Tracey J. Regan

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.

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

A practical guide to the application of the IUCN Red List of Ecosystems criteria

The newly developed IUCN Red List of Ecosystems is part of a growing toolbox for assessing risks to biodiversity, which addresses ecosystems and their functioning. The Red List of Ecosystems standard allows systematic assessment of all freshwater, marine, terrestrial and subterranean ecosystem types in terms of their global risk of collapse. In addition, the Red List of Ecosystems categories and criteria provide a technical base for assessments of ecosystem status at the regional, national, or subnational level. While the Red List of Ecosystems criteria were designed to be widely applicable by scientists and practitioners, guidelines are needed to ensure they are implemented in a standardized manner to reduce epistemic uncertainties and allow robust comparisons among ecosystems and over time. We review the intended application of the Red List of Ecosystems assessment process, summarize ‘best-practice’ methods for ecosystem assessments and outline approaches to ensure operational rigour of assessments. The Red List of Ecosystems will inform priority setting for ecosystem types worldwide, and strengthen capacity to report on progress towards the Aichi Targets of the Convention on Biological Diversity. When integrated with other IUCN knowledge products, such as the World Database of Protected Areas/Protected Planet, Key Biodiversity Areas and the IUCN Red List of Threatened Species, the Red List of Ecosystems will contribute to providing the most complete global measure of the status of biodiversity yet achieved.

Detecting extinction risk from climate change by IUCN red list criteria

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow-acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short-lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions.

The roles of spatial heterogeneity and ecological processes in conservation planning

In this chapter we ask the question: To what extent does an understanding of landscape spatial heterogeneity inform conservation decisions? We answer this question in the context of two central decision-making fields within conservation biology: systematic conservation planning and population viability analysis, The conservation planning principles of comprehensiveness and representativeness are fundamentally reliant on data and concepts of compositional landscape heterogeneity. The principle of adequacy is not accommodated in conservation planning very well and it relies on an understanding of the configurational heterogeneity of the landscape. A major challenge for conservation planning scientists is to develop theory and decision support tools that incorporate ideas of population viability and spatially explicit ecological processes. Population viability analysis invariably includes spatial population processes, and as a field has largely focused on the importance of the configurational heterogeneity of landscapes. We argue that this focus might only be justified when the scale of planning coincides with either the scale of habitat heterogeneity or the scale at which small populations operate. Integrating population viability analysis into conservation planning, and showing a balanced interest in compositional and configurational heterogeneity, are important future challenges.

Testing Decision Rules for Categorizing Species’ Extinction Risk to Help Develop Quantitative Listing Criteria for the U.S. Endangered Species Act

Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding to different levels of protection, and the importance given to different types of listing errors. We tested the performance of 3 sets of decision rules that use alternative functions for weighting the relative urgency of future extinction events: a threshold rule set, which uses a decision rule of x% probability of extinction over y years; a concave rule set, where the relative importance of future extinction events declines exponentially over time; and a shoulder rule set that uses a sigmoid shape function, where relative importance declines slowly at first and then more rapidly. We obtained decision cutoffs by interviewing several biologists and then emulated the listing process with simulations that covered a range of extinction risks typical of ESA listing decisions. We evaluated performance of the decision rules under different data quantities and qualities on the basis of the relative importance of misclassification errors. Although there was little difference between the performance of alternative decision rules for correct listings, the distribution of misclassifications differed depending on the function used. Misclassifications for the threshold and concave listing criteria resulted in more overprotection errors, particularly as uncertainty increased, whereas errors for the shoulder listing criteria were more symmetrical. We developed and tested the framework for quantitative decision rules for listing species under the U.S. ESA. If policy values can be agreed on, use of this framework would improve the implementation of the ESA by increasing transparency and consistency.

