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At least 1,495 records · Page 83Linked to original sources
USGS2018

Modeling the compensatory response of an invasive tree to specialist insect herbivory

The severity of the effects of herbivory on plant fitness can be moderated by the ability of plants to compensate for biomass loss. Compensation is an important component of the ecological fitness in many plants, and has been shown to reduce the effects of pests on agricultural plant yields. It can also reduce the effectiveness of biocontrol through introduced herbivores in controlling weedy invasive plants. This study used a modeling approach to predict the effect of different levels of foliage herbivory by biological control agents introduced to control the invasive tree Melaleuca quinquennervia (melaleuca) in Florida. It is assumed in the model that melaleuca can optimally change its carbon and nitrogen allocation strategies in order to compensate for the effects of herbivory. The model includes reallocation of more resources to production and maintenance of photosynthetic tissues at the expense of roots. This compensation is shown to buffer the severity of the defoliation effect, but the model predicts a limit on the maximum herbivory that melaleuca can tolerate and survive. The model also shows that the level of available limiting nutrient (e.g., soil nitrogen) may play an important role in a melaleuca’s ability to compensate for herbivory. This study has management implications for the best ways to maximize the level of damage using biological control or other means of defoliation.

Florida↗
USGS2019

Overview of emerging amphibian pathogens and modeling advances for conservation-related decisions

One of the leading causes of global amphibian decline is emerging infectious disease. We summarize the disease ecology of four major emerging amphibian infectious agents: chytrids, ranaviruses, trematodes, and Perkinsea. We focus on recently developed quantitative advances that build on well-established ecological theories and aid in studying epizootic and enzootic disease dynamics. For example, we identify ecological and evolutionary selective forces that determine disease outcomes and transmission pathways by borrowing ideas from population and community ecology theory. We outline three topics of general interest in disease ecology: (i) the relationship between biodiversity and disease risk, (ii) individual, species, or environmental transmission heterogeneity, and (iii) pathogen coinfections. Finally, we identify specific knowledge gaps impeding the success of conservation-related decisions for disease mitigation and the future of amphibian conservation success.

Biological Conservation↗
USGS2018

Doublethink and scale mismatch polarize policies for an invasive tree

Mismatches between invasive species management policies and ecological knowledge can lead to profound societal consequences. For this reason, natural resource agencies have adopted the scientifically-based density-impact invasive species curve to guide invasive species management. We use the density-impact model to evaluate how well management policies for a native invader ( Juniperus virginiana ) match scientific guidelines. Juniperus virginiana invasion is causing a sub-continental regime shift from grasslands to woodlands in central North America, and its impacts span collapses in endemic diversity, heightened wildfire risk, and crashes in grazing land profitability. We (1) use land cover data to identify the stage of Juniperus virginiana invasion for three ecoregions within Nebraska, USA, (2) determine the range of invasion stages at individual land parcel extents within each ecoregion based on the density-impact model, and (3) determine policy alignment and mismatches relative to the density-impact model in order to assess their potential to meet sustainability targets and avoid societal impacts as Juniperus virginiana abundance increases. We found that nearly all policies evidenced doublethink and policy-ecology mismatches, for instance, promoting spread of Juniperus virginiana regardless of invasion stage while simultaneously managing it as a native invader in the same ecoregion. Like other invasive species, theory and literature for this native invader indicate that the consequences of invasion are unlikely to be prevented if policies fail to prioritize management at incipient invasion stages. Theory suggests a more realistic approach would be to align policy with the stage of invasion at local and ecoregion management scales. There is a need for scientists, policy makers, and ecosystem managers to move past ideologies governing native versus non-native invader classification and toward a framework that accounts for the uniqueness of native species invasions, their anthropogenic drivers, and their impacts on ecosystem services.

Nebraska↗
USGS2021

Metamorphosis in an era of increasing climate variability

Most animals have complex life cycles including metamorphosis or other discrete life stage transitions during which individuals may be particularly vulnerable to environmental stressors. With climate change, individuals will be exposed to increasing thermal and hydrologic variability during metamorphosis, which may affect survival and performance through physiological, behavioral, and ecological mechanisms. Furthermore, because metamorphosis entails changes in traits and vital rates, it is likely to play an important role in how populations respond to increasing climate variability. To identify mechanisms underlying population responses and associated trait and life history evolution, we need new approaches to estimating changes in individual traits and performance throughout metamorphosis, and we need to integrate metamorphosis as an explicit life stage in analytical models.

