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Understanding the constraints that dominant predators impose on subordinate species is important for predicting ecosystem dynamics and anticipating outcomes of predator management. Subordinate predators may avoid dominant predators in time or space, making it difficult to quantify antipredator behaviours unless joint spatiotemporal analyses are used. Here, we test whether an invasive dominant predator (red fox Vulpes vulpes) alters the spatiotemporal activity of an invasive subordinate predator (feral cat Felis catus). We collated records of both species from 3667 camera‐traps deployed experimentally across two regions of south‐eastern Australia with simplified predator guilds. Foxes were poison‐baited in some landscapes within each region. We used generalised additive models to quantify changes in predator spatiotemporal activity across geographic space, vegetation types, human footprints and (artificially manipulated) gradients of dominant predator activity. Foxes and cats had similar diel activity patterns when averaged across all sites, but there was important differentiation at a finer scale: cats did not reduce their spatial activity but shifted diel patterns when localised fox activity was high. Cats were crepuscular on average. However, across dry vegetation types of both regions (where foxes were nocturnal), cats shifted to diurnal behaviour with increasing fox activity. In contrast, fox activity was relatively consistent throughout the daily cycle in the wet forest; here cats avoided dawn when fox activity was high. Changes in cat diel activity patterns may facilitate spatial coexistence between these two invasive predators, potentially shifting feral cat impacts onto different native prey. While it is well‐appreciated that predator activity varies spatially and fluctuates throughout the daily cycle,our study demonstrates that diel activity patterns also vary across space, likely mediated by both landscape‐context and fear. Dominant predator avoidance in time also appears to be spatially dynamic – a key nuance overlooked when simply comparing the average activity overlap between two species. [ABSTRACT FROM AUTHOR]

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data‐rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species' ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments. Article impact statement: Not explicitly including ecological complexity in biodiversity metrics may result in misleading conclusions when assessing impacts. [ABSTRACT FROM AUTHOR]

The 2019–2020 megafires in Australia brought a tragic loss of human life and the most dramatic loss of habitat for threatened species and devastation of ecological communities in postcolonial history. What must be done now to keep impacted species from extinction? What can be done to avoid a repeat of the impacts of such devastating bushfires? Here, we describe hard-won lessons that may also be of global relevance. [ABSTRACT FROM AUTHOR]

Decision science emphasizes necessary elements required for robust decision‐making. By incorporating decision science principles, frameworks, and tools, it has been demonstrated that decision‐makers can increase the chances of achieving conservation aims. Setting measurable objectives, clearly documenting assumptions about the impact of available actions on a specific threat or problem, explicitly considering constraints, exploring and characterizing uncertainty, and structured deliberation on trade‐offs have been identified as key elements of successful decision‐making. We quantify the extent to which these five elements were utilized in published examples of decision making in conservation in both academic and conservation practice between 2009 and 2018. We found that less than 50% of identified examples included all five elements, with differences in the degree of decision science applied across five commonly used decision support approaches: adaptive management (AM), systematic conservation planning (SCP), structured decision making (SDM), multi‐criteria decision analysis, and cost‐effectiveness analysis. Example applications that utilized the SDM framework were limited in numbers but used on average more than 50% of the five key elements we considered. Although SCP and AM constituted the majority of examples, they were more prevalent in academic studies rather than management applications. SCP and AM examples were widespread in protected area planning, threat abatement, and restoration. Strong geographic bias exists in documented conservation activities that deploy all five decision science elements. [ABSTRACT FROM AUTHOR]

Island biogeography theory posits that species richness increases with island size and decreases with isolation. This logic underpins much conservation policy and regulation, with preference given to conserving large, highly connected areas, and relative ambivalence shown toward protecting small, isolated habitat patches. We undertook a global synthesis of the relationship between the conservation value of habitat patches and their size and isolation, based on 31 systematic conservation planning studies across four continents. We found that small, isolated patches are inordinately important for biodiversity conservation. Our results provide a powerful argument for redressing the neglect of small, isolated habitat patches, for urgently prioritizing their restoration, and for avoiding simplistic application of island biogeography theory in conservation decisions [ABSTRACT FROM AUTHOR]

Offsetting policies have increased worldwide, utilizing a range of biodiversity metrics to compensate for development impacts. We conducted a global analysis of offset legislation by reviewing policies from 108 countries, which have voluntary offsets, or which require offsets by law. We sought to understand how well biodiversity metrics and offset currencies are documented in current policies. Where biodiversity metrics are documented we aimed to understand how metrics were scored, combined, and multiplied to create offset currencies. We found only 22 jurisdictions (from 14 countries) had guidelines documenting how biodiversity should be assessed during offsetting, representing a significant gap in the guidance available for offsets. Of the 22 guidelines, 14 (63%) documented use of aggregated currencies, eight (23%) did not aggregate biodiversity metrics into a single currency, and three (17%) did not specify either approach. Habitat type and condition, as well as area, were widely recommended across policies (>50%). Where species‐level metrics were considered, guidelines generally focused on habitat distributions rather than abundance or population metrics. The lack of consistent and clear guidance about how biodiversity should be measured in offsets reduces our ability to determine the effectiveness of offsets in compensating for development impacts long term. [ABSTRACT FROM AUTHOR]

Conservation fences are an increasingly common management action, particularly for species threatened by invasive predators. However, unlike many conservation actions, fence networks are expanding in an unsystematic manner, generally as a reaction to local funding opportunities or threats. We conducted a gap analysis of Australia's large predator-exclusion fence network by examining translocation of Australian mammals relative to their extinction risk. To address gaps identified in species representation, we devised a systematic prioritization method for expanding the conservation fence network that explicitly incorporated population viability analysis and minimized expected species' extinctions. The approach was applied to New South Wales, Australia, where the state government intends to expand the existing conservation fence network. Existing protection of species in fenced areas was highly uneven; 67% of predator-sensitive species were unrepresented in the fence network. Our systematic prioritization yielded substantial efficiencies in that it reduced expected number of species extinctions up to 17 times more effectively than ad hoc approaches. The outcome illustrates the importance of governance in coordinating management action when multiple projects have similar objectives and rely on systematic methods rather than expanding networks opportunistically. [ABSTRACT FROM AUTHOR]

