On formally integrating science and policy: walking the walk

Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases

Emerging diseases can have devastating consequences for wildlife and require a rapid response. A critical first step towards developing appropriate management is identifying the etiology of the disease, which can be difficult to determine, particularly early in emergence. Gathering and synthesizing existing information about potential disease causes, by leveraging expert knowledge or relevant existing studies, provides a principled approach to quickly inform decision-making and management efforts. Additionally, updating the current state of knowledge as more information becomes available over time can reduce scientific uncertainty and lead to substantial improvement in the decision-making process and the application of management actions that incorporate and adapt to newly acquired scientific understanding. Here we present a rapid prototyping method for quantifying belief weights for competing hypotheses about the etiology of disease using a combination of formal expert elicitation and Bayesian hierarchical modeling. We illustrate the application of this approach for investigating the etiology of stony coral tissue loss disease (SCTLD) and discuss the opportunities and challenges of this approach for addressing emergent diseases. Lastly, we detail how our work may apply to other pressing management or conservation problems that require quick responses. We found the rapid prototyping methods to be an efficient and rapid means to narrow down the number of potential hypotheses, synthesize current understanding, and help prioritize future studies and experiments. This approach is rapid by providing a snapshot assessment of the current state of knowledge. It can also be updated periodically (e.g., annually) to assess changes in belief weights over time as scientific understanding increases. Synthesis and applications: The rapid prototyping approaches demonstrated here can be used to combine knowledge from multiple experts and/or studies to help with fast decision-making needed for urgent conservation issues including emerging diseases and other management problems that require rapid responses. These approaches can also be used to adjust belief weights over time as studies and expert knowledge accumulate and can be a helpful tool for adapting management decisions.

Evaluation Options for Wildlife Management and Strengthening of Causal Inference

Wildlife management aims to halt and then reverse the decline of threatened species, to sustainably harvest populations, and to control undesirable impacts of some species. We describe a unifying framework of three feasible options for evaluation of wildlife management, including conservation, and discuss their relative strengths of statistical and causal inference. The first option is trends in abundance, which can provide strong evidence a change has occurred (statistical inference) but does not identify the causes. The second option assesses population outcomes relative to management efforts, which provides strong evidence of cause and effect (causal inference) but not the trend. The third option combines the first and second options and therefore provides both statistical and causal inferences in an adaptive framework. We propose that wildlife management needs to explicitly use causal criteria and inference to complement adaptive management. We recommend incorporating these options into management plans.

Retrospective comparisons of competing demographic models give clarity from "messy" management on a Scottish grouse moor

Retrospective comparison of predictive models that describe competing hypotheses regarding system function can shed light on regulatory mechanisms within the framework of adaptive resource management. We applied this approach to a 28-year study of red grouse (Lagopus lagopus scotica) in Scotland, with the aims of reducing uncertainty regarding important drivers of grouse population dynamics, and of evaluating the efficacy of using seasonal versus annual model assessments. We developed three sets of models that predicted pre-breeding and post-breeding grouse density, matching the timing of grouse counts on the ground. We updated conditions and management through time in the spirit of a real-time, adaptive management program and used a Bayesian model weight updating process to compare model predictions with empirical grouse densities. The first two model sets involved single annual updates from either pre-breeding or post-breeding counts; the third set was updated twice a year. Each model set comprised seven models representing increasingly complex hypotheses regarding potentially important drivers of grouse: the baseline model included weather and parasite effects on productivity, shooting losses and density-dependent overwinter survival; subsequent models incorporated the effect of habitat gain/loss (HAB), control of non-protected predators (NPP) and predation by protected hen harriers (Circus cyaneus, HH) and buzzards (Buteo buteo, BZ). The weight of evidence was consistent across model sets, settling within 10 years on the harrier (NPP + HH), buzzard (NPP + HH + BZ) and buzzard + habitat (NPP + HH + BZ + HAB) models, and downgrading the baseline + habitat, non-protected predator, and non-protected predator + habitat models. By the end of the study only the buzzard and buzzard + habitat models retained substantial weights, emphasizing the dynamical complexity of the system. Habitat inclusion failed to improve model predictions, implying that over the period of this study habitat quantity was unimportant in determining grouse abundance. Comparing annually and biannually assessed model sets, the main difference was in the baseline model, whose weight increased or remained stable when assessed annually, but collapsed when assessed biannually. Our adaptive modeling approach is suitable for many ecological situations in which a complex interplay of factors makes experimental manipulation difficult.