Trends in Ecology & Evolution↗
USGS2021

Ecology of an isolated muskrat population during regional population declines

Evidence indicating a decline in muskrat populations in the United States during the past 40 years has led to speculation regarding factors influencing muskrat survival. In order to understand population dynamics and survival, it is important to first define the ecology of local populations. We investigated the dwelling structure use, movements, home range, and survival of radio-tagged muskrats (n = 14) in an urban wetland complex in central Pennsylvania. We used locations collected from intensive radio telemetry monitoring to determine number of lodging structures used, hourly movement, and size and percent area overlap of home ranges. Muskrats shared an average of nine lodging structures and on average 68% of a muskrat’s home range overlapped other muskrat home ranges. We used four home range estimators (Kernel Density Estimator (KDE) href, KDEad hoc, KDEplug-in, and Local Convex Hull estimator) to assess the ability of each estimator to represent muskrat home ranges. The KDEplug-in that constrained the estimate of home range to habitat boundaries provided the more appropriate home range size for muskrats in a linear-non-linear habitat matrix. We also calculated overwinter survival estimates using known-fate models. Our top model indicated a positive effect of the average weekly precipitation on survival with an overwinter survival estimate of 0.59 (SE = 0.16). The main cause of muskrat mortality was predation by mink (n = 6). The small sample size and uncertainty surrounding our model selection led to weak estimates of survival, however our model suggests that snowfall may be an important factor in muskrat survival. Our study provides novel data on muskrat ecology in Pennsylvania as well as preliminary evidence for future investigations of factors affecting muskrat survival during the winter months.

Pennsylvania↗
USGS2025

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects

Machine learning (ML) models are often based on complex black-box architectures that are difficult to interpret. This interpretability problem can hinder the use of ML in fields like medicine, ecology, and insurance, and has boosted research in interpretable machine learning (IML). Here, we propose a novel approach for the functional decomposition of black-box predictions, which is a core concept of IML. This approach replaces the prediction function with a surrogate model consisting of simpler subfunctions, providing insights into the direction and strength of the main feature contributions and their interactions. Our method is based on a concept termed “stacked orthogonality”, which ensures that the main effects capture as much functional behavior as possible. To compute the subfunctions, we combine neural additive modeling with an efficient post-hoc orthogonalization procedure. Our method yielded plausible results in an analysis of stream biological condition in the Chesapeake Bay watershed (United States).

Chesapeake Bay watershed↗
USGS2002

Modeling and measuring snow for assessing climate change impacts in Glacier National Park, Montana

A 12-year program of global change research at Glacier National Park by the U.S. Geological Survey and numerous collaborators has made progress in quantifying the role of snow as a driver of mountain ecosystem processes. Spatially extensive snow surveys during the annual accumulation/ablation cycle covered two mountain watersheds and approximately 1,000 km2 . Over 7,000 snow depth and snow water equivalent (SWE) measurements have been made through spring 2002. These augment two SNOTEL sites, 9 NRCS snow courses, and approximately 150 snow pit analyses. Snow data were used to establish spatially-explicit interannual variability in snowpack SWE. East of the Continental Divide, snowpack SWE was lower but also less variable than west of the Divide. Analysis of snowpacks suggest downward trends in SWE, a reduction in snow cover duration, and earlier melt-out dates during the past 52 years. Concurrently, high elevation forests and treelines have responded with increased growth. However, the 80 year record of snow from 3 NRCS snow courses reflects a strong influence from the Pacific Decadal Oscillation, resulting in 20-30 year phases of greater or lesser mean SWE. Coupled with the fine-resolution spatial snow data from the two watersheds, the ecological consequences of changes in snowpack can be empirically assessed at a habitat patch scale. This will be required because snow distribution models have had varied success in simulating snowpack accumulation/ablation dynamics in these mountain watersheds, ranging from R2=0.38 for individual south-facing forested snow survey routes to R2=0.95 when aggregated to the watershed scale. Key ecological responses to snowpack changes occur below the watershed scale, such as snow-mediated expansion of forest into subalpine meadows, making continued spatially-explicit snow surveys a necessity.