Despite growing concerns regarding increasing frequency of extreme climate events and declining population sizes, the influence of environmental stochasticity on the relationship between population carrying capacity and time-to-extinction has received little empirical attention. While time-toextinction increases exponentially with carrying capacity in constant environments, theoretical models suggest increasing environmental stochasticity causes asymptotic scaling, thus making minimum viable carrying capacity vastly uncertain in variable environments. Using empirical estimates of environmental stochasticity in fish metapopulations, we showed that increasing environmental stochasticity resulting from extreme droughts was insufficient to create asymptotic scaling of time-to-extinction with carrying capacity in local populations as predicted by theory. Local timeto- extinction increased with carrying capacity due to declining sensitivity to demographic stochasticity and the slope of this relationship declined significantly as environmental stochasticity increased. However, recent 1 in 25 yr extreme droughts were insufficient to extirpate populations with large carrying capacity. Consequently, large populations may be more resilient to environmental stochasticity than previously thought. The lack of carrying capacity-related asymptotes in persistence under extreme climate variability reveals how small populations affected by habitat loss or overharvesting, may be disproportionately threatened by increases in extreme climate events with global warming. [ABSTRACT FROM AUTHOR]

Abstract: Disentangling the relative influence of the environment and biotic interactions in determining species coexistence patterns is a major challenge in ecology. The zonation occurring along elevation gradients, or at bioclimatic contact zones, offers a good opportunity to improve such understanding because the small scale at which the partitioning occurs facilitates inference based on experiments and ecological modelling. We studied the influence of abiotic gradients, habitat types, and interspecific competition in determining the spatial turnover between two pipit and two bunting species in NW Spain. We explored two independent lines of evidence to draw inference about the relative importance of environment and biotic interactions in driving range partitioning along elevation, latitude, and longitude. We combined occurrence data with environmental data to develop joint species distribution models (JSDM), in order to attribute co‐occurrence (or exclusion) to shared (or divergent) environmental responses and to interactions (attraction or exclusion). In the same region, we tested for interference competition by means of playback experiments in the contact zone. The JSDMs highlighted different responses for the two species pairs, although we did not find direct evidence of interspecific aggressiveness in our playback experiments. In pipits, partitioning was explained by divergent climate and habitat requirements and also by the negative correlations between species not explained by the environment. This significant residual correlation may reflect forms of competition others than direct interference, although we could not completely exclude the influence of unmeasured environmental predictors. When bunting species co‐occurred, it was because of shared habitat preferences, and a possible limitation to dispersal might cause their partitioning. Our results indicate that no single mechanism dominates in driving the distribution of our study species, but rather distributions are determined by the combination of many small forces including biotic and abiotic determinants of niche, whose relative strengths varied among species. [ABSTRACT FROM AUTHOR]

Pressure to conserve biodiversity with limited resources has led to increasing use of species distribution models (SDMs) for spatial conservation prioritization. Published spatial prioritization exercises often focus on well-studied groups, with data compiled from on-line databases of ad-hoc collections. Conservation plans generally aim to protect all components of biodiversity, and it is implied that the species used in prioritization act as surrogates. Here, we assess the sensitivity of spatial priorities to model and surrogate choice using a case study from a fragmented agricultural area of south eastern Australia that is poorly represented in the national reserve system. We model the distributions of 30 species of bird, microbat and bee using two types of SDM; generalised linear models based on systematic surveys that yield presence and absence observations, and MaxEnt models based on biodiversity database records. Eight prioritization scenarios were tested using Zonation software, and were based on either the presence-background or presence-absence SDMs and combinations of surrogacy among the three taxa. We found low correlations between SDMs generated for the same species using different modelling frameworks (μ = 0.18, n = 26). Area under the receiver operating characteristic curve (AUC) estimates generated by MaxEnt were optimistic; on average 1.36 times higher than when tested against the systematic survey data. Conservation priorities were sensitive to the choice of surrogate and type of data used to fit SDMs, and though bats and birds formed moderately good surrogates for each other, there was less compelling evidence of surrogacy for bees. Because valid surrogacy is unlikely with most existing data sets, investment in high quality data for less-surveyed groups prior to planning should still be a priority. If this is not possible, then it is advisable to analyse the sensitivity of conservation plans to the assumed surrogacy and quality of data available. [ABSTRACT FROM AUTHOR]

Abstract: Birds use a number of environmental cues to time their breeding season to maximise their chances of raising young when food is most abundant. Such cues include photoperiod, temperature and rainfall. In very arid regions, birds may start egg-laying with the onset of rain to allow fledging to coincide with the availability of grass seeds. However the influence of rainfall on timing of egg-laying in areas with variable, but more reliable, rainfall has not been as clear. Carnaby’s Cockatoo, an endemic species of southwestern Australia, a region with a Mediterranean climate, is known colloquially as “the rainbird” as its movements to the breeding areas appear to coincide with the start of the wetter part of the year. Here we use a long-term data set on the breeding of this species (24years of data from 1969 to 2011) to quantify the link between the timing of autumn rains and the commencement of egg-laying in this endangered cockatoo. We found a tight synchrony which indicates a strong reliance of the species on early autumn rains as a cue for breeding. We describe the conservation implications of increased variability in timing and quantity of rainfall for the long-term viability of Carnaby’s Cockatoo. [Copyright &y& Elsevier]

Spatial models of population dynamics have been proposed as a useful method for predicting the impacts of environmental change on biodiversity. Here, we demonstrate advances in dynamic landscape metapopulation modelling and its use as a decision support tool for evaluating the impacts of forest management scenarios. This novel modelling framework incorporates both landscape and metapopulation model stochasticity and allows their relative contributions to model output variance to be characterized. It includes a detailed sensitivity analysis, allowing defensible uncertainty bounds and the prioritization of future data gathering to reduce model uncertainties. We demonstrate this framework by modelling the landscape-level impacts of eight forest management scenarios on the red-backed salamander ( Plethodon cinereus (Green, 1818)) in the boreal forest of Ontario, Canada, using the RAMAS Landscape package. The 100 year forest management scenarios ranged in intensity of timber harvesting and fire suppression. All scenarios including harvesting predicted decreases in salamander population size and the current style of forest management is predicted to produce a 9%-17% decrease in expected minimum population size compared with scenarios without harvesting. This method is amenable to incorporating many forms of environmental change and allows a meaningful treatment of uncertainty. [ABSTRACT FROM AUTHOR]

Aim Conservation practitioners use biological surveys to ascertain whether or not a site is occupied by a particular species. Widely used statistical methods estimate the probability that a species will be detected in a survey of an occupied site. However, these estimates of detection probability are alone not sufficient to calculate the probability that a species is present given that it was not detected. The aim of this paper is to demonstrate methods for correctly calculating (1) the probability a species occupies a site given one or more non-detections, and (2) the number of sequential non-detections necessary to assert, with a pre-specified confidence, that a species is absent from a site. Location Occupancy data for a tree frog in eastern Australia serve to illustrate methods that may be applied anywhere species' occupancy data are used and detection probabilities are < 1. Methods Building on Bayesian expressions for the probability that a site is occupied by a species when it is not detected, and the number of non-detections necessary to assert absence with a pre-specified confidence, we estimate occupancy probabilities across tree frog survey locations, drawing on information about where and when the species was detected during surveys. Results We show that the number of sequential non-detections necessary to assert that a species is absent increases nonlinearly with the prior probability of occupancy, the probability of detection if present, and the desired level of confidence about absence. Main conclusions If used more widely, the Bayesian analytical approaches illustrated here would improve collection and interpretation of biological survey data, providing a coherent way to incorporate detection probability estimates in the design of minimum survey requirements for monitoring, impact assessment and distribution modelling. [ABSTRACT FROM AUTHOR]

