A novel approach to assessing natural resource injury with Bayesian networks.

Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562-573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Risk Analysis Frameworks Used in Biological Control and Introduction of a Novel Bayesian Network Tool.

Classical biological control, the introduction of natural enemies to new environments to control unwanted pests or weeds, is, despite numerous successful examples, associated with rising concerns about unwanted environmental impacts such as population decline of nontarget species. Recognition of these biosafety risks is globally increasing, and prerelease assessments of biological control agents (BCAs) have become more rigorous in many countries. We review the current approaches to risk assessment for BCAs as used in Australasia, Europe, and North America. Traditionally, these assessments focus on providing assurance about the specificity of a proposed BCA, generally via a list of suitable versus nonsuitable hosts determined through laboratory specificity tests (i.e., by determining the BCA's physiological host range). The outcome of interactions of proposed agents in the natural environment can differ from laboratory-based predictions. Potential nontarget host testing may be incomplete, additional ecological barriers under field conditions may limit encounters between BCA and nontargets or reduce attack levels, and BCAs could disperse to habitats beyond those used by the target species and adversely affect nontarget species. We advocate for the adoption of more comprehensive, ecologically-based, probabilistic risk assessment approaches to BCA introductions. An example is provided using a Bayesian network that can integrate information on probabilities and uncertainties of a BCA to spread and establish in new habitats, interact with nontarget species in these habitats, and eventually negatively impact the populations of these nontarget species. Our new model, Biocontrol Adverse Impact Probability Assessment, aims to be incorporated into a structured decision-making framework to support national regulatory authorities.

EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks.

A recent advancement in modeling was the development of quantum Bayesian networks (QBNs). QBNs generally differ from BNs by substituting traditional Bayes calculus in probability tables with the quantum amplification wave functions. QBNs can solve a variety of problems which are unsolvable by, or are too complex for, traditional BNs. These include problems with feedback loops and temporal expansions; problems with non-commutative dependencies in which the order of the specification of priors affects the posterior outcomes; problems with intransitive dependencies constituting the circular dominance of the outcomes; problems in which the input variables can affect each other, even if they are not causally linked (entanglement); problems in which there may be >1 dominant probability outcome dependent on small variations in inputs (superpositioning); and problems in which the outcomes are nonintuitive and defy traditional probability calculus (Parrondo's paradox and the violation of the Sure Thing Principle). I present simple examples of these situations illustrating problems in prediction and diagnosis, and I demonstrate how BN solutions are infeasible, or at best require overly-complex latent variable structures. I then argue that many problems in ecology and evolution can be better depicted with ecological QBN (EcoQBN) modeling. The situations that fit these kinds of problems include noncommutative and intransitive ecosystems responding to suites of disturbance regimes with no specific or single climax condition, or that respond differently depending on the specific sequence of the disturbances (priors). Case examples are presented on the evaluation of habitat conditions for a bat species, representing state-transition models of a boreal forest under disturbance, and the entrainment of auditory signals among organisms. I argue that many current ecological analysis structures-such as state-and-transition models, predator-prey dynamics, the evolution of symbiotic relationships, ecological disturbance models, and much more-could greatly benefit from a QBN approach. I conclude by presenting EcoQBNs as a nascent field needing the further development of the quantum mathematical structures and, eventually, adjuncts to existing BN modeling shells or entirely new software programs to facilitate model development and application.

Using Decision Science for Monitoring Threatened Western Snowy Plovers to Inform Recovery.

