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At least 181 records · Page 10Linked to original sources
USGS2025

Landscape changes elevate the risk of avian influenza virus diversification and emergence in the East Asian–Australasian Flyway

Highly pathogenic avian influenza viruses (HPAIV) persistently threaten wild waterfowl, domestic poultry, and public health. The East Asian–Australasian Flyway plays a crucial role in HPAIV dynamics due to its large populations of migratory waterfowl and poultry. Over recent decades, this flyway has undergone substantial landscape changes, including both losses and gains of waterfowl habitats. These changes can affect waterfowl distributions, increase contact with poultry, and consequently alter ecological conditions that favor avian influenza virus (AIV) evolution. However, limited research has assessed these likely impacts. Here, we integrated empirical data and an individual-based model to simulate AIV transmission in migratory waterfowl and domestic poultry, including wild-to-poultry spillover and reassortment dynamics in poultry, across landscapes representing the years 2000 and 2015. We used the reassortment incidence as a proxy for ecological and transmission conditions that support viral diversification and the emergence of novel subtypes. Our simulations show that landscape change reshaped the waterfowl distribution, facilitated bird aggregation at improved habitats, increased coinfection, and raised reassortment rate by 1,593%, indicating a substantially higher potential for viral diversification and emergence. Model-generated risk maps show expanded and increased reassortment risk in southeastern China, the Yellow River Basin, and northeastern China. These findings suggest the importance of landscape change as a driver of potential AIV diversification and subtype emergence. This underscores the need for interdisciplinary approaches that integrate landscape dynamics, host movement, and viral evolution to better assess and mitigate future risk.

East Asian–Australasian Flyway↗
USGS2010

How vegetation and sediment transport feedbacks drive landscape change in the Everglades and wetlands worldwide

Mechanisms reported to promote landscape self‐organization cannot explain vegetation patterning oriented parallel to flow. Recent catastrophic shifts in Everglades landscape pattern and ecological function highlight the need to understand the feedbacks governing these ecosystems. We modeled feedback between vegetation, hydrology, and sediment transport on the basis of a decade of experimentation. Results from more than 100 simulations showed that flows just sufficient to redistribute sediment from sparsely vegetated sloughs to dense ridges were needed for an equilibrium patterned landscape oriented parallel to flow. Surprisingly, although vegetation heterogeneity typically conveys resilience, in wetlands governed by flow/sediment feedbacks it indicates metastability, whereby the landscape is prone to catastrophic shifts. Substantial increases or decreases in flow relative to the equilibrium condition caused an expansion of emergent vegetation and loss of open‐water areas that was unlikely to revert upon restoration of the equilibrium hydrology. Understanding these feedbacks is critical in forecasting wetland responses to changing conditions and designing management strategies that optimize ecosystem services, such as carbon sequestration or habitat provision. Our model and new sensitivity analysis techniques address these issues and make it newly apparent that simply returning flow to predrainage conditions in the Everglades may not be sufficient to restore historic landscape patterns and processes.

American Naturalist↗
USGS2019

Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss

Background and Aims Coastal wetlands have evolved to withstand stressful abiotic conditions through the maintenance of hydrologic feedbacks among vegetation production and flooding. However, disruption of these feedbacks can lead to ecosystem collapse, or a regime shift from vegetated wetland to open water. To prevent the loss of critical coastal wetland habitat, we must improve understanding of the abiotic-biotic linkages among flooding and wetland stability. The aim of this research was to identify characteristic landscape patterns and thresholds of wetland degradation that can be used to identify areas of vulnerability, reduce flooding threats, and improve habitat quality. Methods We measured local- and landscape-scale responses of coastal wetland vegetation to flooding stress in healthy and degrading coastal wetlands. We hypothesized that conversion of Spartina patens wetlands to open water could be defined by a distinct change in landscape configuration pattern, and that this change would occur at a discrete elevation threshold. Key Results Despite similarities in total land and water cover, we observed differences in the landscape configuration of vegetated and open water pixels in healthy and degrading wetlands. Healthy wetlands were more aggregated, and degrading wetlands were more fragmented. Generally, greater aggregation was associated with higher wetland elevation and better drainage, compared to fragmented wetlands, which had lower elevation and poor drainage. The relationship between vegetation cover and elevation was non-linear, and the conversion from vegetated wetland to open water occurred beyond an elevation threshold of hydrologic stress. Conclusions The elevation threshold defined a transition zone where healthy, aggregated, wetland converted to a degrading, fragmented, wetland beyond an elevation threshold of 0.09 m NAVD88 (0.27 m MSL), and complete conversion to open water occurred beyond 0.03 m NAVD88 (0.21 m MSL). This work illustrates that changes in landscape configuration can be used as an indicator of wetland loss, with specific elevation thresholds to inform restoration and conservation planning to maximize wetland stability in anticipation of flooding threats.

