Search USGS⌕ Search

SEARCH · Search USGS

Results for “Modeling and Using Context”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources
USGS2018

Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus

Multi-state occupancy modeling can often improve assessments of habitat use and site quality when animal activity or behavior data are available. We examine the use of the approach for evaluating foraging habitat suitability of the endangered Hawaiian hoary bat ( Lasiurus cinereus semotus ) from classifications of site occupancy based on flight activity levels and feeding behavior. In addition, we used data from separate visual and auditory sources, namely thermal videography and acoustic (echolocation) detectors, jointly deployed at sample sites to compare the effectiveness of each method in the context of occupancy modeling. Video-derived observations demonstrated higher and more accurate estimates of the prevalence of high bat flight activity and feeding events than acoustic sampling methods. Elevated levels of acoustic activity by Hawaiian hoary bats were found to be related primarily to beetle biomass in this study. The approach may have a variety of applications in bat research, including inference about species-resource relationships, habitat quality and the extent to which species intensively use areas for activities such as foraging.

PLoS ONE↗
USGS2024

Leveraging relationships between species abundances to improve predictions and inform conservation

Many management and conservation contexts can benefit from understanding relationships between species abundances, which can be used to improve predictions of species occurrence and abundance. We present conditional prediction as a tool to capture information about species abundances via residual covariance between species. From a fitted joint species distribution model, this framework produces a species coefficient matrix that contains relationships between species abundances. The species coefficients allow co-observed species to be treated as a second set of predictors supplementing covariates in the model to improve prediction. We use simulations to demonstrate the potential benefits and limitations of conditional prediction across data types and species covariance before applying conditional prediction to two management contexts with real data. Simulations demonstrate that conditional prediction provides the largest benefits to continuous data and when there is residual covariance between many species. In our first application, we show that conditioning on other species improves in-sample and out-of-sample predictions of fish and invertebrate species, including Atlantic cod. In our second application, we show that the species coefficient matrix can be used to identify bird species at risk of nest parasitism by Brown-headed Cowbirds. Synthesis and applications . We present guidelines for using conditional prediction, which can help understand relationships between species abundances, improve predictions and inform conservation in a variety of contexts.

Journal of Applied Ecology↗
USGS2013

Wetland dynamics influence mid-continent duck recruitment

Recruitment is a key factor influencing duck population dynamics. Understanding what regulates recruitment of ducks is a prerequisite to informed habitat and harvest management. Quantity of May ponds (MP) has been linked to recruitment and population size (Kaminski and Gluesing 1987, Raveling and Heitmeyer 1989). However, wetland productivity (quality) is driven by inter-annual hydrological fluctuations. Periodic drying of wetlands due to wet-dry climate cycles releases nutrients and increases invertebrate populations when wet conditions return (Euliss et al. 1999). Wetlands may also become wet or dry within a breeding season. Accordingly, inter-annual and intra-seasonal hydrologic variation potentially influence duck recruitment. Here, we examined influences of wetland quantity, quality, and intra-seasonal dynamics on recruitment of ducks. We indexed duck recruitment by vulnerability-corrected age ratios (juveniles/adult females) for mid-continent Gadwall (Anas strepera). We chose Gadwall because the majority of the continental population breeds in the Prairie Pothole Region (PPR), where annual estimates of MP exist since 1974. We indexed wetland quality by calculating change in MP (?MP) over the past two years (?MP = 0.6[MPt – MPt-1] + 0.4[MPt – MPt-2]). We indexed intra-seasonal change in number of ponds by dividing the PPR mean standardized precipitation index for July by MP (hereafter summer index). MP and ?MP were positively correlated (r = 0.65); therefore, we calculated residual ?MP (?MPr) with a simple linear regression using MP, creating orthogonal variables. Finally, we conducted a multiple regression to examine how MP, ?MPr, and summer index explained variation in recruitment of Gadwall from 1976–2010. Our model explained 67% of the variation in mid-continent Gadwall recruitment and all three hydrologic indices were positively correlated with recruitment (Figure 1). Type II semi-partial R2 estimates indicated that MP accounted for 41%, ?MPr accounted for an additional 22%, and summer index accounted for the remaining 4% of the variation in recruitment. Our results are consistent with previous findings that quantity of MP was important for explaining variation in recruitment of ducks. However, our results also indicated that considering hydrologic dynamics was important for explaining recruitment. Additionally, the index for retention of MP within breeding year also was important, despite its coarse resolution as an average of precipitation events that can vary greatly spatially and in intensity within the PPR. Our results support the idea that wetland ecosystems in the PPR are ultimately regulated through bottom-up process driven by inter- and intra-annual hydrological dynamics. However from the ducks' perspective, hydrological dynamics could influence recruitment proximately through both bottom-up and top-down processes. Specifically, hydrological fluctuations may influence predator populations, prey switching by predators, or duckling vulnerability to predators (Cox et al. 1998). We will propose a conceptual model for understanding the potential role of bottom-up and top-down regulation of duck recruitment based on different hydrological contexts. Clearly, a better understanding of ultimate and proximate factors regulating duck recruitment would improve the effectiveness and efficiency of habitat conservation for ducks. Lastly, our findings could be used to improve models that predict fall flights for the purposes of informing harvest regulations.

