High prices for rare species can drive large populations extinct: the anthropogenic Allee effect revisited

Consumer demand for plant and animal products threatens many populations with extinction. The anthropogenic Allee effect (AAE) proposes that such extinctions can be caused by prices for wildlife products increasing with species rarity. This price-rarity relationship creates financial incentives to extract the last remaining individuals of a population, despite higher search and harvest costs. The AAE has become a standard approach for conceptualizing the threat of economic markets on endangered species. Despite its potential importance for conservation, AAE theory is based on a simple graphical model with limited analysis of possible population trajectories. By specifying a general class of functions for price-rarity relationships, we show that the classic theory can understate the risk of species extinction. AAE theory proposes that only populations below a critical Allee threshold will go extinct due to increasing price-rarity relationships. Our analysis shows that this threshold can be much higher than the original theory suggests, depending on initial harvest effort. More alarmingly, even species with population sizes above this Allee threshold, for which AAE predicts persistence, can be destined to extinction. Introducing even a minimum price for harvested individuals, close to zero, can cause large populations to cross the classic anthropogenic Allee threshold on a trajectory towards extinction. These results suggest that traditional AAE theory may give a false sense of security when managing large harvested populations

Helping Farmers and Reducing Car Crashes: The Surprising Benefits of Predators

Humans may be Earth’s apex predator, but the fleeting shadow of a vulture or the glimpse of a big cat can cause instinctive fear and disdain. But new evidence suggests that predators and scavengers are much more beneficial to humans than commonly believed, and that their loss may have greater consequences than we have imagined

Subjective risk assessment for planning conservation projects

Conservation projects occur under many types of uncertainty. Where this uncertainty can affect achievement of a project\u27s objectives, there is risk. Understanding risks to project success should influence a range of strategic and tactical decisions in conservation, and yet, formal risk assessment rarely features in the guidance or practice of conservation planning. We describe how subjective risk analysis tools can be framed to facilitate the rapid identification and assessment of risks to conservation projects, and how this information should influence conservation planning. Our approach is illustrated with an assessment of risks to conservation success as part of a conservation plan for the work of The Nature Conservancy in northern Australia. Risks can be both internal and external to a project, and occur across environmental, social, economic and political systems. Based on the relative importance of a risk and the level of certainty in its assessment we propose a series of appropriate, project level responses including research, monitoring, and active amelioration. Explicit identification, prioritization, and where possible, management of risks are important elements of using conservation resources in an informed and accountable manne

Allocating conservation resources between areas where persistence of a species is uncertain

Research on the allocation of resources to manage threatened species typically assumes that the state of the system is completely observable; for example whether a species is present or not. The majority of this research has converged on modeling problems as Markov decision processes (MDP), which give an optimal strategy driven by the current state of the system being managed. However, the presence of threatened species in an area can be uncertain. Typically, resource allocation among multiple conservation areas has been based on the biggest expected benefit (return on investment) but fails to incorporate the risk of imperfect detection. We provide the first decision-making framework for confronting the trade-off between information and return on investment, and we illustrate the approach for populations of the Sumatran tiger (Panthera tigris sumatrae) in Kerinci Seblat National Park. The problem is posed as a partially observable Markov decision process (POMDP), which extends MDP to incorporate incomplete detection and allows decisions based on our confidence in particular states. POMDP has previously been used for making optimal management decisions for a single population of a threatened species. We extend this work by investigating two populations, enabling us to explore the importance of variation in expected return on investment between populations on how we should act. We compare the performance of optimal strategies derived assuming complete (MDP) and incomplete (POMDP) observability. We find that uncertainty about the presence of a species affects how we should act. Further, we show that assuming full knowledge of a species presence will deliver poorer strategic outcomes than if uncertainty about a species status is explicitly considered. MDP solutions perform up to 90% worse than the POMDP for highly cryptic species, and they only converge in performance when we are certain of observing the species during management: an unlikely scenario for many threatened species. This study illustrates an approach to allocating limited resources to threatened species where the conservation status of the species in different areas is uncertain. The results highlight the importance of including partial observability in future models of optimal species management when the species of concern is cryptic in nature

Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most eases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinei Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species

A New Way to Measure the World's Protected Area Coverage

Protected areas are effective at stopping biodiversity loss, but their placement is constrained by the needs of people. Consequently protected areas are often biased toward areas that are unattractive for other human uses. Current reporting metrics that emphasise the total area protected do not account for this bias. To address this problem we propose that the distribution of protected areas be evaluated with an economic metric used to quantify inequality in income— the Gini coefficient. Using a modified version of this measure we discover that 73% of countries have inequitably protected their biodiversity and that common measures of protected area coverage do not adequately reveal this bias. Used in combination with total percentage protection, the Gini coefficient will improve the effectiveness of reporting on the growth of protected area coverage, paving the way for better representation of the world's biodiversity