Overabundant wildlife can cause economic and ecological damage. Therefore population control typically seeks to maintain species' abundance within desired control limits. Efficient control requires targets, methods for estimating population size before and after control, and reliable means of forecasting population size. Demographic stochasticity, environmental variability and model uncertainty complicate these tasks. Monitoring provides critical feedback in the control process, yet examples of integrated monitoring and management are scarce. We developed an integrated Bayesian population modelling and monitoring algorithm to assist with dynamic cull control of an overabundant population. We describe components of the control algorithm and their combination to produce a structured, sequential prescription for implementing control of a kangaroo population. We demonstrate its application within a single management year and evaluate its performance over a multi-year horizon under a range of scenarios reflecting uncertainties about population dynamics. Simulation testing of the algorithm demonstrates that it provides a coherent, flexible, efficient and robust basis for managing population control. It is coherent in that connections between management objectives, models and operating rules are explicit and logically integrated. It is flexible in that the management objectives can be freely varied. It is both cost and operationally efficient because: (i) it avoids the need for an expensive, dedicated sampling process to estimate population size prior to culling; (ii) a relatively small number of culls produces reasonable population size estimates and (iii) the estimation by removal process enables direct assessment of whether control has been achieved. Lastly, it is robust because even when there is substantial uncertainty about system state and dynamics, the algorithm performs well at keeping the population under control over the duration of the management horizon. Synthesis and applications. We provide a general and flexible framework for integrated monitoring and culling when the objective is to keep a species' abundance within control limits. Our framework explicitly deals with uncertainty arising from demographic stochasticity, ecological complexity and lack of knowledge, and provides the foundation for maximizing efficiency and cost-effectiveness of control operations. Our approach could be applied in any instances where control is effected via culling. [ABSTRACT FROM AUTHOR]

“Offsetting” habitat destruction has widespread appeal as an instrument for balancing economic growth with biodiversity conservation. Requiring proponents to pay the nontrivial costs of habitat loss encourages sensitive planning approaches. Offsetting, biobanking, and biodiverse carbon sequestration schemes will play an important role in conserving biodiversity under increasing human pressures. However, untenable assumptions in existing schemes are undermining their benefits. Policies that allow habitat destruction to be offset by the protection of existing habitat are guaranteed to result in further loss of biodiversity. Similarly, schemes that allow trading the immediate loss of existing habitat for restoration projects that promise future habitat will, at best, result in time lags in the availability of habitat that increases extinction risks, or at worst, fail to achieve the offset at all. We detail concerns about existing approaches and describe how offsetting and trading policies can be improved to provide genuine benefits for biodiversity. Due to uncertainties about the way in which restored vegetation matures, we propose that the biodiversity bank should be a savings bank. Accrued biodiversity values should be demonstrated before they can be used to offset biodiversity losses. We provide recommendations about how this could be achieved in practice. [ABSTRACT FROM AUTHOR]

Good forecasts of climate change impacts on extinction risks are critical for effective conservation management responses. Species distribution models (SDMs) are central to extinction risk analyses. The reliability of predictions of SDMs has been questioned because models often lack a mechanistic underpinning and rely on assumptions that are untenable under climate change. We show how integrating predictions from fundamentally different modeling strategies produces robust forecasts of climate change impacts on habitat and population parameters. We illustrate the principle by applying mechanistic (Niche Mapper) and correlative (Maxent, Bioclim) SDMs to predict current and future distributions and fertility of an Australian gliding possum. The two approaches make congruent, accurate predictions of current distribution and similar, dire predictions about the impact of a warming scenario, supporting previous correlative-only predictions for similar species. We argue that convergent lines of independent evidence provide a robust basis for predicting and managing extinctions risks under climate change. [ABSTRACT FROM AUTHOR]

Barriers are used to achieve diverse objectives in conservation and biosecurity. In conservation management, fences are often erected to exclude introduced predators and to contain diseased animals or invasive species. Planning an efficient conservation fence involves a number of decisions, including the size and design of the enclosure. We formulated the first general framework for building a fence that minimizes long-term management costs by balancing the expense of constructing a more secure barrier against the costs of coping with more frequent failures. The approach systematically considers the range of potential solutions to a well-defined fencing problem and results in a solution that maximizes conservation return on investment. We illustrated this method by designing efficient fences to address two different conservation goals: exclusion of invasive predators from populations of threatened eastern barred bandicoots (Perameles gunnii ) and maintenance of isolated populations of healthy Tasmanian devils (Sarcophilus harrisii ). A systematic approach to conservation fencing allows the best fence design to be chosen quantitatively and defensibly. It also facilitates conservation decisions at a strategic level by allowing fencing to be compared transparently with alternative conservation management actions. [ABSTRACT FROM AUTHOR]

Abstract: The wedge-tailed eagle is Australia’s largest bird of prey and one of the largest eagles in the world. Aquila audax fleayi is an endemic Tasmanian subspecies isolated for 10,000years from the nominate subspecies on the Australian mainland. The Tasmanian wedge-tailed eagle is classified nationally and at a State level as endangered due to its small number of breeding pairs, low breeding success and high rate of mortality from unnatural causes. The subspecies experiences mortality throughout its range from shooting, poisoning, trapping, road accidents, electrocutions and collisions with wind turbines, aircraft, fences and overhead wires, which we term ‘un-natural mortality’. A portion of the subspecies’ range is managed for timber production, which can lead to disturbance of nest sites and the loss of nest trees. We use a model of the eagle population from the Bass District in northeast Tasmania to explore the relative importance of different sources of mortality and nesting habitat loss, and the potential for mitigating impacts associated with unnatural mortality, disturbance, nesting habitat loss and human access to forests. We create a habitat map including suitable nest sites and link it to a dynamic landscape population model based on life history traits and disturbance responses. Using the program RAMAS-Landscape, we model alternative forest management scenarios, ranging from no timber harvesting and a natural wildfire regime, to scenarios prescribing native forest harvesting and regeneration and different levels of conversion of native forest to plantation under the same natural wildfire regime. The results indicate that the Tasmanian wedge-tailed eagle is sensitive to unnatural mortality, plantation establishment and native forest harvesting. The predicted decline over the next 160years (∼65%) will most likely be driven largely by loss of current and potential future nest sites associated with harvesting activities, exacerbated by unnatural mortality in the wider landscape. Interventions that minimise unnatural mortality, reduce nest disturbance, and retain breeding habitat and nest sites may improve the prospects for the subspecies in the Bass District. If nest disturbance and unnatural mortality continue at the rates modelled here, the species appears to face a high risk of declining substantially in the region. [Copyright &y& Elsevier]

This article discusses the use of biodiversity investment banking in conjunction with carbon emissions credits. According to the authors, current carbon biosequestration off-setting schemes provide little biodiversity benefit. By planting monocultures on former agricultural land, these schemes can have negative impacts on biodiversity. They argue that by linking biodiversity banking to emissions trading companies can improve their public relations and reduce uncertainties about achieving environmental benefits.