Louisiana, Texas↗
USGS2021

Nest microclimates of Greater Sage-Grouse in a post-megafire landscape: does selection equate to success?

Temperature at fine spatial scales is an important driver of nest site selection for many avian species during the breeding season and can influence nest success. Sagebrush ( Artemisia spp. ) communities have areas with high levels of vegetation heterogeneity and high thermal variation; however, fire removes vegetation that provides protection from predators and extreme environmental conditions. To examine the influence of microclimates on Greater Sage-Grouse ( Centrocercus urophasianus ) nest site selection and nest success in a fire affected landscape, we measured black bulb temperature (Tbb) and vegetation attributes (e.g. visual obstruction) at 3 spatial scales (i.e. nest bowl, microsite, and landscape) in unburned and burned areas. Nest bowls exhibited greater buffering of Tbb than both nearby microsites and the broader landscape. Notably, nest bowls were warmer in cold temperatures, and cooler in hot temperatures, than nearby microsites and the broader landscape, regardless of burn stage. Nest survival was higher for nests in unburned areas compared to nests in burned areas (unburned NS = 0.43, 95% CI: 0.33 to 0.54; burned NS = 0.24, 95% CI: 0.10 to 0.46). Amount of bare ground was negatively associated with nest survival, but effects diminished as the amount of bare ground reached very low levels. Shrub height and visual obstruction were positively associated with nest survival during the entire study period whereas, minimum Tbb had a weaker effect. Our findings demonstrate that thermoregulatory selection by Greater Sage-Grouse at nest sites had marginal effects on their nest survival. However, given that increases in vegetation structure (e.g. shrub height) provides thermal refuge and increases nest survival, vegetation remnants or regeneration in a post-fire landscape could be critical to Greater Sage-Grouse nesting ecology.

Oregon, Nevada↗
USGS2016

Barrier displacement on a neutral landscape: Towards a theory of continental biogeography

Macroevolutionary theory posits three processes leading to lineage diversification and the formation of regional biotas: dispersal (species geographic range expansion), speciation (species lineage splitting), and extinction (species lineage termination). The Theory of Island Biogeography (TIB) predicts species richness values using just two of these processes; dispersal and extinction. Yet most species on Earth live on continents or continental shelves, and the dynamics of evolutionary diversification at regional and continental scales are qualitatively different from those that govern the formation of species richness on biogeographic islands. Certain geomorphological processes operating perennially on continental platforms displace barriers to gene flow and organismal dispersal, and affect all three terms of macroevolutionary diversification. For example, uplift of a dissected landscape and river capture both merge and separate portions of adjacent areas, allowing dispersal and larger geographic ranges, vicariant speciation and smaller geographic ranges, and extinction when range sizes are subdivided below a minimum persistence threshold. The TIB also does not predict many biogeographic and phylogenetic patterns widely observed in continentally distributed taxa, including: 1, power function-like species-area relationships; 2, log-normal distribution of species geographic range sizes, in which most species have restricted ranges (are endemic) and few species have broad ranges (are cosmopolitan); 3, mid-domain effects with more species towards the geographic center, and more early-branching, species-poor clades towards the geographic periphery; 4, exponential rates of net diversification with log-linear accumulation of lineages through geological time; and 5, power function-like relationships between species-richness and clade diversity, in which most clades are species-poor and few clades are species-rich. Current theory does not provide a robust mechanistic framework to connect these seemingly disparate patterns. Here we present SEAMLESS (Spatially-Explicit Area Model of Landscape Evolution by SimulationS) that generates clade diversification by moving geographic barriers on a continuous, neutral landscape. SEAMLESS is a neutral Landscape Evolution Model (LEM) that treats species and barriers as functionally equivalent with respect to model parameters. SEAMLESS differs from other model-based biogeographic methods (e.g. Lagrange, GeoSSE, BayArea, BioGeoBEARS) by modeling properties of dispersal barriers rather than areas, and by modeling the evolution of species lineages on a continuous landscape, rather than the evolution of geographic ranges along branches of a phylogeny. SEAMLESS shows how dispersal is required to maintain species richness and avoid clade-wide extinction, demonstrates that ancestral range size does not predict species richness, and provides a unified explanation for the suite of commonly observed biogeographic and phylogenetic patterns listed above. SEAMLESS explains how a simple barrier-displacement mechanism affects lineage diversification under neutral conditions, and is advanced here towards the formulation of a general theory of continental biogeography.