A novel approach to assessing the ecosystem-wide impacts of reintroductions

Reintroducing a species to an ecosystem can have significant impacts on the recipient ecological community. Although reintroductions can have striking and positive outcomes, they also carry risks; many well intentioned conservation actions have had surprising and unsatisfactory outcomes. A range of network-based mathematical methods have been developed to make quantitative predictions of how communities will respond to management interventions. These methods are based on the limited knowledge of which species interact with each other and in what way. However, expert knowledge isn’t perfect and can only take models so far. Fortunately, other types of data, such as abundance time-series, is often available, but, to date, no quantitative method exists to integrate these various data types into these models, allowing more precise ecosystem-wide predictions. In this paper, we develop mathematical methods that combine time-series data of multiple species with knowledge of species interactions and we apply it to proposed reintroductions at Booderee National Park in Australia. There have been large fluctuations in species abundances at Booderee National Park in recent history, following intense feral fox (Vulpes vulpes) control – including the local extinction of the greater glider (Petauroides volans). These fluctuations can provide information about the system isn’t readily obtained from a stable system, and we use them to inform models that we then use to predict potential outcomes of eastern quoll (Dasyurus viverrinus) and long-nosed potoroo (Potorous tridactylus) reintroductions. One of the key species of conservation concern in the park is the eastern bristlebird (Dasyornis brachypterus), and we find that long-nosed potoroo introduction would have very little impact on the eastern bristlebird population, while the eastern quoll introduction increased the likelihood of eastern bristlebird decline, although that depends on the strength and form of any possible interaction.We thank the ARC Centre of Excellence for Environmental Decisions, The National Environmental Research Project Decisions Hub and an ARC Linkage Project (LP160100496) for funding. CB is the recipient of a John Stocker Postdoctoral Fellowship from the Science and Industry Endowment Fund. MB is supported by an ARC Future Fellowship (FT170100274). EMM is a current ARC Future Fellowship (FT170100140) and was supported by an ARC DECRA Fellowship for the majority of this work

Allocating conservation resources between areas where persistence of a species is uncertain

Abstract. Research on the allocation of resources to manage threatened species typically assumes that the state of the system is completely observable; for example whether a species is present or not. The majority of this research has converged on modeling problems as Markov decision processes (MDP), which give an optimal strategy driven by the current state of the system being managed. However, the presence of threatened species in an area can be uncertain. Typically, resource allocation among multiple conservation areas has been based on the biggest expected benefit (return on investment) but fails to incorporate the risk of imperfect detection. We provide the first decision-making framework for confronting the trade-off between information and return on investment, and we illustrate the approach for populations of the Sumatran tiger (Panthera tigris sumatrae) in Kerinci Seblat National Park. The problem is posed as a partially observable Markov decision process (POMDP), which extends MDP to incorporate incomplete detection and allows decisions based on our confidence in particular states. POMDP has previously been used for making optimal management decisions for a single population of a threatened species. We extend this work by investigating two populations, enabling us to explore the importance of variation in expected return on investment between populations on how we should act. We compare the performance of optimal strategies derived assuming complete (MDP) and incomplete (POMDP) observability. We find that uncertainty about the presence of a species affects how we should act. Further, we show that assuming full knowledge of a species presence will deliver poorer strategic outcomes than if uncertainty about a species status is explicitly considered. MDP solutions perform up to 90% worse than the POMDP for highly cryptic species, and they only converge in performance when we are certain of observing the species during management: an unlikely scenario for many threatened species. This study illustrates an approach to allocating limited resources to threatened species where the conservation status of the species in different areas is uncertain. The results highlight the importance of including partial observability in future models of optimal species management when the species of concern is cryptic in nature

Two-step adaptive management for choosing between two management actions

Adaptive management is widely advocated to improve environmental management. Derivations of optimal strategies for adaptive management, however, tend to be case specific and time consuming. In contrast, managers might seek relatively simple guidance, such as insight into when a new potential management action should be considered, and how much effort should be expended on trialing such an action. We constructed a two-time-step scenario where a manager is choosing between two possible management actions. The manager has a total budget that can be split between a learning phase and an implementation phase. We use this scenario to investigate when and how much a manager should invest in learning about the management actions available. The optimal investment in learning can be understood intuitively by accounting for the expected value of sample information, the benefits that accrue during learning, the direct costs of learning, and the opportunity costs of learning. We find that the optimal proportion of the budget to spend on learning is characterized by several critical thresholds that mark a jump from spending a large proportion of the budget on learning to spending nothing. For example, as sampling variance increases, it is optimal to spend a larger proportion of the budget on learning, up to a point: if the sampling variance passes a critical threshold, it is no longer beneficial to invest in learning. Similar thresholds are observed as a function of the total budget and the difference in the expected performance of the two actions. We illustrate how this model can be applied using a case study of choosing between alternative rearing diets for hihi, an endangered New Zealand passerine. Although the model presented is a simplified scenario, we believe it is relevant to many management situations. Managers often have relatively short time horizons for management, and might be reluctant to consider further investment in learning and monitoring beyond collecting data from a single time period

Geographical surrogates of genetic variation for selecting island populations for conservation

Aim: Threatened species often exist in small numbers in isolated populations. Limited financial resources usually constrain conservationists to allocate funds to a subset of these populations. Because obtaining information required to maximize the amount of genetic and phenotypic variation protected can be costly and time-consuming, the utility of surrogates should be explored. This study tests the efficacy of three simple and cost-effective geographical measures in capturing genetic and phenotypic variation in fragmented populations when setting conservation priorities. Location: Vanuatu archipelago. Methods: We used neutral genetic data (mtDNA and microsatellites) and morphometric data (a proxy for functional variation) for two bird species displaying different patterns of regional population genetic structure: Zosterops flavifrons and Zosterops lateralis. We tested the performance of three geographical surrogates (maximizing: geographical distance between islands; area of islands; geographical representation of islands), in representing divergence between and diversity within populations, constrained to the number of islands being protected. Results: Maximizing geographical separation of sites provided the best surrogate for a constrained budget ( 50% of the populations), the spatially most representative sites were often more effective. Selecting islands based on size retained about half of within-population genetic diversity; however, this was not much higher than selecting the islands randomly. Main conclusions: The ability of surrogates to capture genetic or phenotypic variation varied depending on the species, genetic markers and number of islands selected. While imperfect, selection of populations based on simple geographical surrogates for genetic and phenotypic variation will generally be better than random selection for conserving the evolutionary potential of threatened populations when time and money limit a more thorough and direct analyses of genetic and phenotypic variation