Search

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]

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]

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]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process–explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process–explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs – regulatory planning, extinction risk, climate refugia and invasive species – we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process‐explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Aim Species distribution models (SDMs) are currently the most widely used tools in ecology for evaluating the suitability of environments for biodiversity in the face of future environmental change. In this study we seek to provide an assessment of the predictive performance of SDMs over time. How well do SDMs predict for future time periods and what factors influence predictive performance? Innovation We used a historical spatially explicit database of 1.8 million occurrence records for 318 tetrapod species from across continental Australia over the period 1950-2013. We fitted distribution models for each species to data from four multi-decadal time slices and used these to predict the species distributions up to 60 years after the data collection period for the fitted models. We evaluated predictions against observed data from the relevant time period. Predictions were made assuming either complete knowledge of changes in climatic and environmental conditions or assuming the environment and climate remained unchanged between the fitting and evaluation time periods. We used generalized linear mixed models to model variation in the predictive performance of SDMs over time in relation to a variety of factors, including the length of time between fitting and evaluation, species traits, taxonomic group and attributes of the dataset used to fit models. Main conclusions We found that most models provided useful predictions even when the period between model fitting and evaluation was 60 years (area under the receiver operator characteristic curve > 0.7 in 80% of the species evaluated). Variation in predictive performance over time was strongly related to the species range breadth (models for species with broad geographical ranges tended to perform worse than models for locally restricted species) and to the environmental coverage of occupancy data. Conversely, taxonomic group, habitat preferences and body size were not highly influential in describing the variation in predictive performance over time. [ABSTRACT FROM AUTHOR]

Each year, two or three species that had been considered to be extinct are rediscovered. Uncertainty about whether or not a species is extinct is common, because rare and highly threatened species are difficult to detect. Biological traits such as body size and range size are expected to be associated with extinction. However, these traits, together with the intensity of search effort, might influence the probability of detection and extinction differently. This makes statistical analysis of extinction and rediscovery challenging. Here, we use a variant of survival analysis known as cure rate modelling to differentiate factors that influence rediscovery from those that influence extinction. We analyse a global data set of 99 mammals that have been categorized as extinct or possibly extinct. We estimate the probability that each of these mammals is still extant and thus estimate the proportion of missing (presumed extinct) mammals that are incorrectly assigned extinction. We find that body mass and population density are predictors of extinction, and body mass and search effort predict rediscovery. In mammals, extinction rate increases with body mass and population density, and these traits act synergistically to greatly elevate extinction rate in large species that also occurred in formerly dense populations. However, when they remain extant, larger-bodied missing species are rediscovered sooner than smaller species. Greater search effort increases the probability of rediscovery in larger species of missing mammals, but has a minimal effect on small species, which take longer to be rediscovered, if extant. By separating the effects of species characteristics on extinction and detection, and using models with the assumption that a proportion of missing species will never be rediscovered, our new approach provides estimates of extinction probability in species with few observation records and scant ecological information. [ABSTRACT FROM AUTHOR]

Aim We explored the extent to which occupancy of butterflies within three biogeographic regions could be explained by vegetation structure and composition, topography and other environmental attributes; whether results were consistent among regions; and whether assumptions of closure were met with assemblage-level sampling designs. Location Chesapeake Bay Lowlands (Virginia), central Great Basin (Nevada) and western Great Basin (Nevada and California) (all USA). Methods We applied single-season occupancy models that either assumed closure or relaxed the closure assumption to data from 2013 and 2014 for 13-15 species in each region. Results Maximum single-year estimates of detection probabilities ranged from 0.14 to 0.99, and single-year occupancy from 0.28 to 0.98. The assumption of closure was met for a maximum of 54% of the species in a given region and year. Detection probabilities of > 90% of the species in each region increased as the categorical abundance of nectar or mud increased. Measures of the dominance or abundance of deciduous woody species and structural heterogeneity were included in the greatest number of occupancy models for the Chesapeake Bay Lowlands, which may in part reflect the intensity of browsing by white-tailed deer ( Odocoileus virginianus). Elevation and precipitation were prominent covariates in occupancy models for Great Basin butterflies. Main conclusions Because occupancy models do not rely on captures or observations of multiple individuals in a population, they potentially can be applied to a relatively high proportion of the species in an assemblage. However, estimation of occupancy is complicated by taxonomic, temporal and spatial variation in phenology. In multiple, widely divergent ecosystems, all or some associations between covariates and detection probability or occupancy for at least one-third of the species could not be estimated, often because a given species rarely was detected at locations with relatively low or high values of a covariate. Despite their advantages, occupancy models may leave unexplained the environmental associations with the distributions of many species. [ABSTRACT FROM AUTHOR]

