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USGS2012

Evaluating the ability of regional models to predict local avian abundance

Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush (Hylocichla mustelina) and blue-winged warbler (Vermivora cyanoptera) from 2 time periods (1995-1998 and 2006-2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006-2007. Competing (Deviance Information Criteria <5) blue-winged warbler models accounted for 67% of the variability and suggested positive associations with forest edge and proportion of forest at the 8,000-ha scale, and negative associations with forest patch area (800 ha) and wetness (800 ha and 80,000 ha). The regional model performed best for blue-winged warbler predicted abundances from point counts conducted in Iowa during 1995-1996 ( r s = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined ( r s = 0.58, P ≤ 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data ( r s = 0.03, P = 0.92 for Iowa and r s = 0.13, P = 0.51 for all areas, 2006-2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by land managers, but we caution that the utility of such models may decrease over time for species preferring relatively ephemeral habitats if model inputs are not updated accordingly.

Journal of Wildlife Management↗

USGS2016

Common carp disrupt ecosystem structure and function through middle-out effects

Middle-out effects or a combination of top-down and bottom-up processes create many theoretical and empirical challenges in the realm of trophic ecology. We propose using specific autecology or species trait (i.e. behavioural) information to help explain and understand trophic dynamics that may involve complicated and non-unidirectional trophic interactions. The common carp ( Cyprinus carpio ) served as our model species for whole-lake observational and experimental studies; four trophic levels were measured to assess common carp-mediated middle-out effects across multiple lakes. We hypothesised that common carp could influence aquatic ecosystems through multiple pathways (i.e. abiotic and biotic foraging, early life feeding, nutrient). Both studies revealed most trophic levels were affected by common carp, highlighting strong middle-out effects likely caused by common carp foraging activities and abiotic influence (i.e. sediment resuspension). The loss of water transparency, submersed vegetation and a shift in zooplankton dynamics were the strongest effects. Trophic levels furthest from direct pathway effects were also affected (fish life history traits). The present study demonstrates that common carp can exert substantial effects on ecosystem structure and function. Species capable of middle-out effects can greatly modify communities through a variety of available pathways and are not confined to traditional top-down or bottom-up processes.

Marine and Freshwater Research↗

USGS2018

A causal partition of trait correlations: using graphical models to derive statistical models from theoretical language

Recent studies hypothesize various causes of species‐level trait covariation, namely size (e.g., metabolic theory of ecology and leaf economics spectrum), pace‐of‐life (e.g., slow‐to‐fast continuum; lifestyle continuum), evolutionary history (e.g., phylogenetic conservatism), and ecological conditions (e.g., stabilizing selection). Various methods have been used in attempts to partition trait correlation among these influences (e.g., univariate analysis, principal components analysis, and factor analysis). However, it is not clear that the implied causal structure assumed by these methods matches the hypothesized causal structure driving trait correlations, a situation that can potentially lead to biased estimates and incorrect partitioning among mechanisms. Here, we propose the application of graphical causal models (GCM) for across‐kingdom synthesis and to aid researchers in their selection of correct analytical strategies. Graphical causal models use causal diagrams (i.e., box‐and‐arrow graphs) to represent expert knowledge of the data‐generating processes to analytically investigate the possibility of identifying hypothesized causal associations. We developed a causal diagram that synthesizes prominent hypotheses of trait covariation. Using the causal diagram, we (1) derived a quantitative expression to partition trait covariance among its hypothesized causal elements (i.e., size, pace‐of‐life, evolutionary history, and ecological conditions) and (2) developed analytic strategies to attribute trait covariance among the hypothesized causal elements under real‐world data availability, namely unobserved variables (i.e., pace‐of‐life) and confounding variables (i.e., evolutionary history and ecological conditions). Finally, we tested each analytic strategy by simulating trait datasets and, after incorporating the data limitations, tested their ability to correctly partition trait covariance. The analytical strategies were able to correctly partition trait covariance into the hypothesized causal elements of size, pace‐of‐life, and the historical effects of evolutionary history and ecological conditions. We demonstrate the efficacy of these strategies by applying them to a widely used trait dataset. Overall, the application of GCM revealed that researchers have used inappropriate measures to represent their theoretical constructs and have relied on analytical strategies that violated their causal assumptions, likely resulting in biased estimates. We discuss how this mismatch between theoretical language and statistical methods is prevalent in species‐level, trait‐based research and call for future studies to address these limitations.