Montana↗
USGS2019

Simultaneous autoregressive (SAR) model

Simultaneous autoregressive (SAR) models are useful for accommodating various forms of dependence among data that have discrete support in a space of interest. These models are often specified hierarchically as mixed-effects regression models with first-moment structure controlled by a conventional linear regression term and second-moment structure induced by correlated random effects. In their general form, SAR models resemble conditional autoregressive (CAR) models, and can be made equivalent but are often parameterized differently. Importantly, SAR models can be specified by simultaneously regressing a discrete spatial process on itself. Thus, they allow one to construct statistical models for processes with directional graphical properties that pertain to data generating mechanisms. Most commonly SAR models have been used to account for structure among data with areal spatial support in applications involving ecology, epidemiology, sociology, and environmental science.

Book chapter↗
USGS2026

Environment, taxonomy, and socioeconomics predict non-imperilment in freshwater fishes

Freshwater fishes are among the most threatened taxa, yet conservation assessments remain incomplete for many species. Freshwater fishes provide essential ecosystem services such as food security, recreational opportunities, and cultural significance. Despite heavy alterations to freshwater ecosystems, the reasons for species’ sensitivity and resistance to imperilment are unclear. To address this need, we develop a machine learning framework to predict global imperilment status for 10,631 freshwater fish species using a comprehensive set of environmental, socioeconomic, and intrinsic species-level predictors. Using updated IUCN Red List data, we train and validate Random Forest classifiers to distinguish imperiled (Vulnerable, Endangered, Critically Endangered) from non-imperiled species. We examine the relative influence of 52 variables derived from 12 global sources describing extrinsic environmental and socioeconomic factors and intrinsic species-specific characteristics. Our models achieve higher accuracy for non-imperiled species (90.1%) compared to imperiled species (81.8%), reflecting the greater heterogeneity of threats and conditions driving imperilment. Across models, key predictors include habitat variables, taxonomic order, hydrological characteristics, and disturbance indicators, underscoring the interplay between ecology, geography, and human pressures. This integrative, reproducible approach demonstrates the utility of machine learning for guiding proactive conservation and provides a scalable framework for global biodiversity risk assessment.

Nature Communications↗
USGS2025

Reduced Atlantic reef growth past 2 °C warming amplifies sea-level impacts

Coral reefs form complex physical structures that can help to mitigate coastal flooding risk 1,2 . This function will be reduced by sea-level rise (SLR) and impaired reef growth caused by climate change and local anthropogenic stressors 3 . Water depths above reef surfaces are projected to increase as a result, but the magnitudes and timescales of this increase are poorly constrained, which limits modelling of coastal vulnerability 4,5 . Here we analyse fossil reef deposits to constrain links between reef ecology and growth potential across more than 400 tropical western Atlantic sites, and assess the magnitudes of resultant above-reef increases in water depth through to 2100 under various shared socioeconomic pathway (SSP) emission scenarios. Our analysis predicts that more than 70% of tropical western Atlantic reefs will transition into net erosional states by 2040, but that if warming exceeds 2 °C (SSP2–4.5 and higher), nearly all reefs (at least 99%) will be eroding by 2100. The divergent trajectories of reef growth and SLR will thus magnify the effects of SLR; increases in water depth of around 0.3–0.5 m above the present are projected under all warming scenarios by 2060, but depth increases of 0.7–1.2 m are predicted by 2100 under scenarios in which warming surpasses 2 °C. This would increase the risk of flooding along vulnerable reef-fronted coasts and modify nearshore hydrodynamics and ecosystems. Reef restoration offers one pathway back to higher reef growth 6,7 , but would dampen the effects of SLR in 2100 only by around 0.3–0.4 m, and only when combined with aggressive climate mitigation.

Florida↗
USGS2008

Quantile regression applied to spectral distance decay

Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.

IEEE Geoscience and Remote Sensing Letters↗
USGS2009

Spectral distance decay

Remotely sensed data represents key information for character-izing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (OLS) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both OLS and quantile regression. Nonetheless, OLS regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression.

Photogrammetric Engineering and Remote Sensing↗
USGS2007

On the choice of statistical models for estimating occurrence and extinction from animal surveys

In surveys of natural animal populations the number of animals that are present and available to be detected at a sample location is often low, resulting in few or no detections. Low detection frequencies are especially common in surveys of imperiled species; however, the choice of sampling method and protocol also may influence the size of the population that is vulnerable to detection. In these circumstances, probabilities of animal occurrence and extinction will generally be estimated more accurately if the models used in data analysis account for differences in abundance among sample locations and for the dependence between site-specific abundance and detection. Simulation experiments are used to illustrate conditions wherein these types of models can be expected to outperform alternative estimators of population site occupancy and extinction. ?? 2007 by the Ecological Society of America.