Population modeling is now widely used in threatened species management and for predicting the impacts and benefits of competing management options. However, some argue that the results of models must be used with caution, particularly when data are limited. This is important, as even the simplest models would generally require more data (and knowledge) than are available in order to have complete confidence in model predictions. In particular, population models often suffer from a lack of data on demographic rates, spatial distribution, dispersal, management responses, habitat correlations and the magnitude of temporal variations. A number of authors identify behavioral and physiological responses of animals to anthropogenic noise. Assessing population level impacts of noise on cetacean populations is essential to understanding how noise impacts on the future viability of marine mammal populations. This assessment will be particularly challenging due to the difficulties associated with identifying a clear link between behavioral responses of animals and physiological impacts, observing and measuring changes in cetacean population parameters and the long lag-times over which population changes manifest in long-lived species. The urgency of the conservation situation for many of these socially important species demands immediate action, despite pervasive uncertainty. Adaptive management provides a coherent framework for action and continuous improvement under uncertainty. I review the elements of adaptive management and discuss the role of population modeling in that context. I discuss Bayesian approaches to enhancing inferential power and reducing uncertainty in model parameter estimation. I then review approaches to characterizing irreducible uncertainty with Monte Carlo methods and sensitivity analysis and conclude with a brief discussion of formal decision tools available to assist with decision making under severe uncertainty. I propose that urgently needed action should not be postponed due to uncertainty and that adaptive management provides a coherent framework for instituting immediate action with a plan for learning. [ABSTRACT FROM AUTHOR]

Aggregation of reserve networks is generally considered desirable for biological and economic reasons: aggregation reduces negative edge effects and facilitates metapopulation dynamics, which plausibly leads to improved persistence of species. Economically, aggregated networks are less expensive to manage than fragmented ones. Therefore, many reserve-design methods use qualitative heuristics, such as distance-based criteria or boundary-length penalties to induce reserve aggregation. We devised a quantitative method that introduces aggregation into reserve networks. We call the method the boundary-quality penalty (BQP) because the biological value of a land unit (grid cell) is penalized when the unit occurs close enough to the edge of a reserve such that a fragmentation or edge effect would reduce population densities in the reserved cell. The BQP can be estimated for any habitat model that includes neighborhood (connectivity) effects, and it can be introduced into reserve selection software in a standardized manner. We used the BQP in a reserve-design case study of the Hunter Valley of southeastern Australia. The BQP resulted in a more highly aggregated reserve network structure. The degree of aggregation required was specified by observed (albeit modeled) biological responses to fragmentation. Estimating the effects of fragmentation on individual species and incorporating estimated effects in the objective function of reserve-selection algorithms is a coherent and defensible way to select aggregated reserves. We implemented the BQP in the context of the Zonation method, but it could as well be implemented into any other spatially explicit reserve-planning framework. [ABSTRACT FROM AUTHOR]

Methods for reserve selection and conservation planning often ignore uncertainty. For example, presence-absence observations and predictions of habitat models are used as inputs but commonly assumed to be without error. We applied information-gap decision theory to develop uncertainty analysis methods for reserve selection. Our proposed method seeks a solution that is robust in achieving a given conservation target, despite uncertainty in the data. We maximized robustness in reserve selection through a novel method, “distribution discounting,” in which the site- and species-specific measure of conservation value (related to species-specific occupancy probabilities) was penalized by an error measure (in our study, related to accuracy of statistical prediction). Because distribution discounting can be implemented as a modification of input files, it is a computationally efficient solution for implementing uncertainty analysis into reserve selection. Thus, the method is particularly useful for high-dimensional decision problems characteristic of regional conservation assessment. We implemented distribution discounting in the zonation reserve-selection algorithm that produces a hierarchy of conservation priorities throughout the landscape. We applied it to reserve selection for seven priority fauna in a landscape in New South Wales, Australia. The distribution discounting method can be easily adapted for use with different kinds of data (e.g., probability of occurrence or abundance) and different landscape descriptions (grid or patch based) and incorporated into other reserve-selection algorithms and software. [ABSTRACT FROM AUTHOR]

Abstract: It has been widely argued that habitat fragmentation is bad for (meta)population persistence and that a high level of fragmentation is a similarly undesirable characteristic for a reserve network. However, modelling the effects of fragmentation for many species is very difficult due to high data demands and uncertainty concerning its effect on particular species. Hence, several reserve selection methods employ qualitative heuristics such as boundary length penalties that aggregate reserve network structures. This aggregation usually comes at a cost because low quality habitats will be included for the sake of increased connectivity. Here a biologically justified method for designing aggregated reserve networks based on a technique called distribution smoothing is investigated. As with the boundary length penalty, its use incurs an apparent biological cost. However, taking a step further, potential negative effects of fragmentation on individual species are evaluated using a decision-theoretic uncertainty analysis approach. This analysis shows that the aggregated reserve network (based on smoothed distributions) is likely to be biologically more valuable than a more fragmented one (based on habitat model predictions). The method is illustrated with a reserve design case study in the Hunter Valley of south-eastern Australia. The uncertainty analysis method, based on information-gap decision theory, provides a systematic framework for making robust decisions under severe uncertainty, making it particularly well adapted to reserve design problems. [Copyright &y& Elsevier]

We evaluated the utility of combining metapopulation models with landscape-level forest-dynamics models to assess the sustainability of forest management practices. We used the Brown Creeper (Certhia americana ) in the boreal forests of northern Ontario as a case study. We selected the Brown Creeper as a potential indicator of sustainability because it is relatively common in the region but is dependent on snags and old trees for nesting and foraging; hence, it may be sensitive to timber harvesting. For the modeling we used RAMAS Landscape, a software package that integrates RAMAS GIS, population-modeling software, and LANDIS, forest-dynamics modeling software. Predictions about the future floristic composition and structure of the landscape under a variety of management and natural disturbance scenarios were derived using LANDIS. We modeled eight alternative forest management scenarios, ranging in intensity from no timber harvesting and a natural fire regime to intensive timber harvesting with salvage logging after fire. We predicted the response of the Brown Creeper metapopulation over a 160-year period and used future population size and expected minimum population size to compare the sustainability of the various management scenarios. The modeling methods were easy to apply and model predictions were sensitive to the differences among management scenarios, indicating that these methods may be useful for assessing and ranking the sustainability of forest management options. Primary concerns about the method are the practical difficulties associated with incorporating fire stochasticity in prediction uncertainty and the number of model assumptions that must be made and tested with sensitivity analysis. We wrote new software to help quantify the contribution of landscape stochasticity to model prediction uncertainty. [ABSTRACT FROM AUTHOR]

Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri-urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence-absence and presence-only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species-habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow-bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available. [ABSTRACT FROM AUTHOR]

A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from ‘true zero’ or ‘false zero’ observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses. [ABSTRACT FROM AUTHOR]

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, rain-fall, 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. [ABSTRACT FROM AUTHOR]

Climate and land‐use change pose unprecedented threats to ecosystems, economies, and communities worldwide. To help mitigate the climate crisis, restoration is a rapidly growing industry used to offset carbon emissions. The most common approach is to plant fast‐growing monocultures with the aim of sequestering as much carbon as possible in the shortest time. However, there has been little economic analysis of planting options that explicitly address short and long‐term ecological risks such as fire, disease, and environmental change. Here we develop a method for quantifying ecological risks from fire to sequestration investments and show how these risks can be factored into an analysis of long‐term financial returns relative to opportunity costs. In the case study presented, we find that the apparent advantage of fast‐growing monoculture plantations is likely to be outweighed by the long‐term fire risks to the carbon stored in them. Our analytical framework provides a widely applicable approach to comparing planting options against each other and other land uses, considering key uncertainties. With climate change already manifesting through extreme weather events, rising sea levels, and shifting wildlife populations, our framework can be used to make informed decisions about the best solutions to increase carbon sequestration, reduce ecological risks, and reduce climate impacts with greater certainty. [ABSTRACT FROM AUTHOR]

Summary: Increasingly scientists and policy makers are acknowledging the importance of Indigenous participation in effective biodiversity conservation. In Australia, the recognised Indigenous estate is vast, accounting for up to 57% of the continent and comprising some of the highest priority conservation lands, including 46% of the formal National Reserve System. The Environment Protection and Biodiversity Conservation Act1999 (the Act) is Commonwealth legislation designed to protect and manage nationally and internationally important species and ecological communities, which entails specific objectives to recognise Indigenous people. However, to date the involvement of Indigenous people in implementation of the Act has been inconsistent and inadequate, particularly in the protection of the Indigenous estate, understanding and supporting Indigenous people's aspirations for Country and culturally significant species, and respecting the traditional management of species and landscapes. In this article, we will explore the key barriers and opportunities for improving Indigenous participation in biodiversity conservation under the Act. We structure our exploration using the three connected themes: (1) meaningful Indigenous engagement and participation, (2) recognition of the Indigenous Estate and (3) strengthening Indigenous‐led governance. We find that there is a pressing need and an immediate opportunity to reform and strengthen the Act to protect Indigenous Knowledge, to recognise and report on the role of Indigenous Estate, and to realise the aspirations of Indigenous peoples for improved land and sea management that strengthens people, culture and Country. [ABSTRACT FROM AUTHOR]

As with most governments worldwide, Australian governments list threatened species and proffer commitments to recovering them. Yet most of Australia's imperiled species continue to decline or go extinct and a contributing cause is inadequate investment in conservation management. However, this has been difficult to evaluate because the extent of funding committed to such recovery in Australia, like in many nations, is opaque. Here, by collating disparate published budget figures of Australian governments, we show that annual spending on targeted threatened species recovery is around U.S.$92m (AU$122m) which is around one tenth of that spent by the U.S. endangered species recovery program, and about 15% of what is needed to avoid extinctions and recover threatened species. Our approach to estimating funding needs for species recovery could be applied in any jurisdiction and could be scaled up to calculate what is needed to achieve international goals for ending the species extinction crisis. [ABSTRACT FROM AUTHOR]

The importance of expert input to spatial conservation prioritization outcomes is poorly understood. We quantified the impacts of refinements made during consultation with experts on spatial conservation prioritization of Christmas Island. There was just 0.57 correlation between the spatial conservation priorities before and after consultation, bottom ranked areas being most sensitive to changes. The inclusion of a landscape condition layer was the most significant individual influence. Changes (addition, removal, modification) to biodiversity layers resulted in a combined 0.2 reduction in correlation between initial and final solutions. Representation of rare species in top ranked areas was much greater after expert consultation; representation of widely distributed species changed relatively little. Our results show how different inputs have notably different impacts on the final plan. Understanding these differences helps plan time and resources for expert consultation. [ABSTRACT FROM AUTHOR]

Effective biodiversity conservation requires robust and transparent prioritization of management actions. However, this is often hampered by a lack of spatially‐explicit data on habitat variables and empirical data on the effect of management actions. Although approaches exist that integrate structured expert elicitation (SEE) with species distribution models (SDMs) to encode species responses across habitat gradients, difficulties remain in predicting management outcomes under different settings, at a region‐wide scale when key habitat covariates are not spatially explicit. Therefore, we developed an approach to integrate SDMs with SEE to capture expert understanding of likely outcomes of management actions for individual frog species, and use this to spatially predict the effect of management actions. We demonstrate our approach across approximately 4000 wetlands in greater Melbourne, Victoria, Australia. As a measure of management effectiveness, we used the change in predicted probability of occurrence of seven frog species at wetlands 10 years after conservation actions are implemented (or not implemented). Management effect was elicited from experts under six scenarios. Individual expert estimates were aggregated using generalized linear models that were then used to spatially predict expected management effects, and a measure of uncertainty in the prediction, at all wetlands. Predicted management effect was strongly influenced by species initial probability of occurrence, with enhancing aquatic and surrounding vegetation an effective action for most species. We discuss practical challenges and recommend solutions in the integration of SDMs and SEE for the spatial prediction of management effect. [ABSTRACT FROM AUTHOR]

