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A range of mathematical models has been developed to infer whether a species is extinct based on a sighting record. Although observations have variable reliability, current methods for detecting extinction do not differentiate observation qualities. A more suitable approach would consider certain and uncertain sightings throughout the sighting period. We consider a small population system, meaning we assume sighting rates are constant and the population is not declining. Based on such an assumption, we develop a Bayesian method that assumes that certain and uncertain sightings occur independently and at uniform rates. These two types of sightings are connected by a common extinction date. Several rates of false sightings can be calculated to differentiate between observation types. Prior rates of false and true sightings, as well as a prior probability that the species is extant, are included. The model is implemented in OpenBugs, which uses Markov chain Monte Carlo (MCMC). Based on records of variable reliability, we estimate the probability that the following species are extinct: Caribbean seal Monachus tropicalis, grey, black-footed ferret Mustela nigripes, Audubon & Bachman, greater stick-nest rat Leporillus conditor, Sturt, and lesser stick-nest rat Leporillus apicalis, Gould. As further examples, Birdlife International provided the sighting records for the Alaotra grebe Tachybaptus rufolavatus, Delacour, Jamaica petrel Pterodroma caribbaea, Carte, and Pohnpei mountain starling Aplonis pelzelni, Finsch, with prior probabilities for extinction. The results are compared with existing methods, which ignore uncertain sightings. We find that including uncertain sightings can considerably change the probability that the species is extant, in either direction. However, in our examples, including the quality of the uncertain sighting made little difference. When we ignore uncertain sightings, our results agree with existing methods, especially when the last sightin

Policy documents advocate that managers should keep their options open while planning to protect coastal ecosystems from climate-change impacts. However, the actual costs and benefits of maintaining flexibility remain largely unexplored, and alternative approaches for decision making under uncertainty may lead to better joint outcomes for conservation and other societal goals. For example, keeping options open for coastal ecosystems incurs opportunity costs for developers. We devised a decision framework that integrates these costs and benefits with probabilistic forecasts for the extent of sea-level rise to find a balance between coastal ecosystem protection and moderate coastal development. Here, we suggest that instead of keeping their options open managers should incorporate uncertain sea-level rise predictions into a decision-making framework that evaluates the benefits and costs of conservation and development. In our example, based on plausible scenarios for sea-level rise and assuming a risk-neutral decision maker, we found that substantial development could be accommodated with negligible loss of environmental assets. Characterization of the Pareto efficiency of conservation and development outcomes provides valuable insight into the intensity of trade-offs between development and conservation. However, additional work is required to improve understanding of the consequences of alternative spatial plans and the value judgments and risk preferences of decision makers and stakeholders. Minimizando el Costo de Mantener Opciones Abiertas para la Conservacion en un Clima Cambiante Resumen Los documentos de politica abogan que los administradores deben mantener sus opciones abiertas mientras planean proteger a los ecosistemas costeros de los impactos del cambio climatico. Sin embargo, el beneficio de mantener la flexibilidad permanece en su mayoria sin explorar y los acercamientos alternativos para la toma de decisiones bajo incertidumbre pueden llevar a mejores

Decisions affecting the management of natural resources in agricultural landscapes are influenced by both social and ecological factors. Models that integrate these factors are likely to better predict the outcomes of natural resource management decisions

In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD's claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.

[Aim] Species often remain undetected at sites where they are present. However, the impact of imperfect detection on species distribution models (SDMs) is not fully appreciated. In this paper we evaluate the influence of imperfect detection on the calibration and discrimination capacity of SDMs. We compare the performance of three types of SDMs: (1) a technique based on presence–absence data, (2) a technique based on presence–background data, and (3) a technique based on detection/non-detection data that accounts for imperfect detection., [Innovation] We use simulations to evaluate the impacts of imperfect detection in SDMs. This allows us to assess model performance with respect to the true objective of the models: the estimation of species distributions. We study a range of scenarios of occupancy and detection based on ecologically plausible environmental relationships and identify the circumstances in which imperfect detection affects model calibration and discrimination. We show that imperfect detection can substantially reduce the inferential and predictive accuracy of presence–absence and presence–background methods that do not account for detectability. While calibration is always affected, the influence on discrimination depends on the relationship of detectability and environmental variables., [Main conclusions] The performance of a model should be assessed with respect to its objectives. Comparative studies that intend to assess the performance of an SDM by evaluating its ability to predict detections rather than presences fail to reveal the benefits of accounting for detectability. Disregarding imperfect detection can have severe consequences for SDM performance, and hence for the estimation of species distributions. To date, this issue has been largely ignored in the SDM literature. Simultaneously modelling occupancy and detection does not necessarily require a greater sampling effort, but rather that data are collected so that they are informative about detectability. We recommend that consideration of imperfect detection become standard practice for species distribution modelling.

