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USGS2013

Taming wildlife disease: bridging the gap between science and management

1.Parasites and pathogens of wildlife can threaten biodiversity, infect humans and domestic animals, and cause significant economic losses, providing incentives to manage wildlife diseases. Recent insights from disease ecology have helped transform our understanding of infectious disease dynamics and yielded new strategies to better manage wildlife diseases. Simultaneously, wildlife disease management (WDM) presents opportunities for large-scale empirical tests of disease ecology theory in diverse natural systems. 2.To assess whether the potential complementarity between WDM and disease ecology theory has been realized, we evaluate the extent to which specific concepts in disease ecology theory have been explicitly applied in peer-reviewed WDM literature. 3.While only half of WDM articles published in the past decade incorporated disease ecology theory, theory has been incorporated with increasing frequency over the past 40 years. Contrary to expectations, articles authored by academics were no more likely to apply disease ecology theory, but articles that explain unsuccessful management often do so in terms of theory. 4.Some theoretical concepts such as density-dependent transmission have been commonly applied, whereas emerging concepts such as pathogen evolutionary responses to management, biodiversity–disease relationships and within-host parasite interactions have not yet been fully integrated as management considerations. 5.Synthesis and applications. Theory-based disease management can meet the needs of both academics and managers by testing disease ecology theory and improving disease interventions. Theoretical concepts that have received limited attention to date in wildlife disease management could provide a basis for improving management and advancing disease ecology in the future.

Journal of Applied Ecology↗

USGS2015

Biodiversity and Habitat Markets—Policy, Economic, and Ecological implications of Market-Based Conservation

This report is a primer on market-like and market-based mechanisms designed to conserve biodiversity and habitat. The types of markets and market-based approaches that were implemented or are emerging to benefit biodiversity and habitat in the United States are examined. The central approaches considered in this report include payments for ecosystem services, conservation banks, habitat exchanges, and eco-labels. Based on literature reviews and input from experts and practitioners, the report characterizes each market-based approach including policy context and structure; the theoretical basis for applying market-based approaches; the ecological effectiveness of practices and tools for measuring performance; and the future outlook for biodiversity and habitat markets. This report draws from previous research and serves as a summary of pertinent information associated with biodiversity and habitat markets while providing references to materials that go into greater detail on specific topics.

Circular↗

USGS2026

Ecological diffusion models are still useful in ecology

Context Connecting animal movement across scales—both among spatial scales and between individual and population level processes—is a central theme in ecology, including the sub-fields of movement and landscape ecology. However, most modeling frameworks operate at either the individual (Lagrangian) or population (Eulerian) level. Objectives We aimed to review how a partial differential equation (PDE) known as the ecological diffusion equation (EDE) offers connections among the scales of animal movement and how it can be embedded in commonly used statistical frameworks (e.g., hierarchical models) and fit to multiple types of data, such as count, presence-only, presence-absence, and telemetry data. In doing so, we also highlight emerging and promising avenues of research related to statistical EDE models. Methods We present how the EDE is derived from a simple set of first principles, can be used to represent both individual animal movement and spatiotemporal population dynamics, and can be fit to data by being applied in a hierarchical statistical framework. Finally, we review literature on recent applications and advances related to EDE models. Results In presenting the derivation of the EDE and reviewing recent advances and applications of EDE models, we established that the EDE can enable a mechanistic understanding of animal movement across scales, as well as the integration of multiple types of data. We also identified a number of topics where EDE models can be advanced to gain additional ecological insight. Conclusions Ecological diffusion is a process that emerges naturally from first principles governing animal movement and can be used to model both individual and population-level spatiotemporal dynamics. Statistical EDE models are related to other modeling frameworks, such as species distribution models, occupancy models, and step-selection analysis. The flexibility of hierarchical statistical modeling allows combining different types of data collected on movement and population dynamics, and the theoretical underpinnings of the EDE allow it to connect ecological processes occurring at fine and coarse spatial scales, as well as individual animal movement and population dynamics.

Landscape Ecology↗

USGS2017

Moving forward in circles: Challenges and opportunities in modeling population cycles

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer–resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research.

Ecology Letters↗

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↗

USGS2011

Comparisons of fish species traits from small streams to large rivers

To examine the relations between fish community function and stream size, we classified 429 lotic freshwater fish species based on multiple categories within six species traits: (1) substrate preference, (2) geomorphic preference, (3) trophic ecology, (4) locomotion morphology, (5) reproductive strategy, and (6) stream size preference. Stream size categories included small streams, small, medium, and large rivers, and no size preference. The frequencies of each species trait category were determined for each stream size category based on life history information from the literature. Cluster analysis revealed the presence of covarying groups of species trait categories. One cluster (RUN) included the traits of planktivore and herbivore feeding ecology, migratory reproductive behavior and broadcast spawning, preferences for main-channel habitats, and a lack of preferences for substrate type. The frequencies of classifications for the RUN cluster varied significantly across stream size categories (P = 0.009), being greater for large rivers than for small streams and rivers. Another cluster (RIFFLE) included the traits of invertivore feeding ecology, simple nester reproductive behavior, a preference for riffles, and a preference for bedrock, boulder, and cobble-rubble substrate. No significant differences in the frequency of classifications among stream size categories were detected for the RIFFLE cluster (P = 0.328). Our results suggest that fish community function is structured by large-scale differences in habitat and is different for large rivers than for small streams and rivers. Our findings support theoretical predictions of variation in species traits among stream reaches based on ecological frameworks such as landscape filters, habitat templates, and the river continuum concept. We believe that the species trait classifications presented here provide an opportunity for further examination of fish species' relations to physical, chemical, and biological factors in lotic habitats ranging from small streams to large rivers.

Transactions of the American Fisheries Society↗

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↗