Highlights • Glasshouse trials show plant water use is optimum at a relative compaction of 77%. • A relative compaction of 77% is lower than currently recommended for phytocapping. • Relative compaction ranges >86% and <67% reduce plant water use by 20% or more. • Operationally-relevant specifications of 70–83% relative compaction are recommended. Abstract There is little experimental evidence to guide the design of substrate compaction for the optimal plant water use performance of a landfill phytocap. A glasshouse study was undertaken to address this with aim to evaluate the effect of substrate compaction on the water use of a phytocap plant community. Four levels of relative compaction (RC), i.e. the ratio of dry bulk density over the standard maximum dry bulk density, (72%, 77%, 82%, and 87%) were considered. The native tree and grass species selected were typical of an Australian phytocap plant community: Themeda triandra , Microlaena stipoides , Eucalyptus camaldulensis , Eucalyptus cladocalyx , Acacia mearnsii and Allocasuarina verticillata. Plant water use was measured by weight as the difference between planted and unplanted cores over 5 drying periods occurring through the first 6 months of plant establishment traversing winter, spring and summer. Plant water use was optimal for all species at low-intermediate RC (72%, 77% and 82%), and all species except Themeda triandra , were most negatively impacted by the highest RC of 87%. The best linear model based on Akaike's Information Criterion included a second-order term for the continuous fixed factor 'RC' and the categorical fixed factor 'species'. This model showed plant water use to be optimum at a RC of 76.5% and highlighted a wide range of RC's (70–83%), for which plant water use is not <90% of this optimum. It also highlighted increasing plant water use-sensitivity to RC's beyond these ranges, with a RC > 86% and a RC < 67% leading to reductions in plant water use of 20% or more. Substrate specifications are recommended to optimize phytocap plant water use within achievable RC ranges. These can be generalized beyond application to a single species or substrate texture to inform the design and quality assurance of substrate placement for future landfill phytocaps. [ABSTRACT FROM AUTHOR]

Conservation of species under climate change relies on accurate predictions of species ranges under current and future climate conditions. To date, modelling studies have focused primarily on how changes in long‐term averaged climate conditions are likely to influence species distributions with much less attention paid to the potential effect of extreme events such as droughts and heatwaves which are expected to increase in frequency over coming decades. In this study we explore the benefits of tailoring predictor variables to the specific physiological constraints of species, or groups of species. We show how utilizing spatial predictors of extreme temperature and water availability (heat‐waves and droughts), derived from high‐temporal resolution, long‐term weather records, provides categorically different predictions about the future (2070) distribution of suitable environments for 188 mammal species across different biomes (from arid zones to tropical environments) covering the whole of continental Australia. Models based on long‐term averages‐only and extreme conditions‐only showed similarly high predictive performance tested by hold‐out cross‐validation on current data, and yet some predicted dramatically different future geographic ranges for the same species under 2070 climate scenarios. Our results highlight the importance of accounting for extreme conditions/events by identifying areas in the landscape where species may cope with average conditions, but cannot persist under extreme conditions known or predicted to occur there. Our approach provides an important step toward identifying the location of climate change refuges and danger zones that goes beyond the current standard of extrapolating long‐term climate averages. [ABSTRACT FROM AUTHOR]

1. The mesopredator release theory predicts that the density of subordinate predators will increase as dominant predators decline. Persistent debate around mesopredator release in part reflects the lack of robust, replicated experiments that test this theory, and the use of population indices that confound changes in mesopredator density and detectability. This uncertainty has immediate impacts for conservationists who are faced with managing sympatric invasive predators. 2. We used replicated experimental designs and spatially explicit models to examine whether mesopredator release of the feral cat Felis catus occurs in response to targeted control of the introduced red fox Vulpes vulpes. We surveyed three Control‐Impact paired landscapes in a region with long‐term fox control (1080 poison baiting) and conducted a Before‐After Control‐Impact Paired‐Series experiment in another region. We used fox occurrence as a simple metric of fox populations and estimated feral cat density with spatial mark–resight models. 3. Lethal fox control had varying effects on fox occurrence, consistent with variation in the duration and intensity of poison baiting. Correspondingly, responses in feral cat density ranged from negligible to a 3.7‐fold higher density in fox‐baited landscapes. At a fine spatial scale (200 m2), feral cat density was negatively associated with fox occurrence probability across both regions. These results were consistent with mesopredator release, although uncertainty was high in the region where fox control had only recently commenced. 4. Feral cat detectability also varied across the (artificially manipulated) gradients of fox occurrence probability. In one region, nonlinear models indicated that feral cats had lower detection and increased movement rates when foxes were uncommon, giving way to density suppression at high fox occurrence probabilities. 5. Synthesis and applications. Our study provides replicated, experimental evidence that dominant predator suppression can be associated with a higher mesopredator density. Mesopredator release can manifest as changes in both behaviour and density, distorting inference if these processes are not distinguished. Our results may help explain why fox control does not consistently improve native prey persistence, suggesting integrated pest management may be necessary to improve conservation outcomes. [ABSTRACT FROM AUTHOR]

Aim Species distribution models based on breeding occurrence data allow for identifying both environmental drivers and geographic areas potentially relevant for breeding. However, the interpretation of model predictions in terms of reproductive performance should be further investigated, as this information is crucial for conservation planning. We evaluated the strengths and weaknesses of a correlative modelling approach based on breeding occurrence data (presence-absence) against another approach based on vital rates' data (breeding success) for gaining insights on species persistence in the case of Great Bustards ( Otis tarda). Location Spain. Methods Breeding occurrence and breeding success were independently modelled using generalized linear models and multimodel inference analyses. Sensitivities to the way in which the population parameter (breeding success) was defined were explored by building five versions of the dependent variable. We evaluated differences in model performance and identified areas of congruence for breeding occurrence and breeding success. Results The agreement between the spatial predictions achieved by breeding occurrence and breeding success models differed substantially across databases, with the largest differences in occupied breeding areas. The deviance explained by the breeding occurrence model was 64.98% and ranged from 7.83% to 62.27% for the breeding success models. Model performance was higher for models calibrated within potential than within occupied breeding areas. Main conclusions The combination of data on both breeding occurrence and breeding success into a species distribution modelling framework showed the limitations of breeding occurrence models for inferring reproductive parameters. The definition of the population parameter as dependent variable was a key factor that strongly affected the inference of vital rates' models. The approach allowed for discriminating between areas and landscape attributes necessary for the long-term species persistence from others that may be relevant, but not so much for reproductive performance. [ABSTRACT FROM AUTHOR]

Biodiversity impact assessments under threatened species legislation often focus on individual development proposals at a single location, usually for a single species, leading to inadequate assessments of multiple impacts that accumulate over large spatial scales for multiple species. Regulations requiring ad-hoc assessments can lead to 'death by a thousand cuts,' where biodiversity is degraded by many small impacts that individually do not appear to threaten species' persistence. Spatial prioritization methods can improve the efficiency of decision-making by explicitly considering cumulative impacts of multiple proposed developments on multiple species over large spatial scales. We present an assessment approach and a unique case study in which spatial prioritization tools were used to support strategic assessment of a large development plan in Western Australia. The application of the approach helped identify relatively minor alterations to development plans that resulted in reductions in biodiversity impacts and informed expansion of the protected area network. Using these tools to assess trade-offs between conservation and development will help identify planning footprints that minimize biodiversity losses. [ABSTRACT FROM AUTHOR]