Population viability analyses (PVAs) contribute to conservation theory, policy, and management. Most PVAs focus on single species within a given landscape and address a specific problem. This specificity often is reflected in the organization of published PVA descriptions. Many lack structure, making them difficult to understand, assess, repeat, or use for drawing generalizations across PVA studies. In an assessment comparing published PVAs and existing guidelines, we found that model selection was rarely justified; important parameters remained neglected or their implementation was described vaguely; limited details were given on parameter ranges, sensitivity analysis, and scenarios; and results were often reported too inconsistently to enable repeatability and comparability. Although many guidelines exist on how to design and implement reliable PVAs and standards exist for documenting and communicating ecological models in general, there is a lack of organized guidelines for designing, applying, and communicating PVAs that account for their diversity of structures and contents. To fill this gap, we integrated published guidelines and recommendations for PVA design and application, protocols for documenting ecological models in general and individual-based models in particular, and our collective experience in developing, applying, and reviewing PVAs. We devised a comprehensive protocol for the design, application, and communication of PVAs (DAC-PVA), which has 3 primary elements. The first defines what a useful PVA is; the second element provides a workflow for the design and application of a useful PVA and highlights important aspects that need to be considered during these processes; and the third element focuses on communication of PVAs to ensure clarity, comprehensiveness, repeatability, and comparability. Thereby, DAC-PVA should strengthen the credibility and relevance of PVAs for policy and management, and improve the capacity to generalize PVA findings acro

Summary for Policy Makers Australia’s biodiversity is threatened by climate change, but we currently know little about the scale of the threat or how to deploy on ground conservation actions to protect biodiversity against the changes expected. In this project we predict the impacts of climate change for threatened species and delineate the best options for climate adaptation for all these species collectively via protecting and restoring their habitat. For 504 of Australia’s currently threatened species we predict their distributional responses to climate change, under three climate change scenarios of increasing severity: early mitigation, delayed mitigation and business-as-usual. We then simulate the optimal placement of new protected areas and where necessary, restoration of critical habitat for those species most affected by a changing climate, taking into account variation in the costs and benefits of taking action in different places. We measured the benefits of protecting and restoring habitat by considering the long-term availability and quality of habitat for threatened species as climate changes. We undertook a state-of-the-art multi-action optimisation that accounts for spatial and temporal habitat connectivity under climate change. The scale of the prioritisation analysis implemented here is unprecedented in the conservation literature, and is only possible because of recent advances in software sophistication and parallel computer processing power.

Conservation monitoring programs are critical for identifying many elements of species ecology and for detecting changes in populations. However, without articulating how monitoring information will trigger relevant conservation actions, programs that mon

A range of mathematical models has been developed to infer whether a species is extinct based on a sighting record. Although observations have variable reliability, current methods for detecting extinction do not differentiate observation qualities. A more suitable approach would consider certain and uncertain sightings throughout the sighting period. We consider a small population system, meaning we assume sighting rates are constant and the population is not declining. Based on such an assumption, we develop a Bayesian method that assumes that certain and uncertain sightings occur independently and at uniform rates. These two types of sightings are connected by a common extinction date. Several rates of false sightings can be calculated to differentiate between observation types. Prior rates of false and true sightings, as well as a prior probability that the species is extant, are included. The model is implemented in OpenBugs, which uses Markov chain Monte Carlo (MCMC). Based on records of variable reliability, we estimate the probability that the following species are extinct: Caribbean seal Monachus tropicalis, grey, black-footed ferret Mustela nigripes, Audubon & Bachman, greater stick-nest rat Leporillus conditor, Sturt, and lesser stick-nest rat Leporillus apicalis, Gould. As further examples, Birdlife International provided the sighting records for the Alaotra grebe Tachybaptus rufolavatus, Delacour, Jamaica petrel Pterodroma caribbaea, Carte, and Pohnpei mountain starling Aplonis pelzelni, Finsch, with prior probabilities for extinction. The results are compared with existing methods, which ignore uncertain sightings. We find that including uncertain sightings can considerably change the probability that the species is extant, in either direction. However, in our examples, including the quality of the uncertain sighting made little difference. When we ignore uncertain sightings, our results agree with existing methods, especially when the last sighting wa

The extinction of a species can be inferred from a record of its sightings. Existing methods for doing so assume that all sightings in the record are valid. Often,however,there are sightings of uncertain validity. To date,uncertain sightings have been treated in an ad hoc way, either excluding them from the record or including them as if they were certain. We developed a Bayesian method that formally accounts for such uncertain sightings. The method assumes that valid and invalid sightings follow independent Poisson processes and use noninformative prior distributions for the rate of valid sightings and for a measure of the quality of uncertain sightings. We applied the method to a recently published record of sightings of the Ivory-billed Woodpecker(Campephilus principalis).This record covers the period 1897-2010 and contains 39 sightings classified as certain and 29 classified as uncertain. The Bayes factor in favor of extinction was 4.03,which constitutes substantial support for extinction. The posterior distribution of the time of extinction has 3 main modes in 1944,1952,and 1988. The method can be applied to sighting records of other purportedly extinct species. © 2011 Society for Conservation Biology.

Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity. The Intergovernmental Panel on Climate Change fourth assessment report1 cites 28,586 studies demonstrating significant biological changes in terrestrial systems2. Already high extinction rates, driven primarily by habitat loss, are predicted to increase under climate change3, 4, 5, 6. Yet there is little specific advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity within realistic economic constraints7. Here we present a systematic ecological and economic analysis of a climate adaptation problem in one of the world’s most species-rich and threatened ecosystems: the South African fynbos. We discover a counterintuitive optimal investment strategy that switches twice between options as the available adaptation budget increases. We demonstrate that optimal investment is nonlinearly dependent on available resources, making the choice of how much to invest as important as determining where to invest and what actions to take. Our study emphasizes the importance of a sound analytical framework for prioritizing adaptation investments4. Integrating ecological predictions in an economic decision framework will help support complex choices between adaptation options under severe uncertainty. Our prioritization method can be applied at any scale to minimize species loss and to evaluate the robustness of decisions to uncertainty about key assumptions.

Prioritizing management actions for wildlife conservation is a difficult task due to the large number of problems relative to available conservation resources and uncertainty about the benefits arising from numerous potential management actions. In this study we use a cost-efficiency protocol to evaluate and prioritize eight different management actions for waterbird community in wetlands throughout southeastern Spain. The protocol generated an action priority ranking based on the costs and predicted benefits of the actions in terms of waterbird carrying capacity. Action prioritization outcomes were also evaluated using population viability analysis models for two of the study species. Removal of dead bird carcasses to prevent disease outbreaks was identified as the most cost efficient action. Removing lead pel- lets from the sediment was the least efficient strategy. Our approach highlights the role of detailed risk assessment as a form of quality control on the simpler prioritization protocols. We recommend a two- step prioritization protocol based on (i) a rapid, usually simpler prioritization approach for the bulk of species or values being managed, and (ii) a more sophisticated risk assessment for a subset of the species of interest for which detailed risk assessments are tractable. This process strikes a balance between sophistication and practicality.

Barriers are used to achieve diverse objectives in conservation and biosecurity. In conservation management, fences are often erected to exclude introduced predators and to contain diseased animals or invasive species. Planning an efficient conservation fence involves a number of decisions, including the size and design of the enclosure. We formulated the first general framework for building a fence that minimizes long-term management costs by balancing the expense of constructing a more secure barrier against the costs of coping with more frequent failures. The approach systematically considers the range of potential solutions to a well-defined fencing problem and results in a solution that maximizes conservation return on investment. We illustrated this method by designing efficient fences to address two different conservation goals: exclusion of invasive predators from populations of threatened eastern barred bandicoots (Perameles gunnii) and maintenance of isolated populations of healthy Tasmanian devils (Sarcophilus harrisii). A systematic approach to conservation fencing allows the best fence design to be chosen quantitatively and defensibly. It also facilitates conservation decisions at a strategic level by allowing fencing to be compared transparently with alternative conservation management actions.

"Offsetting" habitat destruction has widespread appeal as an instrument for balancing economic growth with biodiversity conservation. Requiring proponents to pay the nontrivial costs of habitat loss encourages sensitive planning approaches. Offsetting, biobanking, and biodiverse carbon sequestration schemes will play an important role in conserving biodiversity under increasing human pressures. However, untenable assumptions in existing schemes are undermining their benefits. Policies that allow habitat destruction to be offset by the protection of existing habitat are guaranteed to result in further loss of biodiversity. Similarly, schemes that allow trading the immediate loss of existing habitat for restoration projects that promise future habitat will, at best, result in time lags in the availability of habitat that increases extinction risks, or at worst, fail to achieve the offset at all. We detail concerns about existing approaches and describe how offsetting and trading policies can be improved to provide genuine benefits for biodiversity. Due to uncertainties about the way in which restored vegetation matures, we propose that the biodiversity bank should be a savings bank. Accrued biodiversity values should be demonstrated before they can be used to offset biodiversity losses. We provide recommendations about how this could be achieved in practice.

The past decade has seen a global surge in forest management certification, with over 200 million hectares of the world's forest now certified as sustainably harvested. Because forests are some of the most species-rich environments on earth and more than

Policy-makers and managers in natural resource management (NRM) often complain that researchers are out of touch. Researchers often complain that policy-makers and managers make poorly informed decisions. In this article, we report on a meeting between researchers, policy-makers and managers convened to identify practical solutions to improve engagement between these camps. A necessary starting point is that every researcher and policy-maker should understand, and tap into, the motivations and reward systems of the other when seeking engagement. For example, researchers can be motivated to engage in policy development if there is a promise of outputs that align with their reward systems such as co-authored publications. Successful research-policy partnerships are built around personal relationships. As a researcher, you cannot therefore expect your results to inform policy by only publishing in journals. As a policy-maker, you cannot guarantee engagement from researchers by publicly inviting comment on a document. Actively building and maintaining relationships with key individuals through discussions, meetings, workshops or field days will increase the likelihood that research outcomes will inform policy decisions. We identified secondments, sabbaticals, fellowships and 'buddies', an annual national NRM conference and 'contact mapping' (a Facebook-type network) as forums that can catalyse new relationships between researchers and policy-makers. We challenge every researcher, policy-maker and manager in NRM to build one new cross-cultural relationship each year.