Conference Paper↗
USGS2006

Scaling local species-habitat relations to the larger landscape with a hierarchical spatial count model

Much of what is known about avian species-habitat relations has been derived from studies of birds at local scales. It is entirely unclear whether the relations observed at these scales translate to the larger landscape in a predictable linear fashion. We derived habitat models and mapped predicted abundances for three forest bird species of eastern North America using bird counts, environmental variables, and hierarchical models applied at three spatial scales. Our purpose was to understand habitat associations at multiple spatial scales and create predictive abundance maps for purposes of conservation planning at a landscape scale given the constraint that the variables used in this exercise were derived from local-level studies. Our models indicated a substantial influence of landscape context for all species, many of which were counter to reported associations at finer spatial extents. We found land cover composition provided the greatest contribution to the relative explained variance in counts for all three species; spatial structure was second in importance. No single spatial scale dominated any model, indicating that these species are responding to factors at multiple spatial scales. For purposes of conservation planning, areas of predicted high abundance should be investigated to evaluate the conservation potential of the landscape in their general vicinity. In addition, the models and spatial patterns of abundance among species suggest locations where conservation actions may benefit more than one species. ?? 2006 Springer Science+Business Media B.V.

Landscape Ecology↗
USGS2019

Spatial patterns of meadow sensitivities to interannual climate variability in the Sierra Nevada

Conservation of montane meadows is a high priority for land and water managers given their critical role in buffering the effects of climate variability and their vulnerability to increasing temperatures and evaporative demands. Recent advances in cloud computing have provided new opportunities to examine ecological responses to climate variability over the past few decades, and at large spatial scales. In this study we characterized the sensitivities (magnitude and direction of the slope) of meadow vegetation responses to interannual variations in climate. We calculated sensitivity as the regression slope between a 35-year (1985-2016) time series of Landsat-derived vegetation indices characterizing late-season vegetation vigor and water balance variables from the Basin Characterization Model. We identified April 1 snowpack as the climate variable the majority of meadows were most sensitive to. We assessed how vegetation sensitivities to snowpack varied with hydrogeomorphic context (e.g., climate, geology, soils, watershed geometry and land cover) across the Sierra Nevada mountain range using factor analysis to reduce the dimensionality of the hydrogeomorphic data, and multiple linear regression to model sensitivity responses. We found that meadow sensitivities to snowpack varied with long-term average meadow climate, indicators of watershed subsurface water storage capacity, and indicators of meadow vegetation composition. Alpine and sub-alpine meadows with high average annual precipitation, but limited catchment subsurface storage exhibited the largest sensitivities. Our results provide a novel regional perspective on spatial patterns of meadow sensitivities to climate variability and the landscape-scale hydrogeomorphic factors that influence late-season water availability in meadow ecosystems in the Sierra Nevada.