Ecological models used to forecast range change (range change models; RCM) have recently diversified to account for a greater number of ecological and observational processes in pursuit of more accurate and realistic predictions. Theory suggests that process‐explicit RCMs should generate more robust forecasts, particularly under novel environmental conditions. RCMs accounting for processes are generally more complex and data hungry, and so, require extra effort to build. Thus, it is necessary to understand when the effort of building a more realistic model is likely to generate more reliable forecasts. Here, we review the literature to explore whether process‐explicit models have been tested through benchmarking their temporal predictive performance (i.e. their predictive performance when transferred in time) and model transferability (i.e. their ability to keep their predictive performance when transferred to generate predictions into a different time) against simpler models, and highlight the gaps between the rapid development of process‐explicit RCMs and the testing of their potential improvements. We found that, out of five ecological processes (dispersal, demography, physiology, evolution, species interactions) and two observational processes (sampling bias, imperfect detection) that may influence reliability of forecasts, only the effects of dispersal, demography and imperfect detection have been benchmarked using temporally‐independent datasets. Only nine out of twenty‐nine process‐explicit model types have been tested to assess whether accounting for processes improves temporal predictive performance. We found no benchmarks assessing model transferability. We discuss potential reasons for the lack of empirical validation of process‐explicit models. Considering these findings, we propose an expanded research agenda to properly test the performance of process‐explicit RCMs, and highlight some opportunities to fill the gaps by suggesting models to be benchmarked using existing historical datasets. [ABSTRACT FROM AUTHOR]

Increasingly the importance of Indigenous participation is acknowledged as central to effective biodiversity conservation. Traditional management emphasizes the importance of a holistic, integrated approach to safeguard species and ecological communities of cultural significance. This is discordant with many instruments for biodiversity conservation. Indigenous Australians have consistently lobbied for domestic laws to be amended to establish comanagement as the preferred approach to managing significant species and ecological communities – an approach that aligns with international obligations such as the Convention on Biological Diversity and the United Nations Declaration on the Rights of Indigenous Peoples. We describe amendments to Australia's biodiversity legislation and the use of biocultural indicators that would support Traditional management of Culturally Significant Entities (species and ecological communities), and in turn, assist Australia to effectively conserve biodiversity and meet international obligations. The ongoing challenge will be in empowering Indigenous peoples and their governance structures to implement enduring change. [ABSTRACT FROM AUTHOR]

Aim: Megafire plays a crucial role in driving the distribution of biodiversity around the world. Long‐term monitoring is vital for understanding how species are impacted immediately by megafire and subsequently respond over time. However, monitoring should be designed with sufficient statistical power to detect impact and recovery. In this study, we developed a simulation framework for optimizing the design of biodiversity monitoring programmes to detect population recoveries after megafire. Location: Victoria, Australia. Time period: 2019–2020. Major taxa studied: Vertebrates. Methods: We collated species distribution models for 45 priority vertebrates most likely to respond to management after the 2019–2020 megafires in Victoria, Australia. We combined these models with fire severity maps to optimize the location of monitoring sites in and around the fire footprint. We simulated the impact of the megafires on species distributions and modelled plausible recoveries over the next 10 years. Using estimates of detectability for a suite of preferred sampling methods, we simulated monitoring at pairs of burnt and unburnt sites to evaluate the statistical power to detect the modelled recoveries. We tested the sensitivity of power to alternative monitoring designs, rates of recovery and monitoring budgets. Results: Priority regions to establish monitoring sites varied by taxonomic group. Power to detect population recoveries increased as the monitoring budget increased, as the recovery rate increased and when the proportion of sites in burnt compared with unburnt habitat increased. According to the optimal monitoring design, an AUD $9M budget could detect 90% of recoveries to pre‐fire levels in 40% of species with >80% power. Power was highest for mammals, followed by birds, reptiles and amphibians. Main conclusions: Our simulation approach allowed us to test the relative performance of alternative post‐fire monitoring designs ahead of time. Although we focused on megafire, our approach could easily be applied to detect population recoveries after any large‐scale catastrophic disturbance. [ABSTRACT FROM AUTHOR]

Many threatened species depend on climatic microrefugia, but places with harsh climates for predators may also play a refugial role. Feral cats threaten many native species in arid Australia. Although cats can persist in regions with no free water, their abundance should depend on the availability of microclimates that protect them from harsh environmental conditions. We developed a biophysical model of feral cat heat stress and used it to explore how behavior and microhabitat features influence water requirements and activity. Tests of model predictions against fine‐scale GPS and microclimate data highlight the importance of refuges, particularly rabbit burrows. Continent‐wide simulations show large but temporally varying areas of the arid zone that would be lethal for cats without access to deep or shaded burrows. Our approach can identify locations that may act as natural refuges for native prey, and where habitat management strategies may be effective in controlling cat abundance. [ABSTRACT FROM AUTHOR]

Ambitious conservation efforts are needed to stop the global biodiversity crisis. In this study, we estimate the minimum land area to secure important biodiversity areas, ecologically intact areas, and optimal locations for representation of species ranges and ecoregions. We discover that at least 64 million square kilometers (44% of terrestrial area) would require conservation attention (ranging from protected areas to land-use policies) to meet this goal. More than 1.8 billion people live on these lands, so responses that promote autonomy, self-determination, equity, and sustainable management for safeguarding biodiversity are essential. Spatially explicit land-use scenarios suggest that 1.3 million square kilometers of this land is at risk of being converted for intensive human land uses by 2030, which requires immediate attention. However, a sevenfold difference exists between the amount of habitat converted in optimistic and pessimistic land-use scenarios, highlighting an opportunity to avert this crisis. Appropriate targets in the Post-2020 Global Biodiversity Framework to encourage conservation of the identified land would contribute substantially to safeguarding biodiversity. [ABSTRACT FROM AUTHOR]

Aim Species often remain undetected at sites where they are present. However, the impact of imperfect detection on species distribution models ( SDMs) is not fully appreciated. In this paper we evaluate the influence of imperfect detection on the calibration and discrimination capacity of SDMs. We compare the performance of three types of SDMs: (1) a technique based on presence-absence data, (2) a technique based on presence-background data, and (3) a technique based on detection/non-detection data that accounts for imperfect detection. Innovation We use simulations to evaluate the impacts of imperfect detection in SDMs. This allows us to assess model performance with respect to the true objective of the models: the estimation of species distributions. We study a range of scenarios of occupancy and detection based on ecologically plausible environmental relationships and identify the circumstances in which imperfect detection affects model calibration and discrimination. We show that imperfect detection can substantially reduce the inferential and predictive accuracy of presence-absence and presence-background methods that do not account for detectability. While calibration is always affected, the influence on discrimination depends on the relationship of detectability and environmental variables. Main conclusions The performance of a model should be assessed with respect to its objectives. Comparative studies that intend to assess the performance of an SDM by evaluating its ability to predict detections rather than presences fail to reveal the benefits of accounting for detectability. Disregarding imperfect detection can have severe consequences for SDM performance, and hence for the estimation of species distributions. To date, this issue has been largely ignored in the SDM literature. Simultaneously modelling occupancy and detection does not necessarily require a greater sampling effort, but rather that data are collected so that they are informative about detectability. We recommend that consideration of imperfect detection become standard practice for species distribution modelling. [ABSTRACT FROM AUTHOR]