Ecosphere↗

USGS2021

Stoichiometric ecotoxicology for a multisubstance world

Nutritional and contaminant stressors influence organismal physiology, trophic interactions, community structure, and ecosystem-level processes; however, the interactions between toxicity and elemental imbalance in food resources have been examined in only a few ecotoxicity studies. Integrating well-developed ecological theories that cross all levels of biological organization can enhance our understanding of ecotoxicology. In the present article, we underline the opportunity to couple concepts and approaches used in the theory of ecological stoichiometry (ES) to ask ecotoxicological questions and introduce stoichiometric ecotoxicology, a subfield in ecology that examines how contaminant stress, nutrient supply, and elemental constraints interact throughout all levels of biological organization. This conceptual framework unifying ecotoxicology with ES offers potential for both empirical and theoretical studies to deepen our mechanistic understanding of the adverse outcomes of chemicals across ecological scales and improve the predictive powers of ecotoxicology.

BioScience↗

USGS2016

Roost site selection by ring-billed and herring gulls

Gulls ( Larus spp.) commonly roost in large numbers on inland and coastal waters, yet there is little information on how or where gulls choose sites for roosting. Roost site selection can lead to water quality degradation or aviation hazards when roosts are formed on water supply reservoirs or are close to airports. Harassment programs are frequently initiated to move or relocate roosting gulls but often have mixed results because gulls are reluctant to leave or keep returning. As such, knowledge of gull roost site selection and roosting ecology has applied and ecological importance. We used satellite telemetry and an information-theoretic approach to model seasonal roost selection of ring-billed ( L. delawarensis ) and herring gulls ( L. argentatus ) in Massachusetts, USA. Our results indicated that ring-billed gulls preferred freshwater roosts and will use a variety of rivers, lakes, and reservoirs. Herring gulls regularly roosted on fresh water but used salt water roosts more often than ring-billed gulls and also roosted on a variety of land habitats. Roost modeling showed that herring and ring-billed gulls selected inland fresh water roosts based on size of the water body and proximity to their last daytime location; they selected the largest roost closest to where they ended the day. Management strategies to reduce or eliminate roosting gulls could identify and try to eliminate other habitat variables (e.g., close-by foraging sites) that are attracting gulls before attempting to relocate or redistribute (e.g., through hazing programs) roosting birds.

Massachusetts↗

USGS2007

Testing ecological and behavioral correlates of nest predation

Variation in nest predation rates among bird species are assumed to reflect differences in risk that are specific to particular nest sites. Theoretical and empirical studies suggest that parental care behaviors can evolve in response to nest predation risk and thereby differ among ecological conditions that vary in inherent risk. However, parental care also can influence predation risk. Separating the effects of nest predation risk inherent to a nest site from the risk imposed by parental strategies is needed to understand the evolution of parental care. Here we identify correlations between risks inherent to nest sites, and risk associated with parental care behaviors, and use an artificial nest experiment to assess site-specific differences in nest predation risk across nesting guilds and between habitats that differed in nest predator abundance. We found a strong correlation between parental care behaviors and inherent differences in nest predation risk, but despite the absence of parental care at artificial nests, patterns of nest predation risk were similar for real and artificial nests both across nesting guilds and between predator treatments. Thus, we show for the first time that inherent risk of nest predation varies with nesting guild and predator abundance independent of parental care. ?? Oikos.

Oikos↗

USGS2007

Representing general theoretical concepts in structural equation models: The role of composite variables

Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically-based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling heterogeneous concepts of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially-reduced-form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influence of suites of variables are often of interest. ?? Springer Science+Business Media, LLC 2007.

Environmental and Ecological Statistics↗

USGS2007

Design and implementation of estimation-based monitoring programs for flora and fauna: A case study on the Cherokee National Forest