A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference. In this study, we used Bayesian hierarchical models and a 43-year capture-mark-recovery dataset from 404,241 female mallards Anas platyrhynchos released in the North American midcontinent to estimate mallard demographic parameters. Furthermore, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters. We demonstrate that density dependence, habitat conditions and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.

Iterative model predictions for wildlife populations impacted by rapid climate change

To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005-2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach.

Reconstructing population dynamics of a threatened marine mammal using multiple data sets

Models of marine mammal population dynamics have been used extensively to predict abundance. A less common application of these models is to reconstruct historical population dynamics, filling in gaps in observation data by integrating information from multiple sources. We developed an integrated population model for the Florida manatee (Trichechus manatus latirostris) to reconstruct its population dynamics in the southwest region of the state over the past 20 years. Our model improved precision of key parameter estimates and permitted inference on poorly known parameters. Population growth was slow (averaging 1.02; 95% credible interval 1.01-1.03) but not steady, and an unusual mortality event in 2013 led to an estimated net loss of 332 (217-466) manatees. Our analyses showed that precise estimates of abundance could be derived from estimates of vital rates and a few input estimates of abundance, which may mean costly surveys to estimate abundance don't need to be conducted as frequently. Our study also shows that retrospective analyses can be useful to: (1) model the transient dynamics of age distribution; (2) assess and communicate the conservation status of wild populations; and (3) improve our understanding of environmental effects on population dynamics and thus enhance our ability to forecast.

Seasonal habitat suitability models for a threatened species: the Gunnison sage-grouse

Abstract ContextThe Gunnison sage-grouse (Centrocercus minimus) has experienced range-wide declines and has been listed as Threatened by the USA Fish and Wildlife Service to receive protections under the USA Endangered Species Act. A draft Recovery Plan was recently completed. No seasonal habitat models have been developed for the small isolated populations. AimsTo develop a habitat suitability model that was collaboratively developed between modellers and conservation practitioners to predict the probability of use by Gunnison sage-grouse during the breeding and summer seasons in designated occupied critical habitat, and extrapolate to adjacent designated unoccupied critical habitat. MethodsWe captured, marked and tracked Gunnison sage-grouse in nine different studies spanning 25 years. We used a suite of biotic, abiotic and vegetation local-level and population-scale covariates in a use-available resource selection function to develop models that predict the probability of use by Gunnison sage-grouse. Key resultsWe used 9140 Gunnison sage-grouse locations from 406 individual birds to develop nine resource selection models for occupied habitat and extrapolated model predictions to adjacent unoccupied critical habitat in five small isolated Gunnison sage-grouse populations. A majority of our models validated well. ConclusionsWe report the first two-season resource use-based habitat suitability models for five of six small isolated Gunnison sage-grouse populations. Because of the unique habitat use by Gunnison sage-grouse in each population, we recommend that resource managers strategically target management actions in individual populations and avoid ‘one-size-fits-all’ habitat management prescriptions. ImplicationsOur models will assist managers in the identification of seasonal habitats within populations to target management actions for Gunnison sage-grouse recovery.