Western Snowy Plovers (Charadrius nivosus nivosus) are federally listed under the US Endangered Species Act as Threatened. They occur along the US Pacific coastline and are threatened by habitat loss and destruction and excessive levels of predation and human disturbance. Populations have been monitored since the 1970s for distribution, reproduction, and survival. Since the species was federally listed in 1993 and a recovery plan was approved under the US Fish and Wildlife Service in 2007, recovery actions have resulted in growing populations with increased presence at breeding and wintering sites throughout their Pacific Coast range. This success has created logistical challenges related to monitoring a recovering species and a need for identifying and instituting the best monitoring approach given recovery goals, budgets, and the likelihood of monitoring success. We devised and implemented a structured decision analysis to evaluate nine alternative monitoring strategies. The analysis included inviting plover biologists involved in monitoring to score each strategy according to a suite of performance measures. Using multi-attribute utility theory, we combined scores across the performance measures for each monitoring strategy, and applied weighted utility values to show the implications of tradeoffs and find optimal decisions. We evaluated four scenarios for weighting the monitoring objectives and how risk attitude affects optimal decisions. This resulted in identifying six strategies that best meet recovery needs and were Pareto optimal for cost-effective monitoring. Results were presented to the US Fish and Wildlife Service, responsible for monitoring as well as for consideration to ensure consistent monitoring methods across the species' range. Our use of structured decision-making can be applied to cases of other species once imperiled but now on the road to recovery.

Bayesian decision network modeling for environmental risk management: A wildfire case study.

Environmental decision-making requires an understanding of complex interacting systems across scales of space and time. A range of statistical methods, evaluation frameworks and modeling approaches have been applied for conducting structured environmental decision-making under uncertainty. Bayesian Decision Networks (BDNs) are a useful construct for addressing uncertainties in environmental decision-making. In this paper, we apply a BDN to decisions regarding fire management to evaluate the general efficacy and utility of the approach in resource and environmental decision-making. The study was undertaken in south-eastern Australia to examine decisions about prescribed burning rates and locations based on treatment and impact costs. Least-cost solutions were identified but are unlikely to be socially acceptable or practical within existing resources; however, the statistical approach allowed for the identification of alternative, more practical solutions. BDNs provided a transparent and effective method for a multi-criteria decision analysis of environmental management problems.

Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay.

Understanding habitat associations is vital for conservation of at-risk marsh-endemic wildlife species, particularly those under threat from sea level rise. We modeled environmental and habitat associations of the marsh-endemic, Federally endangered salt marsh harvest mouse (Reithrodontomys raviventris, RERA) and co-occurrence with eight associated small mammal species from annual trap data, 1998-2014, in six estuarine marshes in North San Francisco Bay, California. Covariates included microhabitat metrics of elevation and vegetation species and cover; and landscape metrics of latitude-longitude, distance to anthropogenic features, and habitat patch size. The dominant cover was pickleweed (Salicornia pacifica) with 86% mean cover and 37 cm mean height, and bare ground with about 10% mean cover. We tested 38 variants of Bayesian network (BN) models to determine covariates that best account for presence of RERA and of all nine small mammal species. Best models had lowest complexity and highest classification accuracy. Among RERA presence models, three best BN models used covariates of latitude-longitude, distance to paved roads, and habitat patch size, with 0% error of false presence, 20% error of false nonpresence, and 20% overall error. The all-species presence models suggested that within the pickleweed marsh environment, RERA are mostly habitat generalists. Accounting for presence of other species did not improve prediction of RERA. Habitat attributes compared between RERA and the next most frequently captured species, California vole (Microtus californicus), suggested substantial habitat overlap, with RERA habitat being somewhat higher in marsh elevation, greater in percent cover of the dominant plant species, closer to urban areas, further from agricultural areas, and, perhaps most significant, larger in continuous size of marsh patch. Findings will inform conservation management of the marsh environment for RERA by identifying best microhabitat elements, landscape attributes, and adverse interspecific interactions.

Conservation planning for species recovery under the Endangered Species Act: A case study with the Northern Spotted Owl.