Science-based monitoring of biological resources is important for a greater understanding of ecological systems and for assessment of the target population using theoretic-based management approaches. When selecting variables to monitor, managers first need to carefully consider their objectives, the geographic and temporal scale at which they will operate, and the effort needed to implement the program. Generally, monitoring can be divided into two categories: index and inferential. Although index monitoring is usually easier to implement, analysis of index data requires strong assumptions about consistency in detection rates over time and space, and parameters are often biasednot accounting for detectability and spatial variation. In most cases, individuals are not always available for detection during sampling periods, and the entire area of interest cannot be sampled. Conversely, inferential monitoring is more rigorous because it is based on nearly unbiased estimators of spatial distribution. Thus, we recommend that detectability and spatial variation be considered for all monitoring programs that intend to make inferences about the target population or the area of interest. Application of these techniques is especially important for the monitoring of Threatened and Endangered (T&E) species because it is critical to determine if population size is increasing or decreasing with some level of certainty. Use of estimation-based methods and probability sampling will reduce many of the biases inherently associated with index data and provide meaningful information with respect to changes that occur in target populations. We incorporated inferential monitoring into protocols for T&E species spanning a wide range of taxa on the Cherokee National Forest in the Southern Appalachian Mountains. We review the various approaches employed for different taxa and discuss design issues, sampling strategies, data analysis, and the details of estimating detectability using site occupancy. These techniques provide a science-based approach for monitoring and can be of value to all resource managers responsible for management of T&E species.

Proceedings of the Annual Conference of the Southe↗

USGS2026

Impacts of dams on river herring populations through their native range: What’s left?

Introduction: River herring (alewife, Alosa pseudoharengus , and blueback herring, Alosa aestivalis ) are native along the East Coast of North America. These fish are culturally important and provide critical ecosystem linkages between marine and freshwater habitats. Due to overfishing, and habitat loss (through degradation and damming), river herring populations are at historical lows across their native range. In the last 200 years, dams and other impoundments have reduced access to spawning habitat for these and other anadromous fish. Methods: To assess the theoretical coast-wide spawning potential for river herring, we quantified historically accessible spawning habitat (pre-dam) in coastal freshwater rivers using physical river characteristics (width and gradient). To assess the impact of dams on spawning habitat, we used a dams database to segment river reaches. This allowed us to characterize spawning habitat upstream and downstream of dams. River-specific population models were then used to estimate the number of potential spawners for each species, based on habitat estimates and life history parameters. We investigated three scenarios: no dams, favorable dam passage, and no dam passage. We use these simulations to compare the theoretical spawning potential for river herring prior to and after dam construction coastwide. Results: We estimated that 52% of alewife and 51% of blueback herring habitat is located upstream of dams throughout the East Coast of North America. This results in a theoretical loss of ~880 million alewife and ~100 million blueback herring potential spawners due to dams, reducing both the ecological connection and fishery potential of these species. Discussion: Even with the best-case “current” scenario for fish passage, we observed marginal increases in spawner abundance. These results highlight the relatively small theoretical influence current passage structures may have on restoring river herring to their historical abundances compared to dam removal. Our sensitivity analyses demonstrate that upstream passage to access additional habitat is only advantageous when effective juvenile and adult downstream passage are in place.

East Coast of North America↗

USGS2000

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude -0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude -0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude -0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at -0.3769.

Environmental and Ecological Statistics↗

USGS2016

Combining statistical inference and decisions in ecology

Statistical decision theory (SDT) is a sub-field of decision theory that formally incorporates statistical investigation into a decision-theoretic framework to account for uncertainties in a decision problem. SDT provides a unifying analysis of three types of information: statistical results from a data set, knowledge of the consequences of potential choices (i.e., loss), and prior beliefs about a system. SDT links the theoretical development of a large body of statistical methods including point estimation, hypothesis testing, and confidence interval estimation. The theory and application of SDT have mainly been developed and published in the fields of mathematics, statistics, operations research, and other decision sciences, but have had limited exposure in ecology. Thus, we provide an introduction to SDT for ecologists and describe its utility for linking the conventionally separate tasks of statistical investigation and decision making in a single framework. We describe the basic framework of both Bayesian and frequentist SDT, its traditional use in statistics, and discuss its application to decision problems that occur in ecology. We demonstrate SDT with two types of decisions: Bayesian point estimation, and an applied management problem of selecting a prescribed fire rotation for managing a grassland bird species. Central to SDT, and decision theory in general, are loss functions. Thus, we also provide basic guidance and references for constructing loss functions for an SDT problem.

