Snow cover dynamics: an overlooked feature of winter bird occurrence and abundance

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

How well can deep learning models explain multiscale hierarchical habitat selection in birds?

Habitat selection is a fundamental behavior of species that shapes a wide range of ecological processes, including species distribution, abundance, nutrient transfer, and tropic dynamics. The study of habitat selection is important to understand the interaction between species and environment. But it is a multivariate and hierarchical process, in which species are distinctively affected by several factors at multiple spatial scales. Therefore, it is important to understand how species select their habitat, what are the important spatial scales, and how the habitat selection process varies for different species.

Figure 1: Conceptual hierarchy of the decision-making process of habitat use by a migratory songbird (Stanley et al 2021).
Figure 1: Conceptual hierarchy of the decision-making process of habitat use by a migratory songbird (Stanley et al 2021).

Hierarchical habitat selection in birds varies greatly by species due to their ecological niches and behaviors. For instance, the Northern Spotted Owl specializes in old-growth conifers for nesting, forages in mature forests, and prefers undisturbed landscapes for its home range. Conversely, the Kirtland’s Warbler prefers, early to mid-successional jack pine forests, growing on sandy soil for nesting, these forests provide the specific vegetation structure and insect abundance that are essential for their foraging needs. Studying habitat selection is therefore crucial for effective conservation and ecosystem management, as it provides insights into their ecological requirements and aids in preserving their populations and the overall health of ecosystems.

Figure 2: Kirtland’s Warbler (left) Spotted Owl (right)
Figure 2: Kirtland’s Warbler (left) Spotted Owl (right)

Despite notable advancements in the field, our understanding of the hierarchical aspects of habitat selection in birds remains limited. Habitat selection models typically rely on satellite data from a single sensor and scale, which limits their effectiveness in capturing spatial patterns of bird habitat.

Akash Anand is currently conducting a study aimed at modeling multiscale hierarchical habitat selection in birds and explaining the factors influencing individual species’ choices. His research investigates the crucial spatial scales for different species and identifies local environmental features that play a pivotal role in overall habitat selection decisions. To achieve this, he employs deep learning models to gain insights into the intricate interactions between species and their environments.

In conclusion, Akash’s research aims to determine the crucial spatial scales for individual species, providing valuable insights for conservationists and policymakers. Additionally, the findings will provide evidence of how the same species respond to varying environmental conditions and how their choices differ in different scenarios. This knowledge will inform more effective conservation and management strategies.

Performance of novel remotely-sensed variables in maps of bird species distributions in Argentina

Mitred Parakeet (Psittacara mitratus) eating the fruit of a Schinus sp. tree
Nothofagus forest in Tierra del Fuego, Argentina.
Nothofagus forest in Tierra del Fuego, Argentina.

Halting biodiversity declines and promoting sustainable ecosystem usage are major conservation goals. To do so, it is necessary to understand the environmental correlates of biodiversity patterns.

Environmental variables used in biodiversity modelling come from a variety of sources and have varying levels of power to explain distributions of different species. Many environmental variables that have been used regularly for many years have shortcomings- they may not cover large areas, may not capture suitable habitat, or may not be able to capture changes in environmental conditions over time or space. Increasingly, novel remotely-sensed environmental data are being developed for modelling biodiversity patterns. Novel remotely sensed products may complement or even offer better results than environmental variables that have been used for many years.

Olah set out to identify sets of complementary variables, from among a set of standard variables and newly created variables, that can improve species distribution modelling. Olah used a combination of land cover, elevation, precipitation, and temperature variables, that are commonly used in species distribution modelling, and a set of novel-remotely sensed products to model distributions of forest affiliated bird species in Argentina.

Yungas forest in Calilegua National Park, Jujuy, Argentina
Yungas forest in Calilegua National Park, Jujuy, Argentina

The set of novel environmental variables were created by SILVIS lab postdoctoral researcher Eduarda Silveira. These products measure spatial and interannual variation in the phenology of land surface temperature and forest vegetation greenness. Olah predicted that areas with more spatial variability in phenology and thermal conditions are more likely to host more species because there are a variety of resources and thermal conditions in close proximity, allowing many species to coexist in a small area. These areas may also buffer against high year-to-year variation in conditions because organisms are more likely to have access to refugia or resources that could allow them to persist. Temporal variation in forest greenness or temperature describes how consistent conditions are between years. High variability means that phenological events are not occurring at a predictable time, while low variability means that events are occurring predictably each year.