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models ( SDMs) using long-term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat-induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long-term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range - with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot-spells, in driving species-climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species. [ABSTRACT FROM AUTHOR]

Fire regimes are predicted to change under climate change, with associated impacts on species and ecosystems. However, the magnitude and direction of regime changes are uncertain, as will be species' responses. For many species, how they respond will determine their medium-long-term viability. We propagate fire regime and species' response uncertainties through a 50-year viability analysis of the great desert skink, Liopholis kintorei, in central Australia, characterizing fire regime change under three scenarios. Species' response uncertainty was characterized with three competing models based on fire and habitat variables, fitted to 11 years of occupancy data. We evaluate fire management options for conserving the species, based on their robustness to uncertainty about fire and species' response. Efforts to minimize the frequency and size of fires provides the most consistent improvements to species' persistence. We show that disentangling important from unimportant uncertainties enables conservation managers to make more efficient, defensible decisions. [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]

Aim Information on patterns and determinants of spatial distributions remains poorly available for many widespread species of conservation importance. The sloth bear Melursus ursinus in the Indian subcontinent exemplifies this requirement. We aimed at assessing (1) distribution patterns of sloth bears at two spatial scales, (2) ecological and anthropogenic factors that determine bear occupancy. Location We estimated sloth bear habitat occupancy at a nationwide scale across India and at the landscape scale (38, 540 km2) in the Western Ghats of Karnataka, India. Methods We used a grid-based occupancy approach to determine sloth bear distribution patterns. At the nationwide scale, we used data from questionnaire surveys of field experts (grid-cell size ~2818 km2; 1326 cells). At the landscape scale, we conducted field surveys of bear signs (grid-cell size = 188 km2; 205 cells). Detection/non-detection data from both surveys were analyzed using occupancy modelling methods that account for imperfect detection. We examined the influence of scale-specific ecological and social covariates on patterns of occupancy. Results Nationwide, sloth bears occupied an estimated 67% of plausible bear habitat in contrast to 46% derived from methods that disregard detectability. Bear distribution was positively influenced by deciduous forests, scrub and barren areas, regions with high human densities and cultural tolerance. At the landscape scale, bears occupied 61% of the area versus 54% estimated from methods ignoring detectability. Occupancy probabilities increased with forest cover and topographic heterogeneity, whereas annual precipitation and human disturbance showed negative effects. Main conclusions Our study underlines the need to integrate human-modified areas with existing conservation landscapes. Given its widespread nature, functional role, conservation status and relatively benign interactions with humans, we propose recognizing sloth bear as an umbrella species for securing unprotected habitats in India. Protection of widespread species like the sloth bear in other landscapes may complement current conservation strategies for large mammalian communities. [ABSTRACT FROM AUTHOR]

Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions ( Eoccupied), the instability of suitable area ( Einstability) and the overlap between the current and future spatial distribution ( Eoverlap). The current dominance and occurrence were modelled in relation to a set of environmental variables using boosted regression tree ( BRT) models, under two scenarios of seedling establishment: unrestricted and highly restricted. While forecasts of spatial change were qualitatively similar for species occurrence and dominance, the models of species dominance exhibited higher metrics of model fit and predictive performance, and the spatial pattern of future dominance was less similar to the current pattern than was the case for the distributions of species occurrence. This highlights the possibility of greater changes in the spatial patterning of mangrove tree species dominance under future sea level rise. Under the restricted seedling establishment scenario, the area occupied by or dominated by a species declined between 42.1% and 93.8%, while for unrestricted seedling establishment, the area suitable for dominance or occurrence of each species varied from a decline of 68.4% to an expansion of 99.5%. As changes in the spatial patterning of dominance are likely to cause a cascade of effects throughout the ecosystem, forecasting spatial changes in dominance provides new and complementary information in addition to that provided by forecasts of species occurrence. [ABSTRACT FROM AUTHOR]