Ecological Applications↗

USGS2017

A guide to multi-objective optimization for ecological problems with an application to cackling goose management

Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem. A large class of methods for solving MOO problems can be separated into two strategies: modelling preferences pre-optimization (the a priori strategy), or modelling preferences post-optimization (the a posteriori strategy). The a priori strategy requires describing preferences among objectives without knowledge of how preferences affect the resulting decision. In the a posteriori strategy, the decision maker simultaneously considers a set of solutions (the Pareto optimal set) and makes a choice based on the trade-offs observed in the set. We describe several methods for modelling preferences pre-optimization, including: the bounded objective function method, the lexicographic method, and the weighted-sum method. We discuss modelling preferences post-optimization through examination of the Pareto optimal set. We applied each MOO strategy to the natural resource management problem of selecting a population target for cackling goose ( Branta hutchinsii minima ) abundance. Cackling geese provide food security to Native Alaskan subsistence hunters in the goose's nesting area, but depredate crops on private agricultural fields in wintering areas. We developed objective functions to represent the competing objectives related to the cackling goose population target and identified an optimal solution first using the a priori strategy, and then by examining trade-offs in the Pareto set using the a posteriori strategy. We used four approaches for selecting a final solution within the a posteriori strategy; the most common optimal solution, the most robust optimal solution, and two solutions based on maximizing a restricted portion of the Pareto set. We discuss MOO with respect to natural resource management, but MOO is sufficiently general to cover any ecological problem that contains multiple competing objectives that can be quantified using objective functions.

Ecological Modelling↗

USGS2007

Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: A case of ragweed (Ambrosia artemisiifolia L.) distribution in China

Invasive exotic species pose a growing threat to the economy, public health, and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species, the environmental factors that influence these distributions, and the ability to predict them using statistical and information-theoretic approaches. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, for most species, absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed (Ambrosia artemisiifolia L.) from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic, topographic, and land cover information. Our logistic regression model was based on Akaike's Information Criterion (AIC) from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally, we used the model and the compartmentalized criterion developed in native ranges to "project" a potential distribution onto the exotic ranges to build habitat-suitability maps. ?? Science in China Press 2007.

Chinese Science Bulletin↗

USGS2011

How restructuring river connectivity changes freshwater fish biodiversity and biogeography

Interbasin water transfer projects, in which river connectivity is restructured via man-made canals, are an increasingly popular solution to address the spatial mismatch between supply and demand of fresh water. However, the ecological consequences of such restructuring remain largely unexplored, and there are no general theoretical guidelines from which to derive these expectations. River systems provide excellent opportunities to explore how network connectivity shapes habitat occupancy, community dynamics, and biogeographic patterns. We apply a neutral model (which assumes competitive equivalence among species within a stochastic framework) to an empirically derived river network to explore how proposed changes in network connectivity may impact patterns of freshwater fish biodiversity. Without predicting the responses of individual extant species, we find the addition of canals connecting hydrologically isolated river basins facilitates the spread of common species and increases average local species richness without changing the total species richness of the system. These impacts are sensitive to the parameters controlling the spatial scale of fish dispersal, with increased dispersal affording more opportunities for biotic restructuring at the community and landscape scales. Connections between isolated basins have a much larger effect on local species richness than those connecting reaches within a river basin, even when those within-basin reaches are far apart. As a result, interbasin canal projects have the potential for long-term impacts to continental-scale riverine communities.

Water Resources Research↗

USGS2014

How much is new information worth? Evaluating the financial benefit of resolving management uncertainty

Conservation decision-makers face a trade-off between spending limited funds on direct management action, or gaining new information in an attempt to improve management performance in the future. Value-of-information analysis can help to resolve this trade-off by evaluating how much management performance could improve if new information was gained. Value-of-information analysis has been used extensively in other disciplines, but there are only a few examples where it has informed conservation planning, none of which have used it to evaluate the financial value of gaining new information. We address this gap by applying value-of-information analysis to the management of a declining koala Phascolarctos cinereus population. Decision-makers responsible for managing this population face uncertainty about survival and fecundity rates, and how habitat cover affects mortality threats. The value of gaining new information about these uncertainties was calculated using a deterministic matrix model of the koala population to find the expected population growth rate if koala mortality threats were optimally managed under alternative model hypotheses, which represented the uncertainties faced by koala managers. Gaining new information about survival and fecundity rates and the effect of habitat cover on mortality threats will do little to improve koala management. Across a range of management budgets, no more than 1·7% of the budget should be spent on resolving these uncertainties. The value of information was low because optimal management decisions were not sensitive to the uncertainties we considered. Decisions were instead driven by a substantial difference in the cost efficiency of management actions. The value of information was up to forty times higher when the cost efficiencies of different koala management actions were similar. Synthesis and applications . This study evaluates the ecological and financial benefits of gaining new information to inform a conservation problem. We also theoretically demonstrate that the value of reducing uncertainty is highest when it is not clear which management action is the most cost efficient. This study will help expand the use of value-of-information analyses in conservation by providing a cost efficiency metric by which to evaluate research or monitoring.