End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers

Sustainable management of wildlife populations can be aided by building models that both identify current drivers of natural dynamics and provide near-term predictions of future states. We employed a Strategic Foresight Protocol (SFP) involving stakeholders to decide the purpose and structure of a dynamic state-space model for the population dynamics of the Willow Ptarmigan, a popular game species in Norway. Based on local knowledge of stakeholders, it was decided that the model should include food web interactions and climatic drivers to provide explanatory predictions. Modeling confirmed observations from stakeholders that climate change impacts Ptarmigan populations negatively through intensified outbreaks of insect defoliators and later onset of winter. Stakeholders also decided that the model should provide anticipatory predictions. The ability to forecast population density ahead of the harvest season was valued by the stakeholders as it provides the management extra time to consider appropriate harvest regulations and communicate with hunters prior to the hunting season. Overall, exploring potential drivers and predicting short-term future states, facilitate collaborative learning and refined data collection, monitoring designs, and management priorities. Our experience from adapting a SFP to a management target with inherently complex dynamics and drivers of environmental change, is that an open, flexible, and iterative process, rather than a rigid step-wise protocol, facilitates rapid learning, trust, and legitimacy.

Accumulating evidence in ecology: Once is not enough

Many published studies in ecological science are viewed as stand-alone investigations that purport to provide new insights into how ecological systems behave based on single analyses. But it is rare for results of single studies to provide definitive results, as evidenced in current discussions of the "reproducibility crisis" in science. The key step in science is the comparison of hypothesis-based predictions with observations, where the predictions are typically generated by hypothesis-specific models. Repeating this step allows us to gain confidence in the predictive ability of a model, and its corresponding hypothesis, and thus to accumulate evidence and eventually knowledge. This accumulation may occur via an ad hoc approach, via meta-analyses, or via a more systematic approach based on the anticipated evolution of an information state. We argue the merits of this latter approach, provide an example, and discuss implications for designing sequences of studies focused on a particular question. We conclude by discussing current data collection programs that are preadapted to use this approach and argue that expanded use would increase the rate of learning in ecology, as well as our confidence in what is learned.

Effects of lead from ammunition on birds and other wildlife: A review and update

Poisoning of wild birds following ingestion of lead from ammunition has long been recognised and considerable recent research has focused on terrestrial birds, including raptors and scavengers. This paper builds upon previous reviews and finds that both the number of taxa affected and geographical spread of cases has increased. Some lead may also be absorbed from embedded ammunition fragments in injured birds which risk sub-lethal and welfare effects. Some papers suggest inter-specific differences in sensitivity to lead, although it is difficult to disentangle these from other factors that influence effect severity. Sub-lethal effects have been found at lower blood lead concentrations than previously reported, suggesting that previous effect-level 'thresholds' should be abandoned or revised. Lead poisoning is estimated to kill a million wildfowl a year in Europe and cause sub-lethal poisoning in another ≥ 3 million. Modelling and correlative studies have supported the potential for population-level effects of lead poisoning in wildfowl, terrestrial birds, raptors and scavengers.

Dynamic wildlife occupancy models using automated acoustic monitoring data

Automated acoustic monitoring of wildlife has been used to characterize populations of sound-producing species across large spatial scales. However, false negatives and false positives produced by automated detection systems can compromise the utility of these data for researchers and land managers, particularly for research programs endeavoring to describe colonization and extinction dynamics that inform land use decision-making. To investigate the suitability of automated acoustic monitoring for dynamic occurrence models, we simulated underlying occurrence dynamics, calling patterns, and the automated acoustic detection process for a hypothetical species under a range of scenarios. We investigated an automated species detection aggregation method that considered a suite of options for creating encounter histories. From these encounter histories, we generated parameter estimates and computed bias for occurrence, colonization, and extinction rates using a dynamic occupancy modeling framework that accounts for false positives via small amounts of manual confirmation. We were able to achieve relatively unbiased estimates for all three state parameters under all scenarios, even when the automated detection system was simulated to be poor, given particular encounter history aggregation choices. However, some encounter history aggregation choices resulted in unreliable estimates; we provide caveats for avoiding these scenarios. Given specific choices during the detection aggregation process, automated acoustic monitoring data may provide an effective means for tracking species occurrence, colonization, and extinction patterns through time, with the potential to inform adaptive management at multiple spatial scales.