Modelling the impact of timber harvesting on a rare carnivorous land snail (Tasmaphena lamproides) in northwest Tasmania, Australia

Abstract A stage-structured, stochastic metapopulation model was developed for a rare carnivorous snail, Tasmaphena lamproides , occurring in production forest in northwest Tasmania, Australia. The model was used to investigate the impact of a range of plantation options and harvesting strategies. These included: (i) a proposed ‘District plan’ with a combination of plantation conversion and native-forest harvesting; (ii) a ‘plantation’ scenario where all suitable areas within the forest are converted to plantation; and (iii) a ‘native forest’ scenario where all areas are harvested to a schedule and regenerated to native forest. The model facilitates exploration of management options and their consequences in terms of their effects on average population sizes and risk of decline of T. lamproides . The results suggest that retention of contiguous areas regenerated to native forest, the timing of harvest activities, and the management of dispersal barriers may be important elements of a successful management strategy for T. lamproides . A detailed analysis was conducted to determine the sensitivity of the model to small changes in parameter estimates and assumptions. This sensitivity analysis can be used to highlight the features of the model that require a more extensive investigation and to guide future research into the conservation of T. lamproides .

Fire management to combat disease: turning interactions between threats into conservation management.

As the number and intensity of threats to biodiversity increase, there is a critical need to investigate interactions between threats and manage populations accordingly. We ask whether it is possible to reduce the effects of one threat by mitigating another. We used long-term data for the long-lived resprouter, Xanthorrhoea resinosa Pers., to parameterise an individual-based population model. This plant is currently threatened by adverse fire regimes and the pathogen Phytophthora cinnamomi. We tested a range of fire and disease scenarios over various time horizons relevant to the population dynamics of the species and the practicalities of management. While fire does not kill the disease, it does trigger plant demographic responses that may promote population persistence when disease is present. Population decline is reduced with frequent fires because they promote the greatest number of germination events, but frequent fires reduce adult stages, which is detrimental in the long term. Fire suppression is the best action for the non-seedling stages but does not promote recruitment. With disease, frequent fire produced the highest total population sizes for shorter durations, but for longer durations fire suppression gave the highest population sizes. When seedlings were excluded, fire suppression was the best action. We conclude that fire management can play an important role in mitigating threats posed by this disease. The best approach to reducing declines may be to manage populations across a spatial mosaic in which the sequence of frequent fires and suppression are staggered across patches depending on the level of disease at the site.

Combined influences of model choice, data quality, and data quantity when estimating population trends

Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10–15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

Impacts of plantation development, harvesting schedules and rotation lengths on the rare snail Tasmaphena lamproides in northwest Tasmania: a population viability analysis

Tasmaphena lamproides is a rare snail found in northwest Tasmania. The species is eliminated by logging but re-establishes a population in ∼20-year-old native forest regeneration and builds up to pre-logging levels by ∼60 years. Major plantation development is planned within the range of the species. It is unlikely that T. lamproides will reinvade areas converted to plantation. To aid the conservation of T. lamproides the managing authority planned to retain a “biodiversity spine” (i.e. a string of contiguous coupes (logging units) that would be regenerated to native forest rather than converted to plantation) within areas earmarked for major plantation development. A PVA was used to assess the comparative impacts of different forest management scenarios. The management scenarios modelled involved differing levels of reservation, differing levels of native forest regeneration or conversion to plantation, different rotation lengths for native forest regeneration and different temporal patterns of logging of native forest. In a forest block with a major reserve simulations indicated that the population would decline to around 50% of the original population and thereafter remain fairly stable. For a forest block earmarked for plantation development where no major reserves occurred, simulations indicated the population would undergo a steep decline to around 20% of the original population. The extent of the recovery of the population before re-harvesting of the native forest coupes depended on the extent of the biodiversity spine. The probability of reaching low absolute population levels (<one or two thousand individuals) varied with the degree of plantation development but not linearly. Increasing the length of the rotation of native forest coupes lowered the probability of reaching low absolute numbers as did increasing the spread of logging of coupes over the length of the rotation rather than logging all of the coupes over a short time span. The model allows the managing authority to design a management scenario that meets a specific quantitative goal, such as a less than 10% probability of numbers falling below 1000 individuals. It also allows them to choose between a mix of different forest management strategies that could all potentially provide the same level of conservation benefit.