Journal of Applied Ecology↗

USGS2020

Methylmercury exposure in wildlife: A review of the ecological and physiological processes affecting contaminant concentrations and their interpretation

Exposure to methylmercury (MeHg) can result in detrimental health effects in wildlife. With advances in ecological indicators and analytical techniques for measurement of MeHg in a variety of tissues, numerous processes have been identified that can influence MeHg concentrations in wildlife. This review presents a synthesis of theoretical principals and applied information for measuring MeHg exposure and interpreting MeHg concentrations in wildlife. Mercury concentrations in wildlife are the net result of ecological processes influencing dietary exposure combined with physiological processes that regulate assimilation, transformation, and elimination. Therefore, consideration of both physiological and ecological processes should be integrated when formulating biomonitoring strategies. Ecological indicators, particularly stable isotopes of carbon, nitrogen, and sulfur, compound-specific stable isotopes, and fatty acids, can be effective tools to evaluate dietary MeHg exposure. Animal species differ in their physiological capacity for MeHg elimination, and animal tissues can be inert or physiologically active, act as sites of storage, transformation, or excretion of MeHg, and vary in the timing of MeHg exposure they represent. Biological influences such as age, sex, maternal transfer, and growth or fasting are also relevant for interpretation of tissue MeHg concentrations. Wildlife tissues that represent current or near-term bioaccumulation and in which MeHg is the predominant mercury species (such as blood and eggs) are most effective for biomonitoring ecosystems and understanding landscape drivers of MeHg exposure. Further research is suggested to critically evaluate the use of keratinized external tissues to measure MeHg bioaccumulation, particularly for less-well studied wildlife such as reptiles and terrestrial mammals. Suggested methods are provided to effectively use wildlife for quantifying patterns and drivers of MeHg bioaccumulation over time and space, as well as for assessing the potential risk and toxicological effects of MeHg on wildlife.

Science of the Total Environment↗

USGS2025

Foundational principles of an applied cultural evolutionary science for natural resource management and conservation

Culture, as the filter through which people view the world and a key determinant of human behaviour, is central to the practice of natural resource management and conservation. Conservation is intended to moderate the impacts of human cultural modification of the environment, exists as an endeavour because it is culturally valued, and acts largely through policies to encourage or discourage targeted human behaviours. However, culture is not static; as organisms and ecologies evolve, so too does culture exist as a dynamic, interconnected, coevolving element of the social–ecological systems in which management action is situated and implemented. Cultural evolution (CE) offers a valuable theoretical contribution to the scientific understanding of culture, cultural diversity and culture change and has the potential to be harnessed in the applied research and practice of conservation social science. We illustrate the essential principles necessary to grow an applied science of CE for natural resource management and conservation, and identify opportunities for CE to provide valuable information for science-based decision making and help conservation institutions and organizations adapt to the ongoing challenges posed by culture change. This transdisciplinary integration can contribute to improved outcomes across conservation objectives and build more resilient, sustainable social–ecological systems.

Philosophical Transactions of the Royal Society B ↗

USGS2019

Testing theoretical metapopulation conditions with genotypic data from Boreal Chorus Frogs (Pseudacris maculata)

The metapopulation concept has far-reaching implications in ecology and conservation biology. Hanski’s criteria operationally define metapopulations, yet testing them is hindered by logistical and financial constraints inherent to the collection of long-term demographic data. Hence, ecologists and conservationists often assume metapopulation existence for dispersal-limited species that occupy patchy habitats. To advance understanding of metapopulation theory and improve conservation of metapopulations, we used population and landscape genetic tools to develop a methodological framework for evaluating Hanski’s criteria. We used genotypic data (11 microsatellite loci) from a purported metapopulation of Boreal Chorus Frogs ( Pseudacris maculata (Agassiz, 1850)) in Colorado, U.S.A., to test Hanski’s four criteria. We found support for each criterion: (1) significant genetic differentiation between wetlands, suggesting distinct breeding populations; (2) wetlands had small effective population sizes and recent bottlenecks, suggesting populations do not experience long-term persistence; (3) population graphs provided evidence of gene flow between patches, indicating potential for recolonization; and (4) multiscale bottleneck analyses suggest asynchrony, indicating that simultaneous extinction of all populations was unlikely. Our methodological framework provides a logistically and financially feasible alternative to long-term demographic data for identifying amphibian metapopulations.

Colorado↗