciduous trees, measured weekly changes in invertebrate biomass on understory foliage, and conducted bird point counts to assess avian species richness and density. In stream gorges, cooler temperatures were associated with slight but significant delays in leaf-out timing of canopy and understory deciduous trees relative to upland sites. At all sites, invertebrate biomass was distributed relatively evenly across the study period, in contrast to other temperate zone sites where phenological mismatches have been reported between birds and their invertebrate prey. Invertebrate
biomass was similar in stream gorges and uplands in both study years. Insectivorous bird species richness was greater in stream gorges than in the surrounding upland forest during both seasons and was positively related to Lepidoptera larvae biomass in the understory. Among eight abundant insectivorous bird species, density was similar in uplands and stream gorges, among four species density was higher in uplands, and density of two species was higher in stream gorges. These results suggest that insectivorous birds within this study area are unlikely to experience trophic mismatches, and that despite having cooler microclimates and higher avian species richness, stream gorges did not provide more invertebrate food resources than uplands under the climate conditions of the years in which we
sampled this tri-trophic system.
The positive monotonic relationship between habitat heterogeneity and species richness is a cornerstone of ecology. Recently, it was suggested that this relationship should be unimodal rather than monotonic due to a tradeoff between environmental heterogeneity and population sizes, which increases local species extinctions at high heterogeneity levels. Here, we studied the richness–heterogeneity relationship for an avian community using two different environmental variables, foliage-height diversity and cover type diversity. We analyzed the richness–heterogeneity within different habitat types (grasslands, savannas, or woodlands) and at the landscape scale. We found strong evidence that both positive and unimodal relationships exist at the landscape scale. Within habitats we found positive relationships between richness and heterogeneity in grasslands and woodlands, and unimodal relationships in savannas. We suggest that the length of the environmental heterogeneity gradient (which is affected by both spatial scale and the environmental variable being analyzed) affects the type of the richness–heterogeneity relationship. We conclude that the type of the relationship between species richness and environmental heterogeneity is non-ubiquitous, and varies both within and among habitats and environmental variables.File: Bar-Massada-Wood-2014.pdf
Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.File: Suttidate_etal_RSE_TropicalBirds_DHI_2019.pdf
Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.
The Rufous-throated Dipper Cinclus schulzi is endemic to the Southern Yungas of north-western Argentina and southern Bolivia. The species is categorised as ‘Vulnerable’ on the IUCN Red List on
the basis of small population size and restricted range. The purpose of our study was to determine the distribution of potentially suitable habitat for the Rufous-throated Dipper, estimate its pop-ulation size, and assess potential distribution within strict protected areas, in north-western
Argentina. We surveyed 44 rivers in the Southern Yungas of Argentina from 2010 to 2013 to determine dipper density (i.e. the number of individuals detected per km surveyed). The dipper’s potential distribution was assessed using a maximum entropy modeling approach based on
31 occurrence points and eight bioclimatic and two topographic variables as predictors. The species is dependent on mountain forest rivers, so the potential distribution was restricted to rivers. We estimated dipper population size by multiplying density by the potential distribution along rivers.
Finally, we calculated the extent of suitable habitat contained within the boundaries of Argentina´s National Parks. Dipper density was 0.94 1.55 individuals/km. We estimate that within north-west Argentina there are ~2,815 km of river that are potential habitat, with an area of occupancy of
141 km2 and a population size of 2,657 4,355 dippers. However, of this river extent, less than 5%
is within National Parks. Our results highlight the need to create new and to enlarge existing National Parks that protect the potentially suitable habitat of the species. Although more infor-mation is needed for Bolivia, the country-level area of occupancy and population size of the dipper
found in Argentina provides strong evidence that the IUCN Red List classification of this species as ‘Vulnerable’ is warranted.
While walking through a forest in spring we often marvel at the vivid greenness, listen to birdsong, and mind our steps in order not to get into a spider’s web. Enjoying the moment, we usually do not think about the complexity of this environment, nor the intertwined relations among all of its elements. However, what slips our attention is not going unrecognized by Maia Persche – a Master’s candidate in the SILVIS lab. In her research, Maia seeks to discern the role of topography in the timing of vegetation growth onset within forest, and to understand how topographic position potentially shapes songbird habitat.
To gain insight into these questions, Maia undertook the challenging tasks of measuring tree phenology, and conducting invertebrate and bird surveys in her study area in the Baraboo Hills of Southern Wisconsin. In order to relate these data to each other, each type of survey was carried out at the same 70 locations during narrow time windows throughout the season. Tree phenology surveys occurred in April and May, and invertebrate and bird surveys were repeated throughout the bird breeding season, or until the end of July. At each location, additional data was collected on temperature, tree species composition, and site characteristics. Over the course of two field seasons, she detected 53 insectivorous bird species, and tracked the seasonal abundance of common invertebrate orders (Lepidoptera, Araneae, Hemiptera, Hymenoptera, Diptera and Coleoptera).
Based on only a portion of measurements collected, Maia has already drawn some interesting preliminary conclusions. Trees leafed out slightly later in stream gorges than in uplands, and although invertebrate biomass was related to tree phenology, it did not appear to follow a predictable yearly pattern. However, sheltered stream gorges supporteded high invertebrate biomass during the mid- and late summer. This could be important for double-brooded bird species that still have active nests in July and can be limited by food availability in some habitats. Overall, stream gorges supported the highest bird species richness, perhaps due to stable food resources or habitat complexity. Also, a strong association has become evident between particular tree species and invertebrate orders, suggesting that tree composition may be more important than topographic context for some folivorous invertebrates.
Under shifting climate conditions in deciduous forests, it is important to identify areas where habitat quality for species is likely to remain high. To assess bird territory quality in different topographic situations, Maia used feather growth bar analysis for a few widely distributed forest species (Wood Thrush, Red-eyed Vireo, and Ovenbird). She captured birds throughout her study area using mist nets, playback calls, and bird models. She then banded the birds, took structural measurements, and pulled one tail feather. Growth bars, or horizontal bands along the feather, correspond to diet richness of the bird while the feather was growing, and will be used to assess social dominance and habitat quality. Although this approach provides a detailed look at habitat quality, it is also the most difficult to carry out in the field.
Maia has collected a large amount of data, and analyzing the relationships among different factors and trophic levels is somewhat daunting, but she approaches it with great enthusiasm. Maia is currently working to determine how bird territory density varies according to topographic context. It is definitively worth staying tuned to see what new results Maia uncovers!