Michael A. McCarthy

Scientific Foundations for an IUCN Red List of Ecosystems

An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world’s ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity

Sensitivity analysis for models of population viability

A method of sensitivity analysis for population viability models is presented that uses logistic regression to evaluate the importance of model parameters that influence the risks of extinction. This approach is used to evaluate the importance of fecundity parameters and the initial number of non-breeding birds in a stochastic stage-structured model of helmeted honeyeater Lichenostomus melanops cassidix population dynamics. The regression analysis indicates which model parameters have the greatest impact on the risk of population decline. The results demonstrate that a simple expression containing the parameters of the model can encapsulate predictions of risk. This technique is proposed as an efficient alternative method of sensitivity analysis for population viability models. Of four fecundity parameters, the mean fecundity of intact pairs had the greatest influence on the risks faced by the helmeted honeyeater population. Mean fecundity of split pairs and the sex ratio of offspring were also important parameters. Over the range of parameters considered in this paper, environmental variation in fecundity and the initial number of non-breeding birds had little influence on the risks of decline. The importance of interactions between parameters was analysed.

A global synthesis of plant extinction rates in urban areas

Plant extinctions from urban areas are a growing threat to biodiversity worldwide. To minimize this threat, it is critical to understand what factors are influencing plant extinction rates. We compiled plant extinction rate data for 22 cities around the world. Two-thirds of the variation in plant extinction rates was explained by a combination of the citys historical development and the current proportion of native vegetation, with the former explaining the greatest variability. As a single variable, the amount of native vegetation remaining also influenced extinction rates, particularly in cities > 200 years old. Our study demonstrates that the legacies of landscape transformations by agrarian and urban development last for hundreds of years, and modern cities potentially carry a large extinction debt. This finding highlights the importance of preserving native vegetation in urban areas and the need for mitigation to minimize potential plant extinctions in the future.

Streamlining 'search and destroy': cost-effective surveillance for invasive species management.

Invasive species surveillance has typically been targeted to where the species is most likely to occur. However, spatially varying environmental characteristics and land uses may affect more than just the probability of occurrence. Biodiversity or economic value, and the ease of detection and control are also likely to vary. We incorporate these factors into a detection and treatment model of a low-density invader to determine the surveillance strategy that minimizes expected management costs. Sites with a high probability of invader occurrence and great benefits associated with detection warrant intensive surveillance; however, the optimum investment is a nonlinear function of these factors. Environments where the invader is relatively easy to detect are prioritized for surveillance, although only a moderate investment is necessary to ensure a high probability of detection. Intensive surveillance effort may be allocated to other sites if the probability of occurrence, budget and/or expected benefits is sufficiently high.

Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)

Summary A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species’ and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co-occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co-occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co-occurrence patterns into components describing shared environmental responses and residual patterns of co-occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co-occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co-occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.

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.

Redefine Statistical Significance

We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

PRECISION AND BIAS OF METHODS FOR ESTIMATING POINT SURVEY DETECTION PROBABILITIES

Wildlife surveys often seek to determine the presence or absence of species at sites. Such data may be used in population monitoring, impact assessment, and species– habitat analyses. An implicit assumption of presence/absence surveys is that if a species is not detected in one or more visits to a site, it is absent from that site. However, it is rarely if ever possible to be completely sure that a species is absent, and false negative observation errors may arise when detection probabilities are less than 1. The detectability of species in wildlife surveys is one of the most important sources of uncertainty in determining the proportion of a landscape that is occupied by a species. Recent studies emphasize the need to acknowledge and incorporate false negative observation error rates in the analysis of site occupancy data, although a comparative study of the range of available methods for estimating detectability and occupancy is notably absent. The motivation for this study stems from the lack of guidan...

ESTIMATING AND DEALING WITH DETECTABILITY IN OCCUPANCY SURVEYS FOR FOREST OWLS AND ARBOREAL MARSUPIALS

Abstract Surveys that record the presence or absence of fauna are used widely in wildlife management and research. A false absence occurs when an observer fails to record a resident species. There is a growing appreciation of the importance of false absences in wildlife surveys and its influence on impact assessment, monitoring, habitat analyses, and population modeling. Very few studies explicitly quantify the rate of these errors. Quantifying the rate of false absences provides a basis for estimating the survey effort necessary to assert that a species is absent with a pre-specified degree of confidence and allows uncertainty arising from false absences to be incorporated in inference. We estimated the rate of false absences for 2 species of forest owl and 4 species of arboreal marsupial based on 8 repeat visits to 50 survey locations in south-eastern Australia. We obtained estimates using a generalized zero-inflated binomial model. We presented detectability curves for each species to convey the number of visits required to achieve a specified level of confidence that resident species will be detected. The observation error rates we calculated were substantial but varied between species. For the least detectable species, the powerful owl (Ninox strenua), our standard surveys returned false absences on 87% of visits. However, our surveys of the more detectable sugar glider (Petaurus breviceps) returned a 45% false absence rate. We predict that approximately 18 visits would be required to be 90% sure of detecting resident owls and approximately 5 visits would provide 90% confidence of detecting resident sugar gliders. We fitted hierarchical logistic regression models to the data to describe the variation in detection rates explained by environmental variables. We found that temperature, rainfall, and habitat quality influenced the detectability of most species. Consideration of observation error rates could result in important changes to resource management and conservation planning.

Expected minimum population size as a measure of threat

Risks of population decline are studied extensively in conservation biology, but are difficult to estimate because they change abruptly over a relatively narrow range of parameters. We propose that risks of decline may be usefully summarized by the expected minimum population size. This is the smallest population size that is expected to occur within a particular time period. Analytical solutions for the expected minimum population size are obtained for a stochastic population model of exponential growth. In more complex models that are analyzed by Monte Carlo simulation, the expected minimum population size may be determined by recording the smallest population size obtained in each iteration and taking the average of these values. Whereas risks of decline change abruptly with changes in parameter values, the expected minimum population size changes more gradually. The results demonstrate that the expected minimum population size provides a better indication of the propensity for decline than the risk of extinction (or risk of decline to some other small population size), especially when the risk of extinction is small.