Using neutral landscape models to evaluate the umbrella species concept in an ecotone

Courtney J. Duchardt; Adrian P. Monroe; David R. Edmunds; Matthew James Holloran; Alison G. Holloran; Cameron L. Aldridge

doi:10.1007/s10980-022-01586-7

Home/Archive/USGS

USGS · 70243983

Using neutral landscape models to evaluate the umbrella species concept in an ecotone

Courtney J. Duchardt·Adrian P. Monroe·David R. Edmunds·Matthew James Holloran·Alison G. Holloran·Cameron L. Aldridge

View original source ↗Export metadata ↓

Abstract

Context Steep declines in North American rangeland biodiversity have prompted researchers and managers to use umbrella species as a tool to manage diverse suites of co-occurring wildlife, but efficacy of this method has been variable. Evaluation of prairie and shrubland grouse as umbrellas is typically restricted to observed overlap between umbrella and background species, but this approach does not distinguish between overlap due to ubiquity or niche overlap. Objectives We demonstrate a novel application of neutral landscape models (NLMs) to test the effectiveness of greater sage-grouse ( Centrocercus urophasianus ) as an umbrella species for grassland songbirds at a grassland-sagebrush ecotone in northeastern Wyoming, USA. Methods We leveraged existing spatial data representing sage-grouse habitat in two distinct seasons (nesting and late brood-rearing) and density and distribution of eight grassland songbirds. We applied a permutation-based analysis using NLMs to determine whether overlap between background species and greater sage-grouse was greater than expected by chance. Results Three species (western meadowlark Sturnella neglecta , loggerhead shrike Lanius ludovicianus , and lark bunting Calamospiza melanocorys ) had greater overlap than expected with at least one type of greater sage-grouse habitat, while western kingbirds ( Tyrannus verticalis ) indicated avoidance of all sage-grouse habitat assessed. Conclusions NLMs provided a more nuanced evaluation of the umbrella species concept than previously available and allowed us to differentiate between overlap due to ubiquity (e.g., vesper sparrow; Pooecetes gramineus ) rather than overlap in habitat use. All grassland passerine species with greater than expected overlap with sage-grouse habitat either nest in sagebrush (loggerhead shrike) or often select nest locations underneath small shrubs (western meadowlark, lark bunting). These results indicate that nesting substrate is a potential niche axis to consider when evaluating the umbrella species concept, especially within sagebrush-grassland ecotones.

Explore related subjects

Landscape Ecology Article

Source-reported bounding extent: 40.92596776661196° to 44.99130028797805° latitude; -111.00304481605505° to -104.05240845017104° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Courtney J. Duchardt, Adrian P. Monroe, David R. Edmunds, Matthew James Holloran, Alison G. Holloran, Cameron L. Aldridge. 2023-04-11. Using neutral landscape models to evaluate the umbrella species concept in an ecotone. https://doi.org/10.1007/s10980-022-01586-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Ecological diffusion models are still useful in ecology

Context Connecting animal movement across scales—both among spatial scales and between individual and population level processes—is a central theme in ecology, including the sub-fields of movement and landscape ecology. However, most modeling frameworks operate at either the individual (Lagrangian) or population (Eulerian) level. Objectives We aimed to review how a partial differential equation (PDE) known as the ecological diffusion equation (EDE) offers connections among the scales of animal movement and how it can be embedded in commonly used statistical frameworks (e.g., hierarchical models) and fit to multiple types of data, such as count, presence-only, presence-absence, and telemetry data. In doing so, we also highlight emerging and promising avenues of research related to statistical EDE models. Methods We present how the EDE is derived from a simple set of first principles, can be used to represent both individual animal movement and spatiotemporal population dynamics, and can be fit to data by being applied in a hierarchical statistical framework. Finally, we review literature on recent applications and advances related to EDE models. Results In presenting the derivation of the EDE and reviewing recent advances and applications of EDE models, we established that the EDE can enable a mechanistic understanding of animal movement across scales, as well as the integration of multiple types of data. We also identified a number of topics where EDE models can be advanced to gain additional ecological insight. Conclusions Ecological diffusion is a process that emerges naturally from first principles governing animal movement and can be used to model both individual and population-level spatiotemporal dynamics. Statistical EDE models are related to other modeling frameworks, such as species distribution models, occupancy models, and step-selection analysis. The flexibility of hierarchical statistical modeling allows combining different types of data collected on movement and population dynamics, and the theoretical underpinnings of the EDE allow it to connect ecological processes occurring at fine and coarse spatial scales, as well as individual animal movement and population dynamics.