Sierra Nevada↗
USGS2015

Landscape structure affects specialists but not generalists in naturally fragmented grasslands

Understanding how biotic communities respond to landscape spatial structure is critically important for conservation management as natural landscapes become increasingly fragmented. However, empirical studies of the effects of spatial structure on plant species richness have found inconsistent results, suggesting that more comprehensive approaches are needed. In this study, we asked how landscape structure affects total plant species richness and the richness of a guild of specialized plants in a multivariate context. We sampled herbaceous plant communities at 56 dolomite glades (insular, fire-adapted grasslands) across the Missouri Ozarks, and used structural equation modeling (SEM) to analyze the relative importance of landscape structure, soil resource availability, and fire history for plant communities. We found that landscape spatial structure-defined as the area-weighted proximity of glade habitat surrounding study sites (proximity index)-had a significant effect on total plant species richness, but only after we controlled for environmental covariates. Richness of specialist species, but not generalists, was positively related to landscape spatial structure. Our results highlight that local environmental filters must be considered to understand the influence of landscape structure on communities, and that unique species guilds may respond differently to landscape structure than the community as a whole. These findings suggest that both local environment and landscape context should be considered when developing management strategies for species of conservation concern in fragmented habitats.

Ecology↗
USGS2011

Incorporating parametric uncertainty into population viability analysis models

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

Biological Conservation↗
USGS2025

Update to a management-focused population viability analysis for North Atlantic right whales

We provide an update to the recently published population viability analysis for North Atlantic right whales ( Eubalaena glacialis ). The update includes improvements to the reproduction modeling and also shares additional context given evidence of reduced mortality indicated by recent population monitoring. Projections from the analysis are used to quantify simulated population sizes across 100 years and resulting quasi-extinction probabilities (falling below 50 mature females that have proven ability to reproduce) to compare hypothetical scenarios related to management of threats and changing environmental conditions. Under a status quo scenario reflecting conditions of 2019, prior to the enactment of new regulations by the U.S. and Canada, the North Atlantic right whale population would be expected to continue to fall, with a median decline of 88 percent (95% projection interval, –98 percent to –45 percent change) and a probability of falling below 50 proven females (i.e., quasi-extinction) of 0.988 at 100 years. In hypothetical scenarios that fully remove each of the three primary threats to right whales one at a time, removal of the entanglement threat alone reduces the probability of falling below 50 proven females in 100 years to 0.070; removal of the vessel strike threat alone reduces it to 0.522; and a return to historical prey abundance patterns (pre-2010), but with both human-related threats still in place, reduces it to 0.524. Although additional baseline scenarios were explored to examine the potential effects of recent regulations, we found that the most up-to-date mortality rates (2020–2022) are similar to those simulated under a 70% reduction in severe entanglement injury compared to rates estimated during 2013–2019. If management measures implemented in the U.S. and Canada continue to reduce mortality, the estimated probability of falling below 50 proven females in 100 years is 0.234. Our model continues to provide a tool for assessing North Atlantic right whale recovery.

NOAA Technical Memorandum↗
USGS2014

Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km 2 ) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.

Water Resources Research↗
USGS2020

Using information from global climate models to inform policymaking—The role of the U.S. Geological Survey

This report provides an overview of model-based climate science in a risk management context. In addition, it summarizes how the U.S. Geological Survey (USGS) will continue to follow best scientific practices and when and how the results of this research will be delivered to the U.S. Department of the Interior (DOI) and other stakeholders to inform policymaking. Climate change is a risk management challenge for society because of the uncertain consequences for natural and human systems across decades to centuries. Climate-related science activities within the USGS emphasize research on adaptation to climate change. This research helps inform adaptive management processes and planning activities within other DOI bureaus and by DOI stakeholders. Global climate models are sophisticated numerical representations of the Earth’s climate system. Research groups from around the world regularly participate in a coordinated effort to produce a suite of climate models. This global effort provides a test bed to assess model performance and analyze projections of future change under various prescribed climate scenarios. These climate scenarios describe a plausible future outcome associated with a specific set of societal actions. Because scenarios are developed in a risk-based framework with a high degree of uncertainty about future societal developments, they are usually not assigned a formal likelihood of occurrence. Examining a range of projected climate outcomes based on multiple scenarios is a recommended best practice because it allows decision makers to better consider both short- and long-term risks and opportunities. As part of its routine science practices, the USGS regularly reviews the state of knowledge of climate science, develops and maintains best practices in using global climate models to project climate change impacts, and provides data and interpretations of potential impacts to the DOI and other stakeholders. Management and policy decisions within the DOI will reflect different tolerances for risk, which has implications for what type of information should be considered and how that information should be used. It is suggested that a followup document be produced that would describe in more detail how these management decisions with differing risk tolerances can be made effectively and consistently in light of an uncertain future.