Systematic Biology↗
USGS2016

State-and-transition simulation models: a framework for forecasting landscape change

Summary A wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features. We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states. We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years. State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of landscape dynamics.

Methods in Ecology and Evolution↗
USGS2013

Ghost of habitat past: historic habitat affects the contemporary distribution of giant garter snakes in a modified landscape.

Historic habitat conditions can affect contemporary communities and populations, but most studies of historic habitat are based on the reduction in habitat extent or connectivity. Little is known about the effects of historic habitat on contemporary species distributions when historic habitat has been nearly completely removed, but species persist in a highly altered landscape. More than 93% of the historic wetlands in the Central Valley of California, USA, have been drained and converted to agricultural and other uses, but agricultural wetlands, such as rice and its supporting infrastructure of canals, allow some species to persist. Little is known about the distribution of giant garter snakes Thamnophis gigas , a rare aquatic snake species inhabiting this predominantly agricultural landscape, or the variables that affect where this species occurs. We used occupancy modeling to examine the distribution of giant garter snakes at the landscape scale in the Sacramento Valley (northern portion of the Central Valley) of California, with an emphasis on the relative strength of historic and contemporary variables (landscape-scale habitat, local microhabitat, vegetation composition and relative prey counts) for predicting giant garter snake occurrence. Proximity to historic marsh best explained variation in the probability of occurrence of giant garter snakes at the landscape scale, with greater probability of occurrence near historic marsh. We suspect that the importance of distance to historic marsh represents dispersal limitations of giant garter snakes. These results suggest that preserving and restoring areas near historic marsh, and minimizing activities that reduce the extent of marsh or marsh-like (e.g. rice agriculture, canal) habitats near historic marsh may be advantageous to giant garter snakes.

Animal Conservation↗
USGS2021

Functional connectivity in a continuously distributed, migratory species as revealed by landscape genomics

Maintaining functional connectivity is critical for the long-term conservation of wildlife populations. Landscape genomics provides an opportunity to assess long-term functional connectivity by relating environmental variables to spatial patterns of genomic variation resulting from generations of movement, dispersal and mating behaviors. Identifying landscape features associated with gene flow at large geographic scales for highly mobile species is becoming increasingly possible due to more accessible genomic approaches, improved analytical methods and enhanced computational power. We characterized the genetic structure and diversity of migratory mule deer Odocoileus hemionus using 4051 single nucleotide polymorphisms in 406 individuals sampled across multiple habitats throughout Wyoming, USA. We then identified environmental variables associated with genomic variation within genetic groups and statewide using a stepwise approach to first evaluate nonlinear relationships of landscape resistance with genetic distances and then use mixed-effects modeling to choose top landscape genomic models. We identified three admixed genetic groups of mule deer and found that environmental variables associated with gene flow varied among genetic groups, revealing scale-dependent and regional variation in functional connectivity. At the statewide scale, more gene flow occurred in areas with low elevation and mixed habitat. In the southern genetic group, more gene flow occurred in areas with low elevation. In the northern genetic group, more gene flow occurred in grassland and forest habitats, while highways and energy infrastructure reduced gene flow. In the western genetic group, the null model of isolation by distance best represented genetic patterns. Overall, our findings highlight the role of different seasonal ranges on mule deer genetic connectivity, and show that anthropogenic features hinder connectivity. This study demonstrates the value of combining a large, genome-wide marker set with recent advances in landscape genomics to evaluate functional connectivity in a wide-ranging migratory species.

