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At least 1,189 records · Page 66Linked to original sources
USGS2025

Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?

Emblematic of global coral-reef ecosystem decline, the coral ecosystem-engineer Acropora palmata is now rare throughout much of the western Atlantic. Understanding when and where this foundation species occurred during the past can provide information about the environmental limits defining its distribution through space and time. In this paper, the present, historical and newly dated geological records of A. palmata are compared to reveal novel insights into the environmental constraints on its occurrence in Dry Tortugas National Park, a subtropical reef system at the south-western terminus of the Florida reef tract. Although past geological investigation found little evidence of the species in the park, a single, moderately sized A. palmata reef existed throughout historical times (1881 Common Era [CE] to present day; ‘historical population’, termed herein). Over the last 140 years, repeated population declines occurred with little to no recovery, culminating in the extirpation of A. palmata from the area during the 2023–2024 CE global coral bleaching event. Reported here for the first time is a significant record of Late Holocene A. palmata populations that existed from ca 4500 to 375 years before present (‘Late Holocene population,’ termed herein) in three broadly distributed areas of the shallow Dry Tortugas platform. This discovery challenges previous assumptions regarding the species' limited contribution to reef development in the area by providing data that extend the known spatial and stratigraphic extent of Holocene populations in this location. It is posited that, although the Late Holocene climate largely suppressed regional reef development, the new records provide evidence for centennial-scale periods of more favourable and stable climate that allowed for short-term expansions of A. palmata populations in the Dry Tortugas. In conclusion, the species' prospects for future success in this and other subtropical location

Florida↗
USGS2016

Biomarkers reveal sea turtles remained in oiled areas following the Deepwater Horizon oil spill

Assessments of large-scale disasters, such as the Deepwater Horizon oil spill, are problematic because while measurements of post-disturbance conditions are common, measurements of pre-disturbance baselines are only rarely available. Without adequate observations of pre-disaster organismal and environmental conditions, it is impossible to assess the impact of such catastrophes on animal populations and ecological communities. Here, we use long-term biological tissue records to provide pre-disaster data for a vulnerable marine organism. Keratin samples from the carapace of loggerhead sea turtles record the foraging history for up to 18 years, allowing us to evaluate the effect of the oil spill on sea turtle foraging patterns. Samples were collected from 76 satellite-tracked adult loggerheads in 2011 and 2012, approximately one to two years after the spill. Of the 10 individuals that foraged in areas exposed to surface oil, none demonstrated significant changes in foraging patterns post spill. The observed long-term fidelity to foraging sites indicates that loggerheads in the northern Gulf of Mexico likely remained in established foraging sites, regardless of the introduction of oil and chemical dispersants. More research is needed to address potential long-term health consequences to turtles in this region. Mobile marine organisms present challenges for researchers to monitor effects of environmental disasters, both spatially and temporally. We demonstrate that biological tissues can reveal long-term histories of animal behavior and provide critical pre-disaster baselines following an anthropogenic disturbance or natural disaster.

Ecological Applications↗
USGS2017

Species interactions and the effects of climate variability on a wetland amphibian metacommunity

Disentangling the role that multiple interacting factors have on species responses to shifting climate poses a significant challenge. However, our ability to do so is of utmost importance to predict the effects of climate change on species distributions. We examined how populations of three species of wetland-breeding amphibians, which varied in life history requirements, responded to a six-year period of extremely variable precipitation. This interval was punctuated by both extensive drought and heavy precipitation and flooding, providing a natural experiment to measure community responses to environmental perturbations. We estimated occurrence dynamics using a discrete hidden Markov modeling approach that incorporated information regarding habitat state and predator–prey interactions. This approach allowed us to measure how metapopulation dynamics of each amphibian species was affected by interactions among weather, wetland hydroperiod, and co-occurrence with fish predators. The pig frog, a generalist, proved most resistant to perturbations, with both colonization and persistence being unaffected by seasonal variation in precipitation or co-occurrence with fishes. The ornate chorus frog, an ephemeral wetland specialist, responded positively to periods of drought owing to increased persistence and colonization rates during periods of low-rainfall. Low probabilities of occurrence of the ornate chorus frog in long-duration wetlands were driven by interactions with predators due to low colonization rates when fishes were present. The mole salamander was most sensitive to shifts in water availability. In our study area, this species never occurred in short-duration wetlands and persistence probabilities decreased during periods of drought. At the same time, negative effects occurred with extreme precipitation because flooding facilitated colonization of fishes to isolated wetlands and mole salamanders did not colonize wetlands once fishes were present. We demonstrate that the effects of changes in water availability depend on interactions with predators and wetland type and are influenced by the life history of each of our species. The dynamic species occurrence modeling approach we used offers promise for other systems when the goal is to disentangle the complex interactions that determine species responses to environmental variability.