The northern spotted owl (Strix occidentalis caurina) was listed as threatened under the U.S. Endangered Species Act (ESA) in 1990. We applied modern spatial conservation theory and models to evaluate several candidate critical habitat networks, and sought an efficient conservation solution that encompassed the highest value lands for spotted owl recovery rather than maximizing the total area of potential critical habitat. We created a map of relative habitat suitability, which served as input to the spatial conservation prioritization program Zonation. We used the spatially-explicit individual-based population model HexSim to estimate and compare simulated spotted owl population outcomes among a suite of candidate critical habitat networks that varied in size and spatial arrangement under alternative scenarios of future habitat suitability and barred owl (S. varia) effects. We evaluated simulated spotted owl population outcomes, including total population size, and extinction and quasi-extinction likelihoods for 108 combinations of candidate critical habitat networks by habitat change by barred owl scenarios, both range-wide and within 11 distinct portions of the owl's range. Barred owl encounter rates and the amount and suitability of habitat had substantial effects on simulated spotted owl populations. When barred owl encounter rates were high, changes in the amount and suitability of habitat had minimal impacts on population performance. Under lowered barred owl encounter rates, candidate critical habitat networks that included most existing high suitability habitat supported a high likelihood of long-term population persistence. Barred owls are currently the primary driving force behind poor population performance of NSOs; however, our models demonstrated that a sufficient area of high suitability habitat remains essential for recovery when effects of barred owls can be reduced. The modeling approach we employed is sufficiently flexible to incorporate new information about spotted owls as it becomes available and could likely be applied to conservation planning for other species.

Applying circuit theory and landscape linkage maps to reintroduction planning for California Condors.

Conservation practitioners are increasingly looking to species translocations as a tool to recover imperiled taxa. Quantitative predictions of where animals are likely to move when released into new areas would allow managers to better address the social, institutional, and ecological dimensions of conservation translocations. Using >5 million California condor (Gymnogyps californianus) occurrence locations from 75 individuals, we developed and tested circuit-based models to predict condor movement away from release sites. We found that circuit-based models of electrical current were well calibrated to the distribution of condor movement data in southern and central California (continuous Boyce Index = 0.86 and 0.98, respectively). Model calibration was improved in southern California when additional nodes were added to the circuit to account for nesting and feeding areas, where condor movement densities were higher (continuous Boyce Index = 0.95). Circuit-based projections of electrical current around a proposed release site in northern California comported with the condor's historical distribution and revealed that, initially, condor movements would likely be most concentrated in northwestern California and southwest Oregon. Landscape linkage maps, which incorporate information on landscape resistance, complement circuit-based models and aid in the identification of specific avenues for population connectivity or areas where movement between populations may be constrained. We found landscape linkages in the Coast Range and the Sierra Nevada provided the most connectivity to a proposed reintroduction site in northern California. Our methods are applicable to conservation translocations for other species and are flexible, allowing researchers to develop multiple competing hypotheses when there are uncertainties about landscape or social attractants, or uncertainties in the landscape conductance surface.

Predicting forest insect flight activity: A Bayesian network approach.

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.

Impacts of Human Recreation on Brown Bears (Ursus arctos): A Review and New Management Tool.

Increased popularity of recreational activities in natural areas has led to the need to better understand their impacts on wildlife. The majority of research conducted to date has focused on behavioral effects from individual recreations, thus there is a limited understanding of the potential for population-level or cumulative effects. Brown bears (Ursus arctos) are the focus of a growing wildlife viewing industry and are found in habitats frequented by recreationists. Managers face difficult decisions in balancing recreational opportunities with habitat protection for wildlife. Here, we integrate results from empirical studies with expert knowledge to better understand the potential population-level effects of recreational activities on brown bears. We conducted a literature review and Delphi survey of brown bear experts to better understand the frequencies and types of recreations occurring in bear habitats and their potential effects, and to identify management solutions and research needs. We then developed a Bayesian network model that allows managers to estimate the potential effects of recreational management decisions in bear habitats. A higher proportion of individual brown bears in coastal habitats were exposed to recreation, including photography and bear-viewing than bears in interior habitats where camping and hiking were more common. Our results suggest that the primary mechanism by which recreation may impact brown bears is through temporal and spatial displacement with associated increases in energetic costs and declines in nutritional intake. Killings in defense of life and property were found to be minimally associated with recreation in Alaska, but are important considerations in population management. Regulating recreation to occur predictably in space and time and limiting recreation in habitats with concentrated food resources reduces impacts on food intake and may thereby, reduce impacts on reproduction and survival. Our results suggest that decisions managers make about regulating recreational activities in time and space have important consequences for bear populations. The Bayesian network model developed here provides a new tool for managers to balance demands of multiple recreational activities while supporting healthy bear populations.

Greenhouse gas mitigation can reduce sea-ice loss and increase polar bear persistence.

