Eve McDonald-Madden

Predicting species distributions for conservation decisions.

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Is conservation triage just smart decision making

Conservation efforts and emergency medicine face comparable problems: how to use scarce resources wisely to conserve valuable assets. In both fields, the process of prioritising actions is known as triage. Although often used implicitly by conservation managers, scientists and policymakers, triage has been misinterpreted as the process of simply deciding which assets (e.g. species, habitats) will not receive investment. As a consequence, triage is sometimes associated with a defeatist conservation ethic. However, triage is no more than the efficient allocation of conservation resources and we risk wasting scarce resources if we do not follow its basic principles.

Monitoring does not always count.

The gross under-resourcing of conservation endeavours has placed an increasing emphasis on spending accountability. Increased accountability has led to monitoring forming a central element of conservation programs. Although there is little doubt that information obtained from monitoring can improve management of biodiversity, the cost (in time and/or money) of gaining this knowledge is rarely considered when making decisions about allocation of resources to monitoring. We present a simple framework allowing managers and policy advisors to make decisions about when to invest in monitoring to improve management.

Replacing underperforming protected areas achieves better conservation outcomes

Protected areas vary enormously in their contribution to conserving biodiversity, and the inefficiency of protected area systems is widely acknowledged. However, conservation plans focus overwhelmingly on adding new sites to current protected area estates. Here we show that the conservation performance of a protected area system can be radically improved, without extra expenditure, by replacing a small number of protected areas with new ones that achieve more for conservation. Replacing the least cost-effective 1% of Australia’s 6,990 strictly protected areas could increase the number of vegetation types that have 15% or more of their original extent protected from 18 to 54, of a maximum possible of 58. Moreover, it increases markedly the area that can be protected, with no increase in overall spending. This new paradigm for protected area system expansion could yield huge improvements to global conservation at a time when competition for land is increasingly intense.

When to stop managing or surveying cryptic threatened species.

Threatened species become increasingly difficult to detect as their populations decline. Managers of such cryptic threatened species face several dilemmas: if they are not sure the species is present, should they continue to manage for that species or invest the limited resources in surveying? We find optimal solutions to this problem using a Partially Observable Markov Decision Process and rules of thumb derived from an analytical approximation. We discover that managing a protected area for a cryptic threatened species can be optimal even if we are not sure the species is present. The more threatened and valuable the species is, relative to the costs of management, the more likely we are to manage this species without determining its continued persistence by using surveys. If a species remains unseen, our belief in the persistence of the species declines to a point where the optimal strategy is to shift resources from saving the species to surveying for it. Finally, when surveys lead to a sufficiently low belief that the species is extant, we surrender resources to other conservation actions. We illustrate our findings with a case study using parameters based on the critically endangered Sumatran tiger (Panthera tigris sumatrae), and we generate rules of thumb on how to allocate conservation effort for any cryptic species. Using Partially Observable Markov Decision Processes in conservation science, we determine the conditions under which it is better to abandon management for that species because our belief that it continues to exist is too low.

Should We Protect the Strong or the Weak? Risk, Resilience, and the Selection of Marine Protected Areas

It is thought that recovery of marine habitats from uncontrollable disturbance may be faster in marine reserves than in unprotected habitats. But which marine habitats should be protected, those areas at greatest risk or those at least risk? We first defined this problem mathematically for 2 alternate conservation objectives. We then analytically solved this problem for both objectives and determined under which conditions each of the different protection strategies was optimal. If the conservation objective was to maximize the chance of having at least 1 healthy site, then the best strategy was protection of the site at lowest risk. On the other hand, if the goal was to maximize the expected number of healthy sites, the optimal strategy was more complex. If protected sites were likely to spend a significant amount of time in a degraded state, then it was best to protect low-risk sites. Alternatively, if most areas were generally healthy then, counterintuitively, it was best to protect sites at higher risk. We applied these strategies to a situation of cyclone disturbance of coral reefs on Australias Great Barrier Reef. With regard to the risk of cyclone disturbance, the optimal reef to protect differed dramatically, depending on the expected speed of reef recovery of both protected and unprotected reefs. An adequate consideration of risk is fundamental to all conservation actions and can indicate surprising routes to conservation success.

Active adaptive conservation of threatened species in the face of uncertainty

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives.

Why do we map threats? Linking threat mapping with actions to make better conservation decisions

Spatial representations of threatening processes – “threat maps” – can identify where biodiversity is at risk, and are often used to identify priority locations for conservation. In doing so, decision makers are prone to making errors, either by assuming that the level of threat dictates spatial priorities for action or by relying primarily on the location of mapped threats to choose possible actions. We show that threat mapping can be a useful tool when incorporated within a transparent and repeatable structured decision-making (SDM) process. SDM ensures transparent and defendable conservation decisions by linking objectives to biodiversity outcomes, and by considering constraints, consequences of actions, and uncertainty. If used to make conservation decisions, threat maps are best developed with an understanding of how species respond to actions that mitigate threats. This approach will ensure that conservation actions are prioritized where they are most cost-effective or have the greatest impact, rather than where threat levels are highest.

Dynamic marine protected areas can improve the resilience of coral reef systems

Marine Protected Areas are usually static, permanently closed areas. There are, however, both social and ecological reasons to adopt dynamic closures, where reserves move through time. Using a general theoretical framework, we investigate whether dynamic closures can improve the mean biomass of herbivorous fishes on reef systems, thereby enhancing resilience to undesirable phase-shifts. At current levels of reservation (10-30%), moving protection between all reefs in a system is unlikely to improve herbivore biomass, but can lead to a more even distribution of biomass. However, if protected areas are rotated among an appropriate subset of the entire reef system (e.g. rotating 10 protected areas between only 20 reefs in a 100 reef system), dynamic closures always lead to increased mean herbivore biomass. The management strategy that will achieve the highest mean herbivore biomass depends on both the trajectories and rates of population recovery and decline. Given the current large-scale threats to coral reefs, the ability of dynamic marine protected areas to achieve conservation goals deserves more attention.

Control of pest mammals for biodiversity protection in Australia. I. Patterns of control and monitoring.

Foxes, wild dogs, feral cats, rabbits, feral pigs and feral goats are believed to have deleterious impacts on native biodiversity in Australia. However, although considerable resources have been expended controlling these six species, little is known about national patterns and costs of control and monitoring. We therefore conducted a survey of pest-control operations undertaken by conservation-focused organisations in Australia. A total of 1306 control operations were reported, with most conducted during 1998-2003: there was little information prior to 1990. Foxes and rabbits were the most, and feral cats the least, frequently controlled pest species. The total area on which control was undertaken in 2003, the year for which most information was available, ranged from ~0.4 × 10 4 km 2 for feral cats to ~10.7 × 10 4 km 2 for foxes. A wide range of techniques and intensities were used to control each of the six species. The estimated cost of labour expended on control in 2003 ranged from