Landscape Ecology↗

USGS2026

Assessing environmental influences on gene flow for a migratory ungulate in a fire-prone landscape

Context Migratory species traverse long distances across complex landscape mosaics. Shifts in environmental attributes across space and time could affect movement paths and genetic connectivity of migratory species. Objectives We identified boundaries to genetic connectivity based on population structure and evaluated environmental influences on gene flow in a migratory ungulate. We projected how genetic connectivity might be affected by actual wildfire events. Methods We collected over 168,400 GPS locations for 97 bighorn sheep ( Ovis canadensis ) and estimated kinship with 5,140 single nucleotide polymorphisms for 95 individuals. We identified subpopulations and employed machine-learning optimization to evaluate how environmental attributes affect gene flow between them. We projected future genetic connectivity based on recent landscape changes. Results We observed that water bodies and > 70% canopy cover may limit effective dispersal, suggesting that shifts in these landscape attributes, influenced by climate and vegetation management, may affect future gene flow. We also predicted recent wildfire events will change gene flow between three subpopulations, predicting more flow where fire reduced canopy cover at a pinchpoint near a linear lake and less flow where fire was internal to the subpopulation. Conclusions Modifications in canopy cover could be applied to facilitate or impede connectivity and gene flow, depending on management goals. Large, semi-linear lakes could form movement barriers in other bighorn populations. Recent wildfires will likely increase gene flow between semi-isolated subpopulations, with implications for disease management. As wildfire and tree encroachment and densification increase, gene flow between other spatially structured populations in similar landscapes may shift.

British Columbia, Montana↗

USGS2026

Local, regional, and coastwide effects of interactions between storms and relative position in tidal frame on Louisiana coastal marshes

Context In coastal wetlands, land area is dynamic in both space and time, and it is crucial to understand the factors that may tip the balance between wetland area loss and gain. Flooding has been shown to be a key regulator of wetland resilience to land loss through effects on vegetation health and productivity. However, the relationship between elevation and land change is complex, likely interacting with lateral processes such as edge erosion to influence land change rates. Objectives We sought to determine the role of storms, major drivers of lateral erosion, to help explain the complex relationships between elevation and land change at multiple spatial scales and improve predictions of wetland loss. Methods We used long-term records of elevation change, water elevation, and surface wind stress together with remotely sensed land-area change datasets to determine the factors that contribute to land loss in Louisiana (USA) coastal wetlands. Results Our results illustrate that wetland elevation alone cannot be used as a predictor of land change. Annual time-integrated wind stress, which we used as an indicator of storminess, is an important predictor of land change and interacts with elevation to impact wetland gain or loss. The data showed high site-level variation, indicating that local factors strongly determine land change. Nonetheless, we were able to identify broad generalities at the larger scales. We found that the interaction of flooding and storminess varied among geographic regions along the coast. In the Delta Plain region of the coast, sites that are frequently flooded (low elevation) were more likely to experience land gain in stormy years and land loss in calm years. Conversely, sites that were frequently drained (high elevation) had greater land loss during stormy years, presumably due to wind-wave erosion. This trend was not observed in the heavily managed Chenier Plain region, where perpetually flooded wetlands gained land during stormy years, likely due to wind-driven drainage of the marsh. Conclusion Together, these results illustrate that models of wetland vulnerability based upon elevation change or storm effects alone are too simple and will not capture observed trends in land change. Incorporating additional factors such as wind stress and management status can improve predictions of coastal wetland loss and our understanding of the mechanisms controlling resilience of these ecosystems.

Louisiana↗