Wyoming↗
USGS2016

Identification of landscape features influencing gene flow: How useful are habitat selection models?

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid-range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long-term genetic connectivity.

Evolutionary Applications↗
USGS2018

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

Functional connectivity, quantified using landscape genetics, can inform conservation through the identification of factors linking genetic structure to landscape mechanisms. We used breeding habitat metrics, landscape attributes, and indices of grouse abundance, to compare fit between structural connectivity and genetic differentiation within five long‐established Sage‐Grouse Management Zones (MZ) I‐V using microsatellite genotypes from 6,844 greater sage‐grouse ( Centrocercus urophasianus ) collected across their 10.7 million‐km 2 range. We estimated structural connectivity using a circuit theory‐based approach where we built resistance surfaces using thresholds dividing the landscape into “habitat” and “nonhabitat” and nodes were clusters of sage‐grouse leks (where feather samples were collected using noninvasive techniques). As hypothesized, MZ‐specific habitat metrics were the best predictors of differentiation. To our surprise, inclusion of grouse abundance‐corrected indices did not greatly improve model fit in most MZs. Functional connectivity of breeding habitat was reduced when probability of lek occurrence dropped below 0.25 (MZs I, IV) and 0.5 (II), thresholds lower than those previously identified as required for the formation of breeding leks, which suggests that individuals are willing to travel through undesirable habitat. The individual MZ landscape results suggested terrain roughness and steepness shaped functional connectivity across all MZs. Across respective MZs, sagebrush availability (<10%–30%; II, IV, V), tree canopy cover (>10%; I, II, IV), and cultivation (>25%; I, II, IV, V) each reduced movement beyond their respective thresholds. Model validations confirmed variation in predictive ability across MZs with top resistance surfaces better predicting gene flow than geographic distance alone, especially in cases of low and high differentiation among lek groups. The resultant resistance maps we produced spatially depict the strength and redundancy of range‐wide gene flow and can help direct conservation actions to maintain and restore functional connectivity for sage‐grouse.

Evolutionary Applications↗
USGS2016

Bioenergy production and forest landscape change in the southeastern United States

Production of woody biomass for bioenergy, whether wood pellets or liquid biofuels, has the potential to cause substantial landscape change and concomitant effects on forest ecosystems, but the landscape effects of alternative production scenarios have not been fully assessed. We simulated landscape change from 2010 to 2050 under five scenarios of woody biomass production for wood pellets and liquid biofuels in North Carolina, in the southeastern United States, a region that is a substantial producer of wood biomass for bioenergy and contains high biodiversity. Modeled scenarios varied biomass feedstocks, incorporating harvest of ‘conventional’ forests, which include naturally regenerating as well as planted forests that exist on the landscape even without bioenergy production, as well as purpose-grown woody crops grown on marginal lands. Results reveal trade-offs among scenarios in terms of overall forest area and the characteristics of the remaining forest in 2050. Meeting demand for biomass from conventional forests resulted in more total forest land compared with a baseline, business-as-usual scenario. However, the remaining forest was composed of more intensively managed forest and less of the bottomland hardwood and longleaf pine habitats that support biodiversity. Converting marginal forest to purpose-grown crops reduced forest area, but the remaining forest contained more of the critical habitats for biodiversity. Conversion of marginal agricultural lands to purpose-grown crops resulted in smaller differences from the baseline scenario in terms of forest area and the characteristics of remaining forest habitats. Each scenario affected the dominant type of land-use change in some regions, especially in the coastal plain that harbors high levels of biodiversity. Our results demonstrate the complex landscape effects of alternative bioenergy scenarios, highlight that the regions most likely to be affected by bioenergy production are also critical for biodiversity, and point to the challenges associated with evaluating bioenergy sustainability.