On the basis of projected losses of their essential sea-ice habitats, a United States Geological Survey research team concluded in 2007 that two-thirds of the world's polar bears (Ursus maritimus) could disappear by mid-century if business-as-usual greenhouse gas emissions continue. That projection, however, did not consider the possible benefits of greenhouse gas mitigation. A key question is whether temperature increases lead to proportional losses of sea-ice habitat, or whether sea-ice cover crosses a tipping point and irreversibly collapses when temperature reaches a critical threshold. Such a tipping point would mean future greenhouse gas mitigation would confer no conservation benefits to polar bears. Here we show, using a general circulation model, that substantially more sea-ice habitat would be retained if greenhouse gas rise is mitigated. We also show, with Bayesian network model outcomes, that increased habitat retention under greenhouse gas mitigation means that polar bears could persist throughout the century in greater numbers and more areas than in the business-as-usual case. Our general circulation model outcomes did not reveal thresholds leading to irreversible loss of ice; instead, a linear relationship between global mean surface air temperature and sea-ice habitat substantiated the hypothesis that sea-ice thermodynamics can overcome albedo feedbacks proposed to cause sea-ice tipping points. Our outcomes indicate that rapid summer ice losses in models and observations represent increased volatility of a thinning sea-ice cover, rather than tipping-point behaviour. Mitigation-driven Bayesian network outcomes show that previously predicted declines in polar bear distribution and numbers are not unavoidable. Because polar bears are sentinels of the Arctic marine ecosystem and trends in their sea-ice habitats foreshadow future global changes, mitigating greenhouse gas emissions to improve polar bear status would have conservation benefits throughout and beyond the Arctic.

Protecting rare, old-growth, forest-associated species under the Survey and Manage Program guidelines of the Northwest Forest Plan.

The Survey and Manage Program of the Northwest Forest Plan (NWFP) represents an unparalleled attempt to protect rare, little-known species associated with late-successional and old-growth forests on more than 9.7 million ha of federal lands. Approximately 400 species of amphibians, bryophytes, fungi, lichens, mollusks, vascular plants, arthropod functional groups, and one mammal were listed under this program because viability evaluations indicated the plan's network of reserve land allocations might not sustain the species over time. The program's standards and guidelines used an adaptive approach, protecting known sites and collecting new information to address concerns for species persistence and to develop management strategies. Since implementation in 1994, approximately 68,000 known sites have been recorded at an expense of several tens of millions of dollars. New knowledge from surveys reduced concern for nearly 100 species, and they were removed from the protection list. Although successful in protecting hundreds of rare species not typically considered in most conservation programs, some of the enacted conservation measures created conflicts in meeting other management objectives of the plan, particularly timber harvest. The program accrued important gains in knowledge, reduced uncertainty about conservation of a number of species, and developed new methods of species inventory that will be useful in future management planning and implementation at many scales. The program, however was not completed because of changes in land-management philosophy. Ongoing litigation regarding its termination and potential changes to the plan cast further uncertainty on how the original goal of maintaining persistence of late-successional and old-growth species will be met and measured. The outcomes, controversies, and management frustrations of the program exemplify the inherent difficulties in balancing broad, regional conservation goals with social and economic goals of the NWFP Defining acceptable trade-offs to reach that balance and developing practical conservation solutions remain challenges for the science and management communities. Lessons learned from the program provide a valuable biological and managerial reference to benefit future discussion on meeting those challenges.

Definición de redes de áreas protegidas forestales: Un manual para la delimitación de áreas protegidas forestales en los bosques manejados de las tierras bajas de Bolivia

El propósito del presente manual es brindar una serie de criterios a ser utilizados en la identificación y delimitación de redes de áreas protegidas de ambientes forestales para la conservación de la fauna silvestre, dentro de los bosques manejados de las tierras bajas de Bolivia. El manual está dirigido a las personas encargadas del manejo y planificación forestal. Este manual también incluye un anexo en el que se listan consideraciones adicionales para la conservación de la fauna silvestre en la concesión forestal Tarumá, en el departamento de Santa Cruz, Dicho anexo brinda un ejemplo de la forma en que las condiciones adicionales y más específicas a cada sitio pueden complementar las directrices generales