Finding Common Ground between Adaptive Management and Evidence-Based Approaches to Biodiversity Conservation

Adaptive management (AM) and evidence-based conservation (EBC) have emerged as major decision-making frameworks for conservation management. AM deals with complexity and the importance of local context in making conservation decisions under conditions of high variability, uncertainty, and rapid environmental and social change. EBC seeks for generality from empirical data and aims to develop and enhance best practice. The goal of this review is to explore opportunities for finding common ground between AM and EBC. We propose a framework for distinguishing the subset of conservation problems that are amenable to an evidence-based approach, based on levels of uncertainty, complexity, and social agreement. We then suggest ways for combining multiple lines of evidence and developing greater opportunities for iteration and co-learning in EBC.

Ecological research and environmental management: We need different interfaces based on different knowledge types

The role of ecological science in environmental management has been discussed by many authors who recognize that there is a persistent gap between ecological science and environmental management. Here we develop theory through different perspectives based on knowledge types, research categories and research-management interface types, which we combine into a common framework. To draw out insights for bridging this gap, we build our case by:We point out the complementarities as well as the specificities and limitations of the different types of ecological research, ecological knowledge and research-management interfaces, which is of major importance for environmental management and research policies.

Value of sample information in dynamic, structurally uncertain resource systems

Few if any natural resource systems are completely understood and fully observed. Instead, there almost always is uncertainty about the way a system works and its status at any given time, which can limit effective management. A natural approach to uncertainty is to allocate time and effort to the collection of additional data, on the reasonable assumption that more information will facilitate better understanding and lead to better management. But the collection of more data, either through observation or investigation, requires time and effort that often can be put to other conservation activities. An important question is whether the use of limited resources to improve understanding is justified by the resulting potential for improved management. In this paper we address directly a change in value from new information collected through investigation. We frame the value of information in terms of learning through the management process itself, as well as learning through investigations that are external to the management process but add to our base of understanding. We provide a conceptual framework and metrics for this issue, and illustrate them with examples involving Florida scrub-jays (Aphelocoma coerulescens).

Evaluating wildlife management by using principles of applied ecology: case studies and implications

Context The broad concepts and generalisations that guide conduct of applied ecology, including wildlife management, have been reviewed and synthesised recently into 22 prescriptive and three empirical principles. Aims The aim of this study was to use these principles to evaluate three on-ground wildlife management programs and assess the utility of the principles themselves. Key results Case studies of long-term management of national park biodiversity impacted by feral pigs (Sus scrofa), and of conservation and harvest of red kangaroos (Macropus rufus) and mallards (Anas platyrhnchos), were selected to provide a representative range of management objectives, spatial scales and land tenures, and to include both native and introduced species. Management documents and a considerable scientific literature were available for all three programs. The results highlight similarities and differences among management activities and demonstrate the 25 principles to differing degrees. Most of the prescriptive principles were demonstrated in both the management and the scientific literature in all three programs, but almost no use was made of the three empirical principles. We propose that use of the prescriptive principles constitutes evidence that these programs meet both societal and scientific expectations. However, the limited use of the empirical principles shows gaps in the three programs. Conclusions The results suggest that evaluating other wildlife management programs against the principles of applied ecology is worthwhile and could highlight aspects of those programs that might otherwise be overlooked. Little use was made of the empirical principles, but the the Effort–outcomes principle in particular provides a framework for evaluating management programs. Implications The effort–outcomes relationship should be a focus of future applied research, and both prescriptive and empirical principles should be integrated into wildlife management programs.