Florida↗
USGS2017

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis , and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua . Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea . We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

Mojave Desert↗
USGS2017

Acoustic telemetry and fisheries management

This paper reviews the use of acoustic telemetry as a tool for addressing issues in fisheries management, and serves as the lead to the special Feature Issue of Ecological Applications titled “Acoustic Telemetry and Fisheries Management” . Specifically, we provide an overview of the ways in which acoustic telemetry can be used to inform issues central to the ecology, conservation, and management of exploited and/or imperiled fish species. Despite great strides in this area in recent years, there are comparatively few examples where data have been applied directly to influence fisheries management and policy. We review the literature on this issue, identify the strengths and weaknesses of work done to date, and highlight knowledge gaps and difficulties in applying empirical fish telemetry studies to fisheries policy and practice. We then highlight the key areas of management and policy addressed, as well as the challenges that needed to be overcome to do this. We conclude with a set of recommendations about how researchers can, in consultation with stock assessment scientists and managers, formulate testable scientific questions to address and design future studies to generate data that can be used in a meaningful way by fisheries management and conservation practitioners. We also urge the involvement of relevant stakeholders (managers, fishers, conservation societies, etc.) early on in the process (i.e. in the co-creation of research projects), so that all priority questions and issues can be addressed effectively.

Ecological Applications↗
USGS2017

Model-based approaches to deal with detectability: a comment on Hutto (2016)

In a recent paper, Hutto (2016a) challenges the need to account for detectability when interpreting data from point counts. A number of issues with model-based approaches to deal with detectability are presented, and an alternative suggested: surveying an area around each point over which detectability is assumed certain. The article contains a number of false claims and errors of logic, and we address these here. We provide suggestions about appropriate uses of distance sampling and occupancy modeling, arising from an intersection of design- and model-based inference.

Ecological Applications↗
USGS2017

A pesticide paradox: Fungicides indirectly increase fungal infections

There are many examples where the use of chemicals have had profound unintended consequences, such as fertilizers reducing crop yields (paradox of enrichment) and insecticides increasing insect pests (by reducing natural biocontrol). Recently, the application of agrochemicals, such as agricultural disinfectants and fungicides, has been explored as an approach to curb the pathogenic fungus, Batrachochytrium dendrobatidis ( Bd ), which is associated with worldwide amphibian declines. However, the long-term, net effects of early-life exposure to these chemicals on amphibian disease risk have not been thoroughly investigated. Using a combination of laboratory experiments and analysis of data from the literature, we explored the effects of fungicide exposure on Bd infections in two frog species. Extremely low concentrations of the fungicides azoxystrobin, chlorothalonil, and mancozeb were directly toxic to Bd in culture. However, estimated environmental concentrations of the fungicides did not reduce Bd on Cuban tree frog ( Osteopilus septentrionalis ) tadpoles exposed simultaneously to any of these fungicides and Bd , and fungicide exposure actually increased Bd -induced mortality. Additionally, exposure to any of these fungicides as tadpoles resulted in higher Bd abundance and greater Bd -induced mortality when challenged with Bd post-metamorphosis, an average of 71 d after their last fungicide exposure. Analysis of data from the literature revealed that previous exposure to the fungicide itraconazole, which is commonly used to clear Bd infections, made the critically endangered booroolong frog ( Litoria booroolongensis ) more susceptible to Bd . Finally, a field survey revealed that Bd prevalence was positively associated with concentrations of fungicides in ponds. Although fungicides show promise for controlling Bd , these results suggest that, if fungicides do not completely eliminate Bd or if Bd recolonizes, exposure to fungicides has the potential to do more harm than good. To ensure that fungicide applications have the intended consequence of curbing amphibian declines, researchers must identify which fungicides do not compromise the pathogen resistance mechanisms of amphibians.

