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

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

A call for international leadership and coordination to realize the potential of conservation technology

Advancing technology represents an unprecedented opportunity to enhance our capacity to conserve the Earth's biodiversity. However, this great potential is failing to materialize and rarely endures. We contend that unleashing the power of technology for conservation requires an internationally coordinated strategy that connects the conservation community and policy-makers with technologists. We argue an international conservation technology entity could (1) provide vision and leadership, (2) coordinate and deliver key services necessary to ensure translation from innovation to effective deployment and use of technology for on-the-ground conservation across the planet, and (3) help integrate innovation into biodiversity conservation policy from local to global scales, providing tools to monitor outcomes of conservation action and progress towards national and international biodiversity targets. This proposed entity could take the shape of an international alliance of conservation institutions or a formal intergovernmental institution. Active and targeted uptake of emerging technology can help society achieve biodiversity conservation goals

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

Timing of Protection of Critical Habitat Matters

Conservation Letters published by Wiley Periodicals, Inc. With many conservation issues requiring urgent action, determining how much data are needed to inform good decisions is a common problem. We examine this problem in relation to the protection of critical habitat, the habitat required for species’ recovery and persistence. The protection of critical habitat is an essential step in the threatened species recovery process. It is also one of the most contentious and protracted decisions faced by environmental agencies. Uncertainty about what constitutes critical habitat, and the challenges of balancing competing societal objectives and of protecting critical habitat once identified are stalling the recovery process. We offer insight into this challenge by investigating how long we can afford to spend identifying critical habitat before opportunities to recover a species are lost. We illustrate our decision model using Canada's threatened northern abalone (Haliotis kamtschatkana). Our method delivers the stopping time at which habitat protection must begin, despite uncertainty, in order to avoid an unacceptable risk of extinction