Ecology↗
USGS2013

Landscape indicators and land cover change in the Mid-Atlantic Region of the United States, 1973-2001

Landscape indicators, derived from land use and land cover data as well as other data, were used to calculate the ecological consequences of land cover change in terms of nitrate loading and physical bird habitat. Both were modeled from 1973, 1992, and 2001 land cover data in the Mid-Atlantic region of the United States. Land cover statistics and trends are calculated for three time periods. In general, forest gain and agricultural loss was found in areas of improving landscape indicators and forest loss and agricultural gain was found to occur in areas of declining indicators, which was confirmed by high-resolution aerial photographic analysis.

Delaware, Maryland, New Jersey, New York, North Ca↗
USGS2008

Using Molecular Genetic Markers to Resolve a Subspecies Boundary: The Northern Boundary of the Southwestern Willow Flycatcher in the Four-Corner States

*Executive Summary* The northern boundary of the endangered Southwestern Willow Flycatcher (Empidonax traillii extimus) is currently approximated as running through southern Colorado and Utah, but the exact placement is uncertain because this subspecies shares a border with the more northern and non-endangered E. t. adastus. To help resolve this issue, we evaluated the geographic distribution of mitochondrial and nuclear DNA by sampling breeding sites across the four-corner states (Arizona, Colorado, New Mexico, and Utah). We found that breeding sites clustered into two major groups generally consistent with the currently designated boundary, with the exception of three sites situated along the current boundary. However, delineating a precise boundary that would separate the two subspecies is made difficult because (1) we found evidence for a region of intergradation along the boundary area, suggesting the boundary is not discreet, and (2) the boundary region is sparsely populated, with too few extant breeding populations to precisely locate a boundary. The boundary region encompasses an area where elevation changes markedly over relatively short distances, with low elevation deserts to the south and more mesic, higher elevation habitats to the north. We hypothesized that latitudinal and elevational differences and their concomitant ecological effects could form an ecological barrier that inhibited gene flow between the subspecies, forming the basis for the subspecies boundary. We modeled changes in geographic patterns of genetic markers as a function of latitude and elevation finding significant support for this relationship. The model was brought into a GIS environment to create multiple subspecies boundaries, with the strength of each predicted boundary evaluated on the basis of how much genetic variation it explained. The candidate boundary that accounted for the most genetic variation was situated generally near the currently recognized subspecies boundary, but should be more biologically meaningful because it incorporates the landscape features that may be driving separation of the subspecies. Even so, we caution that using any narrow boundary line as an indicator of subspecies identity could be misleading because biologically the boundary is a region of intergradation rather than a discrete line. Designating, a boundary ultimately becomes a regulatory and management decision based on how much of the genetic variation unique to a subspecies should be protected. We discuss how the results of this study can help guide this decision process by wildlife policy makers.

Open-File Report↗
USGS2014

Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States

Information on future land-use and land-cover (LULC) change is needed to analyze the impact of LULC change on ecological processes. The U.S. Geological Survey has produced spatially explicit, thematically detailed LULC projections for the conterminous United States. Four qualitative and quantitative scenarios of LULC change were developed, with characteristics consistent with the Intergovernmental Panel on Climate Change (IPCC) Special Report on 5 Emission Scenarios (SRES). The four quantified scenarios (A1B, A2, B1, and B2) served as input to the Forecasting Scenarios of Land-use Change (FORE-SCE) model. Four spatially explicit datasets consistent with scenario storylines were produced for the conterminous United States, with annual LULC maps from 1992 through 2100. The future projections are characterized by a loss of natural land covers in most scenarios, with corresponding expansion of 10 anthropogenic land uses. Along with the loss of natural land covers, remaining natural land covers experience increased fragmentation under most scenarios, with only the B2 scenario remaining relatively stable in both proportion of remaining natural land covers and basic fragmentation measures. Forest stand age was also modeled. By 2100, scenarios and ecoregions with heavy forest cutting have relatively lower mean stand ages compared to those with less 15 forest cutting. Stand ages differ substantially between unprotected and protected forest lands, as well as between different forest classes. The modeled data were compared to the National Land Cover Database (NLCD) and other data sources to assess model characteristics. The consistent, spatially explicit, and thematically detailed LULC projections and the associated forest stand age data layers have been used to analyze LULC impacts on carbon and greenhouse gas fluxes, 20 biodiversity, climate and weather variability, hydrologic change, and other ecological processes.

Ecological Applications↗