Ecological Applications↗
USGS2018

Making do with less: Must sparse data preclude informed harvest strategies for European waterbirds?

The demography of many European waterbirds is not well understood because most countries have conducted little monitoring and assessment, and coordination among countries on waterbird management has little precedent. Yet intergovernmental treaties now mandate the use of sustainable, adaptive harvest strategies, whose development is challenged by a paucity of demographic information. In this study, we explore how a combination of allometric relationships, fragmentary monitoring and research information, and expert judgment can be used to estimate the parameters of a theta-logistic population model, which in turn can be used in a Markov decision process to derive optimal harvesting strategies. We show how to account for considerable parametric uncertainty, as well as for different management objectives. We illustrate our methodology with a poorly understood population of taiga bean geese ( Anser fabalis fabalis ), which is a popular game bird in Fennoscandia. Our results for taiga bean geese suggest that they may have demographic rates similar to other, well-studied species of geese, and our model-based predictions of population size are consistent with the limited monitoring information available. Importantly, we found that by using a Markov decision process, a simple scalar population model may be sufficient to guide harvest management of this species, even if its demography is age-structured. Finally, we demonstrated how two different management objectives can lead to very different optimal harvesting strategies, and how conflicting objectives may be traded off with each other. This approach will have broad application for European waterbirds by providing preliminary estimates of key demographic parameters, by providing insights into the monitoring and research activities needed to corroborate those estimates, and by producing harvest management strategies that are optimal with respect to the managers’ objectives, options, and available demographic information.

Ecological Applications↗
USGS2018

Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground‐based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi‐state mark–recapture methods using a hierarchical N ‐mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state‐space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk ( Cervus elaphus nelsoni ) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N‐mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.

Ecological Applications↗
USGS2018

Adaptive management of animal populations with significant unknowns and uncertainties: A case study

Conservation and management decision making in natural resources is challenging due to numerous uncertainties and unknowns, especially relating to understanding system dynamics. Adaptive resource management (ARM) is a formal process to making logical and transparent recurrent decisions when there are uncertainties about system dynamics. Despite wide recognition and calls for implementing adaptive natural resource management, applications remain limited. More common is a reactive approach to decision making, which ignores future system dynamics. This contrasts with ARM, which anticipates future dynamics of ecological process and management actions using a model-based framework. Practitioners may be reluctant to adopt ARM because of the dearth of comparative evaluations between ARM and more common approaches to making decisions. We compared the probability of meeting management objectives when managing a population under both types of decision frameworks, specifically in relation to typical uncertainties and unknowns. We use a population of Sandhill Cranes as our case study. We evaluate each decision process under varying levels of monitoring and ecological uncertainty, where the true underlying population dynamics followed a stochastic age-structured population model with environmentally driven vital rate density dependence. We found that the ARM framework outperformed the currently employed reactive decision framework to manage Sandhill Cranes in meeting the population objective across an array of scenarios. This was even the case when the candidate set of population models contained only naïve representations of the true population process. Under the reactive decision framework, we found little improvement in meeting the population objective even if monitoring uncertainty was eliminated. In contrast, if the population was monitored without error within the ARM framework, the population objective was always maintained, regardless of the population models considered. Contrary to expectation, we found that age-specific optimal harvest decisions are not always necessary to meet a population objective when population dynamics are age structured. Population managers can decrease risks and gain transparency and flexibility in management by adopting an ARM framework. If population monitoring data has high sampling variation and/or limited empirical knowledge is available for constructing mechanistic population models, ARM model sets should consider a range of mechanistic, descriptive, and predictive model types.