Open-File Report↗
USGS2025

A partner-driven decision support model to inform the reintroduction of bull trout

Assessments of species reintroductions involve a series of complex decisions that include human perspectives and ecological contexts. Here, we present a reintroduction assessment involving bull trout ( Salvelinus confluentus ) using a structured decision-making process. We approached this assessment by engaging partners representing public utilities, government agencies, and Tribes with shared interests in a potential reintroduction. These individuals identified objectives, decision alternatives, and ecological scenarios that were incorporated into a co-produced simulation-based model of potential reintroduction outcomes. The model included mathematical representations of habitat availability, life history expression, and assumptions regarding constraints on potential bull trout populations. Within each recipient stream, partners chose to explore a wide range of decision alternatives and simulated scenarios affecting reintroduction success. Results suggested that 1) reintroductions using eggs or adults were most optimal, 2) adding more individuals resulted in diminishing returns, 3) access to migratory habitat could improve success, and 4) the diversity of opportunities for life history expression led to improved reintroduction opportunities. In addition, modeled scenarios indicated some recipient streams consistently produced lower abundance of reintroduced bull trout. This work contributes a novel example to a growing portfolio of reintroduction assessments that may inform future conservation for bull trout and many other species facing similar challenges.

Washington↗
USGS2025

Modeling diverse environmental responses of reservoirs to floating photovoltaic systems

Floating photovoltaic (FPV) systems are emerging as a promising strategy for large-scale clean energy production worldwide. However, by altering key physical drivers such as solar radiation and wind mixing, FPV installations may have also unintended consequences for lakes and reservoirs. Given the wide diversity of freshwater systems globally, understanding the consistency in direction and magnitude of environmental responses to FPV deployment is critical for informed regulatory oversight and sustainable energy development. Here, we used process-based models to simulate the effects of FPV coverage on 11 reservoirs across the United States. This is the first multi-reservoir analysis using a laterally averaged 2D process-based modeling framework to systematically evaluate FPV impacts across diverse climatic and morphometric contexts, enabling direct comparison of magnitude and direction of responses among systems. Specifically, we evaluated changes in (1) surface and outflow temperature, (2) thermocline depth, (3) water column stability, (4) dissolved oxygen concentrations, and (5) potential suitable habitat availability for warm- and cold-water fishes. We quantified changes in these response variables by an iterative approach that simulates increases in FPV coverage and compares them with reference conditions. We summarized responses for winter (January–February) and summer (July–August). As expected, our simulations show that increasing FPV coverage consistently cooled surface waters and altered thermal stratification patterns, but the magnitude and environmental implications of these changes varied among reservoirs. Notably, greater FPV coverage led to increased variability in habitat suitability for aquatic species, with some reservoirs exhibiting distinct and sometimes divergent responses. These findings underscore the importance of considering local environmental contexts when assessing FPV impacts. While large-scale FPV systems offer potential benefits for climate mitigation, their ecological effects, particularly on thermally sensitive biota, require careful site-specific evaluation to avoid unintended consequences to local freshwater biodiversity.

Arkansas, Idaho, Ohio, Oregon, Tennessee, Washingt↗
USGS2020

Animal movement models with mechanistic selection functions

A suite of statistical methods are used to study animal movement. Most of these methods treat animal trajectory data in one of three ways: as discrete pro- cesses, as continuous processes, or as point processes. We brie y review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statis- tically treat animal trajectory data. However, most resource selection analyses provide inference based on approximations of point process models. The forms of these models have been limited to a few types of specications that provide infer- ence about relative resource use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis often proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specications related to resource selection. We then provide an example of a case where a spatio-temporal point process model coincides with that implied by a mechanistic model for movement expressed as a partial dier- ential equation derived from rst principles of movement. We demonstrate that inference from this form of point process model is intuitive (and could be useful for management and conservation) by analyzing a set of telemetry data from a mountain lion in Colorado, USA, to understand the eects of spatially explicit environmental conditions on movement behavior of this species.