GCB Bioenergy↗
USGS2012

The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States

One of the pervasive challenges in landscape genetics is detecting gene flow patterns within continuous populations of highly mobile wildlife. Understanding population genetic structure within a continuous population can give insights into social structure, movement across the landscape and contact between populations, which influence ecological interactions, reproductive dynamics or pathogen transmission. We investigated the genetic structure of a large population of deer spanning the area of Wisconsin and Illinois, USA, affected by chronic wasting disease. We combined multiscale investigation, landscape genetic techniques and spatial statistical modelling to address the complex questions of landscape factors influencing population structure. We sampled over 2000 deer and used spatial autocorrelation and a spatial principal components analysis to describe the population genetic structure. We evaluated landscape effects on this pattern using a spatial autoregressive model within a model selection framework to test alternative hypotheses about gene flow. We found high levels of genetic connectivity, with gradients of variation across the large continuous population of white-tailed deer. At the fine scale, spatial clustering of related animals was correlated with the amount and arrangement of forested habitat. At the broader scale, impediments to dispersal were important to shaping genetic connectivity within the population. We found significant barrier effects of individual state and interstate highways and rivers. Our results offer an important understanding of deer biology and movement that will help inform the management of this species in an area where overabundance and disease spread are primary concerns.

Illinois, Wisconsin↗
USGS2021

Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model

Simulating long-term, landscape level changes in forest composition requires estimates of stand age to initialize succession models. Detailed stand ages are rarely available, and even general information on stand history often is lacking. We used data from USDA Forest Service Forest Inventory and Analysis (FIA) database to estimate broad age classes for a forested landscape to simulate changes in landscape composition and structure relative to climate change at Fort Drum, a 43,000 ha U.S. Army installation in northwestern New York. Using simple linear regression, we developed relationships between tree diameter and age for FIA site trees from the host and adjacent ecoregions and applied those relationships to forest stands at Fort Drum. We observed that approximately half of the variation in age was explained by diameter breast height (DBH) across all species studied ( r 2 = 0.42 for sugar maple Acer saccharum to 0.63 for white ash Fraxinus americana ). We then used age-diameter relationships from published research on northern hardwood species to calibrate results from the FIA-based analysis. With predicted stand age, we used tree species life histories and environmental conditions represented by ecological site types to parameterize a stochastic forest landscape model (LANDIS-II) to spatially and temporally model successional changes in forest communities at Fort Drum. Forest stands modeled over 100 years without significant disturbance appeared to reflect expected patterns of increasing dominance by shade-tolerant mesophytic tree species such as sugar maple, red maple ( Acer rubrum ), and eastern hemlock ( Tsuga canadensis ) where soil moisture was sufficient. On drier sandy soils, eastern white pine ( Pinus strobus ), red pine ( P. resinosa ), northern red oak ( Quercus rubra ), and white oak ( Q. alba ) continued to be important components throughout the modeling period with no net loss at the landscape scale. Our results suggest that despite abundant precipitation and relatively low evapotranspiration rates for the region, low soil water holding capacity and fertility may be limiting factors for the spread of mesophytic species on excessively drained soils in the region. Increasing atmospheric temperatures projected for the region could alter moisture regimes for many coarse-textured soils providing a possible mechanism for expansion of xerophytic tree species.