Ecological Applications↗
USGS2018

Eco‐evolutionary rescue promotes host–pathogen coexistence

Emerging infectious pathogens are responsible for some of the most severe host mass mortality events in wild populations. Yet, effective pathogen control strategies are notoriously difficult to identify, in part because quantifying and forecasting pathogen spread and disease dynamics is challenging. Following an outbreak, hosts must cope with the presence of the pathogen, leading to host–pathogen coexistence or extirpation. Despite decades of research, little is known about host–pathogen coexistence post‐outbreak when low host abundances and cryptic species make these interactions difficult to study. Using a novel disease‐structured N‐mixture model, we evaluate empirical support for three host–pathogen coexistence hypotheses (source–sink, eco‐evolutionary rescue, and spatial variation in pathogen transmission) in a Neotropical amphibian community decimated by Batrachochytrium dendrobatidis ( Bd ) in 2004. During 2010–2014, we surveyed amphibians in Parque Nacional G. D. Omar Torríjos Herrera, Coclé Province, El Copé, Panama. We found that the primary driver of host–pathogen coexistence was eco‐evolutionary rescue, as evidenced by similar amphibian survival and recruitment rates between infected and uninfected hosts. Average apparent monthly survival rates of uninfected and infected hosts were both close to 96%, and the expected number of uninfected and infected hosts recruited (via immigration/reproduction) was less than one host per disease state per 20‐m site. The secondary driver of host–pathogen coexistence was spatial variation in pathogen transmission as we found that transmission was highest in areas of low abundance but there was no support for the source–sink hypothesis. Our results indicate that changes in the host community (i.e., through genetic or species composition) can reduce the impacts of emerging infectious disease post‐outbreak. Our disease‐structured N‐mixture model represents a valuable advancement for conservation managers trying to understand underlying host–pathogen interactions and provides new opportunities to study disease dynamics in remnant host populations decimated by virulent pathogens.

Ecological Applications↗
USGS2019

The past and future roles of competition and habitat in the range‐wide occupancy dynamics of Northern Spotted Owls

Slow ecological processes challenge conservation. Short‐term variability can obscure the importance of slower processes that may ultimately determine the state of a system. Furthermore, management actions with slow responses can be hard to justify. One response to slow processes is to explicitly concentrate analysis on state dynamics. Here, we focus on identifying drivers of Northern Spotted Owl ( Strix occidentalis caurina ) territorial occupancy dynamics across 11 study areas spanning their geographic range and forecasting response to potential management actions. Competition with Barred Owls ( Strix varia ) has increased Spotted Owl territory extinction probabilities across all study areas and driven recent declines in Spotted Owl populations. Without management intervention, the Northern Spotted Owl subspecies will be extirpated from parts of its current range within decades. In the short term, Barred Owl removal can be effective. Over longer time spans, however, maintaining or improving habitat conditions can help promote the persistence of northern spotted owl populations. In most study areas, habitat effects on expected Northern Spotted Owl territorial occupancy are actually greater than the effects of competition from Barred Owls. This study suggests how intensive management actions (removal of a competitor) with rapid results can complement a slower management action (i.e., promoting forest succession).

Ecological Applications↗
USGS2019

Genetic tagging in the Anthropocene: Scaling ecology from alleles to ecosystems

The Anthropocene is an era of marked human impact on the world. Quantifying these impacts 51 has become central to understanding the dynamics of coupled human-natural systems, resource52 dependent livelihoods, and biodiversity conservation. Ecologists are facing growing pressure to 53 quantify the size, distribution, and trajectory of wild populations in a cost-effective and socially54 acceptable manner. Genetic tagging, combined with modern computational and genetic analyses, 55 is an under-utilized tool to meet this demand, especially for wide-ranging, elusive, sensitive, and 56 low-density species. Genetic tagging studies are now revealing unprecedented insight into the 57 mechanisms that control the density, trajectory, connectivity and human-wildlife conflict for 58 populations over vast spatial scales. Here we outline the application of, and ecological inferences 59 from, new analytical techniques applied to genetically-tagged individuals, contrast this approach 60 with conventional methods, and describe how genetic tagging can be better applied to address 61 outstanding questions in ecology. We provide example analyses using a long-term genetic 62 tagging dataset of grizzly bears in the Canadian Rockies. The genetic tagging toolbox is a 63 powerful and overlooked ensemble that ecologists and conservation biologists can leverage to 64 generate evidence and meet the challenges of the Anthropocene.