In another new product developed by Silveira, ground forest inventory data was combined with radar-based remotely-sensed data, resulting in modelled forest structure wall-to-wall across Argentina. Silveira also developed maps of forest phenoclusters and phenocluster diversity. Phenoclusters classify different forest types in Argentina, based on vegetation phenology, land surface temperature, and precipitation. Olah thinks phenoclusters are a more ecologically relevant way to characterize habitat important to bird species than typical land cover maps. Phenoclusters capture functional rather than only compositional or structural characteristics. By comparing how well these novel remotely-sensed products and traditionally used variables perform in species distribution modelling Olah assessed their usefulness for biodiversity mapping.

Polylepis forest in an Andean Mountain valley, Jujuy, Argentina.
Polylepis forest in an Andean Mountain valley, Jujuy, Argentina.

Olah developed species distribution models for 152 forest bird species. She found that among three sets of models she constructed, those containing novel, traditional, or a mixed set of variables, performance was similar. However, models constructed from the mixed set of variables performed slightly better than models containing only one or the other set of data. The variables that were included in the greatest number of individual species’ distribution models included precipitation seasonality, precipitation of the driest quarter, as well as spatial heterogeneity in winter land surface temperature, which is a novel variable. Her results highlight how variables derived from different sources can offer complementary information for biodiversity modelling. Her models contribute to forest harvest planning in Argentina.

Distribution and habitat use of the endemic Yungas Guan Penelope bridgesi in the Southern Yungas of Argentina

Identifying the factors that determine the spatial distribution and habitat use of species of conservation importance is essential to developing effective conservation and management strategies. As seed dispersers, guans play a key role in the regeneration of forests in South America and are threatened mainly by habitat loss and hunting pressure. The Yungas Guan Penelope bridgesi, an endemic species restricted to the Southern Yungas of Argentina and Bolivia, has been recently recognized as a separate species. To determine the conservation status of Yungas Guan, information on its distribution and habitat use is urgently needed. The objectives of our work were to 1) determine the potential distribution of the Yungas Guan in the Southern Yungas of Argentina and 2) assess the influence of environmental and anthropogenic covariables on habitat use of the species. We used records of Yungas Guan to model the potential distribution of the species with MaxEnt software and developed occupancy models to determine habitat use and influential elements of the landscape (puestos, urban areas, roads, rivers, and elevation). We obtained data on the presence of Yungas Guan with camera traps, with an effort of 6,990 camera trap-days. The total potential distribution of the species was 21,256 km2.We found that the habitat use by Yungas Guan increased with proximity to rivers and streams. The probability of habitat use was 0.27, with a range of 0.02–0.42.Of the total potential distribution area, 15,781 km2 (81%) had a probability of habitat use greater than 0.2. This study is the first in determining the potential distribution of Yungas Guan in the Southern Yungas of Salta and Jujuy provinces in Argentina and highlights the importance of conducting analyses with occupancy models to assess the influence of environmental and anthropogenic variables and threats to cracid species.

File: Tejerina-et-al-2022-BCI.pdf

Arctic waterfowl migration through Eurasian steppe: how to catch short-term environmental conditions and identify key migratory habitats using satellite images

Long-distance migrations are an important part of the life cycle for most Arctic waterfowl. The birds spend several months each year between their breeding and wintering places, with most of this time staying at their stopover sites to rest and feed. Habitat quality at the stopovers determine subsequent survival during migrations and reproductive success. Two questions present themselves: where are the key waterfowl habitats along their migratory routes, and what landscape features make these places vitally important? Understanding this is crucial for bird protection and population management.

Fig. 1 Bewick’s Swan
Fig. 1 Bewick’s Swan

Satellite images are used widely for landscape research; however, their application to bird migration studies is challenging. First, environmental conditions within a large area are not the same during migration period, so there is no single time window suitable to select satellite images for the entire area. Second, favorable conditions at each stopover site are short-term, as birds spend only a few weeks at each site. The needed satellite images may be unavailable for those exact periods due to weather conditions (clouds etc.), making it difficult to obtain sufficient data for a single migratory season. Third, environmental conditions and migration time could vary by several weeks depending on weather of a given year, which, in turn, impedes combining images from different years.

Natalia studied how to use satellite images for delineating key migratory habitats using the example of spring migration of Bewick’s Swan in Eurasian steppe (fig. 1). To overcome the limitation related to satellite image availability, Natalia used snow melt as a synchronizing point. In practice, specific dates on which the swans appear at one or another area do not really matter because the birds will move farther north as soon as ice-free water and food become available there. In other words, they arrive soon after snow melt and stay a couple of weeks until more northern areas become snow-free.