A range of mathematical models has been developed to infer whether a species is extinct based on a sighting record. Although observations have variable reliability, current methods for detecting extinction do not differentiate observation qualities., A more suitable approach would consider certain and uncertain sightings throughout the sighting period. We consider a small population system, meaning we assume sighting rates are constant and the population is not declining. Based on such an assumption, we develop a Bayesian method that assumes that certain and uncertain sightings occur independently and at uniform rates. These two types of sightings are connected by a common extinction date. Several rates of false sightings can be calculated to differentiate between observation types. Prior rates of false and true sightings, as well as a prior probability that the species is extant, are included. The model is implemented in Open Bugs, which uses Markov chain Monte Carlo ( MCMC)., Based on records of variable reliability, we estimate the probability that the following species are extinct: Caribbean seal Monachus tropicalis, grey , black-footed ferret Mustela nigripes, Audubon & Bachman, greater stick-nest rat Leporillus conditor, Sturt, and lesser stick-nest rat Leporillus apicalis, Gould. As further examples, Birdlife International provided the sighting records for the Alaotra grebe Tachybaptus rufolavatus, Delacour, Jamaica petrel Pterodroma caribbaea, Carte, and Pohnpei mountain starling Aplonis pelzelni, Finsch, with prior probabilities for extinction. The results are compared with existing methods, which ignore uncertain sightings. We find that including uncertain sightings can considerably change the probability that the species is extant, in either direction. However, in our examples, including the quality of the uncertain sighting made little difference. When we ignore uncertain sightings, our results agree with existing methods, especially when the last sighting was near the end of the sighting period., Synthesis and applications. Estimating the probability that a species is extinct based on sighting records is important when determining conservation priorities and allocating available resources into management activities. Having a model that allows for certain and uncertain observations throughout the sighting period better accommodates the realities of sighting quality, providing a more reliable basis for decision-making. [ABSTRACT FROM AUTHOR]

Several methods of measuring biodiversity in development‐offset trades exist. However, there is little consensus on which biodiversity metrics should be used for quantifying development impacts and assigning offsets.We simulated development impacts in a virtual landscape and offset these impacts using six biodiversity metrics: vegetation area, vegetation condition, habitat suitability, species abundance, metapopulation connectivity and rarity‐weighted richness. We tested long‐term impacts of metric choice during offsetting by combining simulated landscapes with population viability analyses.No net loss or net gains in habitat were achieved using all metrics except vegetation area and condition. Limited habitat and like‐for‐like requirements resulted in offsets exhausting available habitat in each vegetation class before offset requirements were met when using vegetation‐based metrics. We also found that impact avoidance was an important driver in how much compensation offsets could deliver. When impacts avoided high‐suitability habitats, all six metrics achieved no net loss or net gains for most species. However, when core habitats were developed, none of the metrics were able to consistently prevent population declines.Synthesis and application. When impacts on high‐quality habitat were avoided, and assuming the protection and restoration benefits can occur in practice, vegetation‐based metrics may produce offsets which deliver gains in species abundance equivalent to species‐specific metrics. However, species‐specific metrics outperformed vegetation‐based metrics when core habitats were lost. Applying avoidance measures as a first step to minimise biodiversity impacts during development will significantly improve offset outcomes for species and result in greater long‐term population benefits delivered through offsetting. [ABSTRACT FROM AUTHOR]

Aims: Reconnaissance surveys followed by monitoring are needed to assess the impact and response of biodiversity to wildfire. However, post‐wildfire survey and monitoring design are challenging due to the infrequency and unpredictability of wildfire, an urgency to initiate surveys and uncertainty about how species respond. In this article, we discuss key design considerations and quantitative tools available to aid post‐wildfire survey design. Our motivation was to inform the design of rapid surveys for threatened species heavily impacted by the 2019–2020 fires in Australia. Location: Global. Methods: We discuss a set of best practice design considerations for post‐wildfire reconnaissance surveys across a range of survey objectives. We provide examples that illustrate key design considerations from post‐fire reconnaissance surveys and monitoring programmes from around the world. Results: We highlight how the objective of post‐fire surveys drastically influences design decisions (e.g. survey location and timing). We discuss how the unpredictability of wildfire and uncertainty in the response of biodiversity complicate survey design decisions. Main conclusions: Surveys should be conducted immediately following wildfire to assess the impact on biodiversity, to ground truth fire severity mapping and to provide a benchmark from which to assess recovery. Where possible, surveys should be conducted at burnt and unburnt sites in regions with historical data so that state variables of interest can be compared with baseline estimates (i.e. BACI design). This highlights the need to have long‐term monitoring programmes already in place and be prepared to modify their design when wildfires occur. There is opportunity to adopt tools from statistics (i.e. power analysis) and conservation planning (i.e. spatial prioritization) to improve survey design. We must anticipate wildfires rather than respond to them reactively as they will occur more frequently due to climate change. [ABSTRACT FROM AUTHOR]

Species distribution modelling (SDM) is a widely used tool and has many applications in ecology and conservation biology. Spatial autocorrelation (SAC), a pattern in which observations are related to one another by their geographic distance, is common in georeferenced ecological data. SAC in the residuals of SDMs violates the 'independent errors' assumption required to justify the use of statistical models in modelling species' distributions. The autologistic modelling approach accounts for SAC by including an additional term (the autocovariate) representing the similarity between the value of the response variable at a location and neighbouring locations. However, autologistic models have been found to introduce bias in the estimation of parameters describing the influence of explanatory variables on habitat occupancy. To address this problem we developed an extension to the autologistic approach by calculating the autocovariate on SAC in residuals (the RAC approach). Performance of the new approach was tested on simulated data with a known spatial structure and on strongly autocorrelated mangrove species' distribution data collected in northern Australia. The RAC approach was implemented as generalized linear models (GLMs) and boosted regression tree (BRT) models. We found that the BRT models with only environmental explanatory variables can account for some SAC, but applying the standard autologistic or RAC approaches further reduced SAC in model residuals and substantially improved model predictive performance. The RAC approach showed stronger inferential performance than the standard autologistic approach, as parameter estimates were more accurate and statistically significant variables were accurately identified. The new RAC approach presented here has the potential to account for spatial autocorrelation while maintaining strong predictive and inferential performance, and can be implemented across a range of modelling approaches. [ABSTRACT FROM AUTHOR]