Predicting the optimal amount of time to spend learning before designating protected habitat for threatened species

1. Deciding when to protect threatened species habitat when complete knowledge about the habitat extent is uncertain is a common problem in conservation. More accurate habitat mapping improves conservation outcomes once that habitat is protected. However, delaying protection to improve accuracy can lead to species decline or, at worst, local extinction when threats to that habitat continue unabated before protection is implemented. Hence, there is a trade-off between gaining knowledge and taking conservation action. 2. We quantified this trade-off and determined the optimal time to spend learning about a species' habitat before protecting that habitat. We used a range of hypothetical learning curves to model improvements in the accuracy of predicted habitat over time, and receiver operating characteristic (ROC) curves to model the corresponding increase in the proportion of habitat protected. We used rates of habitat loss to model the impact of delaying habitat protection and derived analytical solutions to the problem for different types of learning curves. 3. We illustrate our approach using two threatened species, the koala Phascolarctos cinereus in Australia and northern abalone Haliotis kamtschatkana in Canada. Our approach confirms that when impacts of threatening processes are incurred rapidly, the need for timely protection is high, and the optimal time to spend learning is short for all learning curves. When the rate of habitat loss is low, we benefit from better habitat identification, and the optimal time to protect is sensitive to assumptions about how we learn and the proportion of non-habitat we are willing to protect unnecessarily. 4. Navigating the trade-off between information gain and timely action is a common problem in conservation. By optimizing the trade-off between the benefits of improving mapping accuracy and the costs of delaying protection, we provide guidelines on the effective allocation of resources between habitat identification and habitat protection. Importantly, by explicitly modelling this trade-off with a range of learning curves and estimates of the rates of habitat loss or other threatening processes, we can predict the optimal time to spend learning even when relatively little is known about a species and its habitat.Abbey E. Camaclang, Iadine Chadès, Tara G. Martin, Hugh P. Possingha

Which States Matter? An Application of an Intelligent Discretization Method to Solve a Continuous POMDP in Conservation Biology

When managing populations of threatened species, conservation managers seek to make the best conservation decisions to avoid extinction. Making the best decision is difficult because the true population size and the effects of management are uncertain. Managers must allocate limited resources between actively protecting the species and monitoring. Resources spent on monitoring reduce expenditure on management that could be used to directly improve species persistence. However monitoring may prevent sub-optimal management actions being taken as a result of observation error. Partially observable Markov decision processes (POMDPs) can optimize management for populations with partial detectability, but the solution methods can only be applied when there are few discrete states. We use the Continuous U-Tree (CU-Tree) algorithm to discretely represent a continuous state space by using only the states that are necessary to maintain an optimal management policy. We exploit the compact discretization created by CU-Tree to solve a POMDP on the original continuous state space. We apply our method to a population of sea otters and explore the trade-off between allocating resources to management and monitoring. We show that accurately discovering the population size is less important than management for the long term survival of our otter population

Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods

Integral control for population management

We present a novel management methodology for restocking a declining population. The strategy uses integral control, a concept ubiquitous in control theory which has not been applied to population dynamics. Integral control is based on dynamic feedback-using measurements of the population to inform management strategies and is robust to model uncertainty, an important consideration for ecological models. We demonstrate from first principles why such an approach to population management is suitable via theory and examples

Connectivity and systemic resilience of the Great Barrier Reef

Australia’s iconic Great Barrier Reef (GBR) continues to suffer from repeated impacts of cyclones, coral bleaching, and outbreaks of the coral-eating crown-of-thorns starfish (COTS), losing much of its coral cover in the process. This raises the question of the ecosystem’s systemic resilience and its ability to rebound after large-scale population loss. Here, we reveal that around 100 reefs of the GBR, or around 3%, have the ideal properties to facilitate recovery of disturbed areas, thereby imparting a level of systemic resilience and aiding its continued recovery. These reefs (1) are highly connected by ocean currents to the wider reef network, (2) have a relatively low risk of exposure to disturbances so that they are likely to provide replenishment when other reefs are depleted, and (3) have an ability to promote recovery of desirable species but are unlikely to either experience or spread COTS outbreaks. The great replenishment potential of these ‘robust source reefs’, which may supply 47% of the ecosystem in a single dispersal event, emerges from the interaction between oceanographic conditions and geographic location, a process that is likely to be repeated in other reef systems. Such natural resilience of reef systems will become increasingly important as the frequency of disturbances accelerates under climate change

A comparison of adaptive management and real options approaches for environmental decisions under uncertainty

Two approaches to sequential decisions under uncertainty in the environmental management adaptive management and real options analysis -have evolved independently over the last decades