Natalia used daily MODIS data to identify where snow melted each year in different parts of the swan’s migratory flyways and then filtered Landsat images using that information. This allowed her to combine Landsat images for all migration areas from different years at the same phenological phase. This approach made it possible to produce accurate and detailed landscape maps demonstrating what environmental conditions prevailed at stopovers at exactly the time when the swans were present. With these maps species distribution modeling can delineate key swan’s habitats.

Fig.2 Key habitats during summer
Fig.2 Key habitats during summer

The maps have revealed the critical landscape feature important to the birds: numerous local depressions scattered across croplands (fig. 2). In summer these appear as a part of the agricultural landscape (only the smallest, lowermost places may not be ploughed and get overgrown with wildflowers) and are hardly detectable (fig. 3, e) on satellite images. In spring, however, they accumulate melted water to become shallow temporary water bodies (fig. 3, a-c). These flooded depressions provide migratory birds with food and refuge so that the waterfowl do not need to move between roosting and feeding sites. They are also available 10-15 days before ice-out on lakes, allowing birds to migrate and potentially reach breeding grounds earlier.

Fig.3 Changes in open water in the steppe during spring. RGB: SWIR 1, NIR, Red bands. Red circle –flooded fields, yellow square -permanent lake. Bright blue color indicates snow/ice, black color indicates open water.
Fig.3 Changes in open water in the steppe during spring. RGB: SWIR 1, NIR, Red bands.
Red circle –flooded fields, yellow square -permanent lake.
Bright blue color indicates snow/ice, black color indicates open water.

More generally, Natalia’s research provides a useful approach to understanding the key short-term conditions that birds rely on during migrations. Natalia’s results have important implications for conservation efforts, such as the creation of protected areas and free hunting zones and adjusting land management in agricultural lands.

Comparing Approaches to Identify Protected Areas for Wildlife across the U.S.

By schlorian 13 May 2019

Species and populations are declining rapidly, with over 3 billion birds lost in the past 50 years. Astoundingly, the US is on track to lose 50% of its remaining individual birds in 50 more years without intervention ( Birds, unfortunately, are not alone, as 40% of all species are projected to face extinction by the end of this century. Despite these alarming numbers, conservation spending in the US has remained relatively stable over the past years – roughly $6-7 billion with few exceptions. Therefore, one of the most challenging questions for scientists is where will conservation action – and protected areas in particular – do the most to protect species of conservation concern.

Kathleen Carroll’s current work in the SILVIS Lab compares various biodiversity metrics, each with unique assumptions, to my previous maps of threatened/endangered and decreasing species (see my previous webstory for more on that project). I can use these comparisons to evaluate how well these additional metrics, which are usually treated as direct surrogates for biodiversity, capture the conservation patterns necessary to protect threatened or endangered species. I also will evaluate which, if any, combinations of these metrics work best to inform conservation planning on regional and national scales. To do this, I will model all metrics for the US and then compare them directly to my threatened/endangered species data. I will do so using Marxan, a conservation planning problem support tool, to create nationwide maps that identify conservation priority areas. These maps, one for each metric, will include a certainty estimate based on pixel importance across data layers and identify gaps in protected areas. By comparing different metrics, we will be provided maps of high-certainty high-priority areas where land managers and agencies can focus on endangered species conservatio through designation of new protected areas.


The Benefits of Satellite Data for Wildlife Species Distribution Models Across the US

For centuries, humans have recognized that our collective actions modify and shape the world around us. These actions also have direct and lasting impacts on the plants and animals that many communities rely on for their livelihoods ─ often referred to as ecosystem services. For example, in the Rocky Mountain West, local communities rely on provisioning from local wildlife (e.g., hunting), safe water and air from local plants and snowmelt, ecosystem cultural services for tribal communities, natural soil creation for agriculture, pastures for grazing livestock, and income from tourists seeking to fish, hike, hunt, ski, and view local wildlife. Similarly, the Great Lakes wetlands provide fisheries habitat that supports wildlife and people, space for recreational activities and tourism, hydro and wind power, shoreline protection, sediment trapping, and storage for nutrients and carbon. The ecosystem services provided by any one region, such as the Rocky Mountain West or the Great Lakes, are intrinsically tied to the health of the species and people in that community. Without plants and animals, many of these services could suffer. Therefore, one of the most challenging questions for scientists is how to ensure that our science advances conservation and subsequently bolsters ecosystem services that support local communities.

A small sample of the diversity of birds in the lower 48. There are an estimated > 1,100 species of birds in the United States. Photo source: Creative Commons.