Economic assessment of wild bird mortality induced by the use of lead gunshot in European wetlands

In European wetlands, at least 40 bird species are exposed to the risk of lead poisoning caused by ingestion of spent lead gunshot. Adopting a methodology developed in North America, we estimated that about 700,000 individuals of 16 waterbird species die annually in the European Union (EU) (6.1% of the wintering population) and one million in whole Europe (7.0%) due to acute effects of lead poisoning. Furthermore, threefold more birds suffer sub-lethal effects. We assessed the economic loss due to this lead-induced mortality of these 16 species by calculating the costs of replacing lethally poisoned wild birds by releasing captive-bred ones. We assessed the cost of buying captive-bred waterbirds for release from market surveys and calculated how many captive-bred birds would have to be released to compensate for the loss, taking into account the high mortality rate of captive birds (72.7%) in the months following release into the wild. Following this approach, the annual cost of waterbird mortality induced by lead shot ingestion is estimated at 105 million euros per year in the EU countries and 142 million euros in the whole of Europe. An alternative method, based upon lost opportunities for hunting caused by deaths due to lead poisoning, gave similar results of 129 million euros per year in the EU countries and 185 million euros per year in the whole of Europe. For several reasons these figures should be regarded as conservative. Inclusion of deaths of species for which there were insufficient data and delayed deaths caused indirectly by lead poisoning and effects on reproduction would probably increase the estimated losses substantially. Nevertheless, our results suggest that the benefits of a restriction on the use of lead gunshot over wetlands could exceed the cost of adapting to non-lead ammunition.

Mitigating amphibian chytridiomycoses in nature

Amphibians across the planet face the threat of population decline and extirpation caused by the disease chytridiomycosis. Despite consensus that the fungal pathogens responsible for the disease are conservation issues, strategies to mitigate their impacts in the natural world are, at best, nascent. Reducing risk associated with the movement of amphibians, non-amphibian vectors and other sources of infection remains the first line of defence and a primary objective when mitigating the threat of disease in wildlife. Amphibian-associated chytridiomycete fungi and chytridiomycosis are already widespread, though, and we therefore focus on discussing options for mitigating the threats once disease emergence has occurred in wild amphibian populations. All strategies have shortcomings that need to be overcome before implementation, including stronger efforts towards understanding and addressing ethical and legal considerations. Even if these issues can be dealt with, all currently available approaches, or those under discussion, are unlikely to yield the desired conservation outcome of disease mitigation. The decision process for establishing mitigation strategies requires integrated thinking that assesses disease mitigation options critically and embeds them within more comprehensive strategies for the conservation of amphibian populations, communities and ecosystems.This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'.

Coping with the loss of large, energy-dense prey: a potential bottleneck for Weddell Seals in the Ross Sea

Extraction of Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea began in 1997, following a management plan that targets the largest fish with a goal of reducing the spawning biomass by 50% over 35 yr. We investigate the potential long-term consequences of the reduced availability of this prey for Weddell seals (Leptonychotes weddellii). Energy demands in seals are acute, especially immediately following lactation, when females must recover substantial mass and cope with molting costs. We tested the hypothesis that toothfish are critically important for adult female seals during this period. Toothfish body mass is three orders of magnitude greater, and its energy density nearly double that of the most common seal prey, Antarctic silverfish (Pleuragramma antarcticum). Reduction or elimination of toothfish consumption could impair a female's ability to sufficiently recover and successfully produce a pup in the following pupping season. Our goals are to (1) illustrate mechanisms and conditions whereby toothfish depletion might plausibly affect seal population trends; (2) identify measurable parameters of the seals' ecology that may help better understand the potential negative impact of toothfish depletion on seal populations; and (3) promote a precautionary management approach for the fishery that includes monitoring of seal populations We constructed a set of inter-linked models of seal diving behavior, physiological condition, and demography based on existing information. We evaluate the effect of the following factors on seal mass recovery and intrinsic population growth rates: fishery depletion rate, daily diving limits, probability of a successful dive, and body mass recovery target. We show that loss of toothfish has the greatest potential impact on seal populations' growth rate. Under some scenarios, populations may decrease at >10% per year. Critical parameters to better understand fishery impacts include prevalence and size of toothfish in the seals' diet; the relationship between diet and the rate of mass recovery; and female breeding propensity in relation to body condition at the end of the molting period. Our results lend support to concerns about the potential negative impact of toothfish extraction in the Ross Sea; and to advocate for a precautionary management approach by the fishery.

Human judgment vs. quantitative models for the management of ecological resources

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1-3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time.