Aim Environmental impact assessments ( EIAs) often involve establishing whether a species of concern is present at the site considered for development. When surveys falsely conclude that sites are unoccupied, species prevalence in the region is cumulatively reduced. We argue that setting an acceptable level of induced decline in species occurrence provides a defensible strategy to determine minimum survey effort requirements. We investigate methods for setting such requirements. Location Eastern Australia, although we demonstrate methods applicable wherever species detection data are available to inform survey design. Methods We use probability theory to investigate required survey effort when aiming to limit decline in species occurrence. We use optimization tools to provide a method that, in addition, minimizes overall survey costs. We demonstrate the methods using data for an Australian gliding marsupial. Results A method based on ensuring a constant probability of occupied site misclassification directly links with a prescribed acceptable decline in occurrence. Optimization results indicate that, under particular conditions, a cost-efficient survey effort allocation can be achieved by setting a constant posterior probability of occupancy at sites where the species is not detected, provided the target level is set in accordance with the acceptable decline in occurrence. Our results provide a critical examination of the approach recently proposed by Wintle et al. (2012) for determining minimum survey effort requirements. Main conclusions EIA survey effort requirements should explicitly link uncertainty in establishing species absence with the broader consequences of failing to detect species presence in places subject to proposed impacts. A direct method, which involves keeping a constant probability of occupied site misclassification, only requires information about species detectability. Alternatively, a method that minimizes overall survey costs can be used. This approach also requires occupancy probability estimates so its performance relies on availability of an informative species distribution model. [ABSTRACT FROM AUTHOR]

Aim Species' ecological traits and evolutionary relatedness have gained attention in the scientific community for their important roles in the functioning and stability of ecosystems. However, conservation science is in urgent need of methods that integrate both ecological and evolutionary components of biodiversity into conservation strategies for threatened species. Here, we propose an approach to identify and locate 'ecologically and evolutionarily distinct and globally endangered' species (EcoEDGE) at broad and fine geographical scales. Location Global. Methods We used phylogeny and life-history traits to calculate evolutionary and ecological distinctiveness values of terrestrial mammals. We combined these values into one measure, EcoEDGE, which also considers species' threat status. Then, we correlated values of ecological and evolutionary distinctiveness (ED) and mapped the geographical distribution of species to locate regions with high number of mammals with great EcoEDGE scores. We also identified and located areas with high number of mammals that are highly distinct across most local assemblages in which they occur. Results Ecological and ED tended to be complementary to each other rather than redundant. Evolutionarily distinct threatened species were mostly concentrated in the tropics, while ecologically distinct threatened species were distributed across tropical and temperate regions. In addition, both ecologically and evolutionarily distinct threatened species overlapped their distributions with biodiversity hotspots, including the Atlantic forest, the Cerrado, Indo-Burma and Sundaland. Mammals with great EcoEDGE scores were concentrated in the tropics, but because they retained patterns from ecologically distinct species, their distribution stretched into temperate regions. Moreover, Africa, Australia and Indo-Malayan regions held a great number of mammals with unique ecological and evolutionary roles in the local assemblages where they occur. Main conclusions Ecologically and evolutionarily distinct and globally endangered builds upon existing strategies that include ecological traits into a species-focused prioritization approach and, thus, further informs conservation of ecosystem processes and services at multiple geographical scales. [ABSTRACT FROM AUTHOR]

Species distribution models ( SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose. [ABSTRACT FROM AUTHOR]

Species distribution models ( SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation ( SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate ( RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. [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]