Ecological Applications↗
USGS2019

Spatially explicit network analysis reveals multi-species annual cycle movement patterns of sea ducks

Conservation of long-distance migratory species poses unique challenges. Migratory connectivity—that is, the extent to which groupings of individuals at breeding sites are maintained in wintering areas—is frequently used to evaluate population structure and assess use of key habitat areas. However, for species with complex or variable annual-cycle movements, this traditional bimodal framework of migratory connectivity may be overly simplistic. Like many other waterfowl, sea ducks often travel to specific pre- and post-breeding sites outside their nesting and wintering areas to prepare for migration and, in some cases, molt their flight feathers. These additional molt migrations may play a key role in population structure, but are not included in traditional models of migratory connectivity. Network analysis, which applies graph theory to assess landscape connectivity, offers a powerful tool for quantitatively assessing the contributions of different sites used throughout the annual cycle to complex spatial networks. We collected satellite telemetry data on annual cycle movements of over 500 individual sea ducks of five species from throughout eastern North America and the Great Lakes. From these data, we constructed a multi-species network model of migratory patterns and site use over the course of the breeding, molting, wintering, and migration periods. Our results highlight inter- and intra-specific differences in the patterns and complexity of annual-cycle movement patterns, including the central importance of staging and molting sites in James Bay and the St. Lawrence River to multi-species habitat connectivity. We also discuss potential applications of network migration models to conservation prioritization, identification of population units, and integrating different data streams.

Ecological Applications↗
USGS2020

The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes

(Munson) The challenges of restoration in dryland ecosystems are growing due to a rise in anthropogenic disturbance and increasing aridity. Plant functional traits are often used to predict plant performance and can offer a window into the potential outcomes of restoration efforts across environmental gradients. We tracked 15 years of seeding outcomes across 150 sites on the Colorado Plateau, a cold desert ecoregion in the western United States, and analyzed the independent and interactive effects of functional traits (seed mass, height, and specific leaf area) and local biologically relevant climate variables on seeding success. We predicted that the best models would include an interaction between plant traits and climate, indicating a need to match the right trait value to the right climate conditions in order to maximize seeding success. Indeed, we found that both plant height and seed size significantly interacted with temperature seasonality, with larger seeds and taller plants performing better in more seasonal environments. We also determined that these trait-environment patterns are not driven by the use of native vs. non-native species. Our results lend insight to using plant traits to inform the selection of seed mixes for restoring areas with specific climatic conditions, while also demonstrating the strong influence of temperature seasonality on seeding success in the Colorado Plateau region.

Colorado Plateau↗
USGS2020

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13–20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

Ecological Applications↗
USGS2020

A novel quantitative framework for riverscape genetics

Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout ( Salvelinus fontinalis ) in north central Pennsylvania, USA. Using the fixation index ( F ST ) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top-ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

Ecological Applications↗
USGS2020

Landscape‐scale restoration minimizes tree growth vulnerability to 21st century drought in a dry forest

Increasing aridity is a challenge for forest managers and reducing stand density to minimize competition is a recognized strategy to mitigate drought impacts on growth. In many dry forests, the most widespread and common forest management programs currently being implemented focus on restoration of historical stand structures, primarily to minimize fire risk and enhance watershed function. The implications of these restoration projects for drought vulnerability are not well understood. Here, we examined how planned restoration treatments in the Four Forests Restoration Initiative, the largest forest restoration project in the United States, would alter landscape‐scale patterns of forest growth and drought vulnerability throughout the 21 st century. Using drought‐growth relationships developed within the landscape, we considered a suite of climate and treatment scenarios and estimated average forest growth and the proportion of years with extremely low growth as a measure of vulnerability to long‐term decline. Climatic shifts projected for this landscape include higher temperatures and shifting seasonal precipitation that promotes lower soil moisture availability in the early growing season and greater hot‐dry stress, conditions negatively associated with tree growth. However, drought severity and the magnitude of future growth declines was moderated by the thinning treatments. Compared to historical conditions, proportional growth in mid‐century declines by ~40% if thinning ceases or continues at the status quo pace. By comparison, proportional growth declines by only 20% if the Four Forest Restoration Initiative treatments are fully implemented, and < 10% if stands are thinned even more intensively than currently planned. Furthermore, restoration treatments resulted in dramatically fewer years with extremely low growth in the future, a recognized precursor to forest decline and eventual tree mortality. Benefits from density reduction for mitigating drought‐induced growth declines are more apparent in mid‐century and under RCP4.5 than under RCP8.5 at the end of the century. Future climate is inherently uncertain, and our results only reflect the climate projections from the representative suite of models examined. Nevertheless, these results indicate that forest restoration projects designed for other objectives also have substantial benefits for minimizing future drought vulnerability in dry forests and provide additional incentive to accelerate the pace of restoration.

Ecological Applications↗