New York↗
USGS2018

Landscape connectivity for bobcat (Lynx rufus) and lynx (Lynx canadensis) in the Northeastern United States

Landscape connectivity is integral to the persistence of metapopulations of wide ranging carnivores and other terrestrial species. The objectives of this research were to investigate the landscape characteristics essential to use of areas by lynx and bobcats in northern New England, map a habitat availability model for each species, and explore connectivity across areas of the region likely to experience future development pressure. A Mahalanobis distance analysis was conducted on location data collected between 2005 and 2010 from 16 bobcats in western Vermont and 31 lynx in northern Maine to determine which variables were most consistent across all locations for each species using three scales based on average 1) local (15 minute) movement, 2) linear distance between daily locations, and 3) female home range size. The bobcat model providing the widest separation between used locations and random study area locations suggests that they cue into landscape features such as edge, availability of cover, and development density at different scales. The lynx model with the widest separation between random and used locations contained five variables including natural habitat, cover, and elevation—all at different scales. Shrub scrub habitat—where lynx’s preferred prey is most abundant—was represented at the daily distance moved scale. Cross validation indicated that outliers had little effect on models for either species. A habitat suitability value was calculated for each 30 m 2 pixel across Vermont, New Hampshire, and Maine for each species and used to map connectivity between conserved lands within selected areas across the region. Projections of future landscape change illustrated potential impacts of anthropogenic development on areas lynx and bobcat may use, and indicated where connectivity for bobcats and lynx may be lost. These projections provided a guide for conservation of landscape permeability for lynx, bobcat, and species relying on similar habitats in the region.

PLoS ONE↗
USGS2018

Weather and landscape factors affect white-tailed deer neonate survival at ecologically important life stages in the Northern Great Plains

Offspring survival is generally more variable than adult survival and may limit population growth. Although white-tailed deer neonate survival has been intensively investigated, recent work has emphasized how specific cover types influence neonate survival at local scales (single study area). These localized investigations have often led to inconsistences within the literature. Developing specific hypotheses describing the relationships among environmental, habitat, and landscape factors influencing white-tailed deer neonate survival at regional scales may allow for detection of generalized patterns. Therefore, we developed 11 hypotheses representing the various effects of environmental (e.g., winter and spring weather), habitat (e.g., hiding and escape cover types), and landscape factors (e.g., landscape configuration regardless of specific cover type available) on white-tailed deer neonate survival up to one-month and from one- to three-months of age. At one-month, surviving fawns experienced a warmer lowest recorded June temperature and more June precipitation than those that perished. At three-months, patch connectance (percent of patches of the corresponding patch type that are connected within a predefined distance) positively influenced survival. Our results are consistent with white-tailed deer neonate ecology: increased spring temperature and precipitation are likely associated with a flush of nutritional resources available to the mother, promoting increased lactation efficiency and neonate growth early in life. In contrast, reduced spring temperature with increased precipitation place neonates at risk to hypothermia. Increased patch connectance likely reflects increased escape cover available within a neonate’s home range after they are able to flee from predators. If suitable escape cover is available on the landscape, then managers could focus efforts towards manipulating landscape configuration (patch connectance) to promote increased neonate survival while monitoring spring weather to assess potential influences on current year survival.

Minnesota, North Dakota, South Dakota↗
USGS2018

Isolation by distance versus landscape resistance: Understanding dominant patterns of genetic structure in Northern Spotted Owls (Strix occidentalis caurina)

Landscape genetics investigations examine how the availability and configuration of habitat influence genetic structure of plants and animals. We used landscape genetics to evaluate the role that forest connectivity plays in determining genetic structure of the federally-threatened Northern Spotted Owl ( Strix occidentalis caurina ) using genotypes of 339 Northern Spotted Owls obtained for 10 microsatellite loci. Spatial clustering analyses identified a distinct genetic cluster at the southern extent of the region examined. This cluster could not be linked to landscape connectivity patterns and suggested that post-Pleistocene processes were involved with its development rather than contemporary landscape configuration. We also compared matrices of pairwise inter-individual genetic distances with resistance distances derived from a circuit-theory based framework. Resistance distances were obtained for an idealized raster map that reflected continuous unimpeded dispersal habitat across the landscape along with five empirically-derived raster maps reflecting the 1870’s, 1940’s, 1986, 1994, and 2012. Resistance distances from the idealized map served as surrogates for linear geographic distances. Relative to idealized conditions, resistance distances were ~250% higher in the 1940’s and ~200% higher from 1986 onward. Resistance distances from the 1870’s were ~40% higher than idealized conditions. Inter-individual genetic distances were most highly correlated with resistance distances from the idealized map rather than any of the empirical maps. Two hypotheses explain our results. First, our results may reflect temporal lags between the onset of large-scale habitat alterations and their novel effects on genetic structure in long-lived species such as Northern Spotted Owls. Second, because Northern Spotted Owls disperse over long distances, our results may indicate that forest habitat has never been sufficiently fragmented to the point where connectivity was disrupted. The second hypothesis could indicate that forest management practices mandated by the Northwest Forest Plan succeeded with one of its primary goals. However, our results do not represent a complete portrayal of the status of Northern Spotted Owls given detection of significant population declines and bottlenecks in other studies. Future investigations based on computer simulations may help distinguish between hypotheses.