In the SILVIS Lab, Kathleen Carroll identifies and averts biodiversity loss by developing complex models to predict and examine biodiversity patterns across the US. Biodiversity, which measures the variety of life in an area, is essential to conservation. If biodiversity declines, there is a risk to both species and ecosystem services. By modeling and predicting biodiversity over broad areas, we can determine where the biggest threat to species is. While this approach is simple in theory, extensive information about where species are and what they select habitat based on is key. Researchers have been limited by access to data. The SILVIS lab has developed new satellite indices, which provide detailed datasets that I am using in complex models. My preliminary results show higher predictive power from relatively new machine learning models, called randomForest models, compared to other approaches. Access to the new satellite datasets and complex models helps me to 1) further evaluate the value of fine-scale satellite data for biodiversity mapping, 2) develop predictive biodiversity models, and 3) provide maps to land trusts and communities to help them protect species and ecosystem services.

Preliminary map of bird species richness – a biodiversity metric. Photo credit: Dave Helmers.

Garbage in may not equal garbage out: sex mediates effects of ‘junk food’ in a synanthropic species

Human influence on ecosystems is rapidly expanding, and one consequence is the increased availability of human food subsidies to wildlife. Human food subsidies like refuse and food scraps are widely hypothesized to be ‘junk food’ that is nutritionally incomplete; however, the impacts of ‘junk foods’ on the health and fitness of individual organisms remain unclear. In this study, we aimed to understand how human food consumption affects the body condition and fecundity of a generalist predator, the Steller’s jay (Cyanocitta stelleri). We used stable isotope analysis to quantify individual human food consumption (using d13C as a proxy), estimated individual body condition based on body mass and feather growth bar width and assessed jay fecundity. Adults consumed more human food than juveniles on average, and we observed sex-specific responses to human food use where male body condition tended to increase, whereas female body condition tended to decline with human food consumption. However, fecundity was not strongly related. Thus, we found some evidence for the ‘junk food’ hypothesis in this system, which suggests that human foods may not be an equal replacement for natural foods from a nutritional perspective, especially for females. Human foods tend to be carbohydrate rich, but protein poor, which may benefit males because they are larger and limited overall by calorie intake. Females, particularly reproducing females, are more nutritionally limited and thus may experience fewer benefits from ‘junk food’. Our study advances knowledge of human–wildlife interactions by increasing the resolution of our understanding of the fitness benefits, or detriments, experienced by individuals that consume human foods.

File: Ng-et-al_2023_Garbage-in-may-not-equal-garbage-out_Journal-of-Urban-Ecology-9_1_jua014.pdf

Multi-grain habitat models that combine satellite sensors with different resolutions explain bird species richness patterns best

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

Low Kirtland’s Warbler fledgling survival in Wisconsin plantations relative to Michigan plantations

The Kirtland’s Warbler (Setophaga kirtlandii) is a formerly endangered habitat specialist that breeds mainly in young jack pine (Pinus banksiana) forests in northern Lower Michigan, USA. The species is conservation-reliant and depends on habitat management. Management actions have primarily focused on creating jack pine plantations, but the species also breeds in red pine (Pinus resinosa) plantations in central Wisconsin, USA. However, the plantations were not intended as breeding habitat and have suboptimal pine densities. While nesting success is similar between low-density red pine plantations and optimal jack pine habitat, it is not clear if low-density red pine plantations support high fledging survival. If high-quality nesting and post-fledging habitat are not synonymous, fledgling survival and breeding population recruitment may be low. We characterized survival, habitat use, and movement patterns of dependent Kirtland’s Warbler fledglings in Wisconsin red pine plantations and compared fledgling survival between Wisconsin and Michigan. Mayfield cumulative survival estimates at 30 days post-fledging were 0.20 for Wisconsin fledglings and 0.43–0.78 for Michigan fledglings. Logistic exposure cumulative survival estimates for Wisconsin fledglings were 0.23–0.34 at 30 days post-fledging. Fledglings in Wisconsin used areas where vegetation cover and density of red and jack pine were high relative to available areas but not at greater proportions than what was available. Our findings demonstrate that red pine plantations with low pine densities were not equally suitable as nesting and post-fledging habitat, as fledgling survival rates were low. We hypothesize that reduced habitat structure, and not particular pine species, likely contributed to reduced fledgling survival in Wisconsin. Thus, we recommend including red pine as a component in managed Kirtland’s Warbler habitat only if tree densities approach optimal levels.

File: Olahetal_2023_KirtlandsWarblerFledglingSurvivalWisconsinOrnithologicalApplications.pdf