Spatial Statistics↗
USGS2023

Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps

Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task. The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock. We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.

Delaware, New Jersey, New York, Pennsylvania↗
USGS2026

Advances in volcano monitoring driven by the first decade of Sentinel-1 observations

Sentinel-1 has transformed how satellite radar data (SAR and InSAR) are used in volcanology. The systematic, long-term archive and open-access policy means that volcano observatories and research organisations have invested in integrating Sentinel-1 datasets into their monitoring systems. We identify 233 high priority volcanoes and estimate that Sentinel-1 data has been used in peer-reviewed publications for 90 of them. We examine a global archive of 3.3 million automatically processed Sentinel-1 interferograms of volcanoes and use machine learning methods to identify eruptions and periods of unrest. We then review the ways in which InSAR data are being used in different contexts. At frequently erupting basaltic systems in Iceland, Hawaiʻi, the Galápagos , and Piton de la Fournaise, InSAR has become an effective monitoring tool and is integrated with other datasets and models to forecast magma pathways. For large explosive eruptions, deformation measurements often remain challenging, but SAR backscatter is increasingly used to map damaging flows and measure the changing shape of ocean islands. Sentinel-1's long archive provides critical baseline measurements that are vital for measuring slow deformation, capturing new periods of unrest and providing fresh insights into subsurface dynamics. Understanding the drivers of deformation remains challenging and typically relies on integration with external datasets. Future European Space Agency missions have the potential to improve both resolution and coverage providing an even richer dataset to further enhance global volcano monitoring

Erta Ale volcano↗
USGS2020

The changing sociocultural context of wildlife conservation

We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017–2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves ( Canis lupus ) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.

Conservation Biology↗
USGS2016

Spatially explicit models of full-season productivity and implications for landscape management of Golden-winged Warblers in the western Great Lakes Region

The relationship between landscape structure and composition and full-season productivity (FSP) is poorly understood for most birds. For species of high conservation concern, insight into how productivity is related to landscape structure and composition can be used to develop more effective conservation strategies that increase recruitment. We monitored nest productivity and fledgling survival of Golden-winged Warblers ( Vermivora chrysoptera ), a species of high conservation concern, in managed forest landscapes at two sites in northern Minnesota, and one site in southeastern Manitoba, Canada from 2010 to 2012. We used logistic exposure models to identify the influence of landscape structure and composition on nest productivity and fledgling survival. We used the models to predict spatially explicit, FSP across our study sites to identify areas of low relative productivity that could be targeted for management. We then used our models of spatially explicit, FSP to simulate the impact of potential management actions on our study sites with the goal of increasing total population productivity. Unlike previous studies that suggested wetland cover types provide higher quality breeding habitat for Golden-winged Warblers, our models predicted 14% greater productivity in upland cover types. Simulated succession of a 9-ha grassland patch to a shrubby upland suitable for nesting increased the total number of fledglings produced by that patch and adjacent upland shrublands by 30%, despite decreasing individual productivity by 13%. Further simulated succession of the same patch described above into deciduous forest reduced the total number of fledglings produced to independence on a landscape by 18% because of a decrease in the area available for nesting. Simulated reduction in the cumulative length of shrubby edge within a 50-m radius of any location in our landscapes from 0.6 to 0.3 km increased FSP by 5%. Our models demonstrated that the effects of any single management action depended on the context of the surrounding landscape. We conclude that spatially explicit, FSP models that incorporate data from both the nesting and postfledging periods are useful for informing breeding habitat management plans for Golden-winged Warblers and that similar models can benefit management planning for many other species of conservation concern.

Studies in Avian Biology↗
USGS2011

Social network models predict movement and connectivity in ecological landscapes

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Proceedings of the National Academy of Sciences of↗