California, Oregon, Washington↗
USGS2019

Applying circuit theory and landscape linkage maps to reintroduction planning for California condors

Conservation practitioners are increasingly looking to species translocations as a tool to recover imperiled taxa. Quantitative predictions of where animals are likely to move when released into new areas would allow managers to better address the social, institutional, and ecological dimensions of conservation translocations. Using >5 million California condor ( Gymnogyps californianus ) occurrence locations from 75 individuals, we developed and tested circuit-based models to predict condor movement away from release sites. We found that circuit-based models of electrical current were well calibrated to the distribution of condor movement data in southern and central California (continuous Boyce Index = 0.86 and 0.98, respectively). Model calibration was improved in southern California when additional nodes were added to the circuit to account for nesting and feeding areas, where condor movement densities were higher (continuous Boyce Index = 0.95). Circuit-based projections of electrical current around a proposed release site in northern California comported with the condor’s historical distribution and revealed that, initially, condor movements would likely be most concentrated in northwestern California and southwest Oregon. Landscape linkage maps, which incorporate information on landscape resistance, complement circuit-based models and aid in the identification of specific avenues for population connectivity or areas where movement between populations may be constrained. We found landscape linkages in the Coast Range and the Sierra Nevada provided the most connectivity to a proposed reintroduction site in northern California. Our methods are applicable to conservation translocations for other species and are flexible, allowing researchers to develop multiple competing hypotheses when there are uncertainties about landscape or social attractants, or uncertainties in the landscape conductance surface.

Central California↗
USGS2011

Influence of landscape-scale factors in limiting brook trout populations in Pennsylvania streams

Landscapes influence the capacity of streams to produce trout through their effect on water chemistry and other factors at the reach scale. Trout abundance also fluctuates over time; thus, to thoroughly understand how spatial factors at landscape scales affect trout populations, one must assess the changes in populations over time to provide a context for interpreting the importance of spatial factors. We used data from the Pennsylvania Fish and Boat Commission's fisheries management database to investigate spatial factors that affect the capacity of streams to support brook trout Salvelinus fontinalis and to provide models useful for their management. We assessed the relative importance of spatial and temporal variation by calculating variance components and comparing relative standard errors for spatial and temporal variation. We used binary logistic regression to predict the presence of harvestable-length brook trout and multiple linear regression to assess the mechanistic links between landscapes and trout populations and to predict population density. The variance in trout density among streams was equal to or greater than the temporal variation for several streams, indicating that differences among sites affect population density. Logistic regression models correctly predicted the absence of harvestable-length brook trout in 60% of validation samples. The r 2-value for the linear regression model predicting density was 0.3, indicating low predictive ability. Both logistic and linear regression models supported buffering capacity against acid episodes as an important mechanistic link between landscapes and trout populations. Although our models fail to predict trout densities precisely, their success at elucidating the mechanistic links between landscapes and trout populations, in concert with the importance of spatial variation, increases our understanding of factors affecting brook trout abundance and will help managers and private groups to protect and enhance populations of wild brook trout. ?? Copyright by the American Fisheries Society 2006.

Transactions of the American Fisheries Society↗