Aim Much research in conservation biogeography is fundamentally dependent on obtaining reliable data on species distributions across space and time. Such data are now increasingly being generated using various types of public surveys. These data are often integrated with occupancy models to evaluate distributional patterns, range dynamics and conservation status of multiple species at broad spatio-temporal scales. Occupancy models have traditionally corrected for imperfect detection due to false negatives while implicitly assuming that false positives do not occur. However, public survey data are also prone to false-positive errors, which when unaccounted for can cause bias in occupancy estimates. We test whether false positives in a dataset collected from public surveys lead to overestimation of species site occupancy and whether estimators that simultaneously account for false-positive and false-negative errors improve occupancy estimates. Location Western Ghats, India. Methods We fit occupancy models that simultaneously account for false positives and negatives to data collected from a large-scale key informant interview survey for 30 species of large vertebrates. We tested their performance against standard occupancy models that account only for false negatives. Results Standard occupancy models that correct only for false negatives tended to overestimate species occupancy due to false-positive errors. Occupancy models that simultaneously accounted for false positives and negatives had greater support [lower Akaike's information criterion ( AIC)] and, consistent with predictions, generated systematically lower occupancy estimates than standard models. Furthermore, accounting for false positives improved the accuracy of occupancy estimates despite the added complexity to the statistical estimator. Main conclusions Integrating large-scale public surveys with occupancy modelling approaches is a powerful tool for informing conservation and management. However, in many if not most cases, it will be important to explicitly account for false positives to ensure the reliability of occupancy estimates obtained from public survey datasets such as key informant interviews, volunteer surveys, citizen science programmes, historical archives and acoustic surveys. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim Species distribution models ( SDMs) are commonly used to forecast climate change impacts. These models, however, are subject to important assumptions and limitations. By integrating two independent but complementary methods, ensemble SDMs and statistical phylogeography, we addressed key assumptions and created robust assessments of climate change impacts on species distributions while improving the conservation value of these projections. Location North American cordillera. Methods This approach was demonstrated using the arctic-alpine plant Rhodiola integrifolia ( Crassulaceae). SDMs were fitted to current and past climates using eight models, two thresholds and one to three climate data sets. These projections were combined to create a map of stable climate (refugia) since the Last Interglacial (124,000 kya). Five biogeographic hypotheses were developed based on the configuration of refugia and tested using statistical phylogeography. Projection of SDMs into the future was contingent on agreement across approaches; future projections (to 2085) used five climate data sets and two greenhouse gas scenarios. Results A multiple-refugia hypothesis was supported by both methods, confirming the assumption of niche conservatism in R. integrifolia and justifying the projection of SDMs onto future climates. Future projections showed substantial loss of climatically suitable habitat. Southern populations had the greatest losses, although the biogeographic scale of modelling may overpredict extinction risks in areas of topographic complexity. Past and future SDMs were assessed for novel values of climate variables; areas of novel climate were flagged as having higher uncertainty. Main conclusions Integrating molecular approaches with spatial analyses of species distributions under global change has great potential to improve conservation decision-making. Molecular tools can support and improve current methods for understanding the vulnerability of species to climate change and provide additional data upon which to base conservation decisions, such as prioritizing the conservation of areas of high genetic diversity to build evolutionary resiliency within populations. [ABSTRACT FROM AUTHOR]

Aim Many mangrove communities form bands parallel to the shoreline with each community dominated by a single species. However, the key determinants of mangrove species distribution across the intertidal zone are not well understood. We aimed to quantify the relationship between species' dominance and the hydroperiod (defined as the duration of inundation in a year), soil salinity and the salinity of inundating water for three dominant species, Sonneratia alba, Rhizophora stylosa and Ceriops tagal. Location An extensive (20,000 ha), largely intact mangrove forest in northern Australia, of some note as mangrove forests are threatened globally. Methods We related species dominance to the explanatory variables by applying two statistical modelling approaches: generalized linear models ( GLMs), where a set of competing models were evaluated; and boosted regression tree models ( BRTs), an approach that automatically captures interactions and nonlinear relationships between variables. Results Both GLM and BRT models achieved strong predictive performance for all species based on cross-validation, with receiver operating characteristics above 0.85 for all species, and 88% of deviance explained for S. alba, 42% for R. stylosa and 35% for C. tagal. All models indicated that the hydroperiod was the key variable influencing distribution, followed by soil salinity. The salinity of inundating water was the least informative variable in the models. Ecological space, determined by gradients in hydroperiod and soil salinity, was partitioned between the three species with little overlap. Main conclusions As anticipated changes in sea level will alter the hydroperiod, our findings are critical for global forecasting of future distributions of mangrove communities, and for the design of mitigation and adaptation measures. [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]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The article presents the authors' opinion on a work on methods to account for spatial autocorrelation in the analysis of species distributional data. They consider that though many spatial autocovariance approaches may seem redundant in the spatial processes they reflect, subtle differences in approach can have major implications for the resulting descriptions of the data and conclusions that are drawn from it. They also comment on the multicollinearity of space and environment.

Assessing the statistical power to detect changes in wildlife populations is a crucial yet often overlooked step when designing and evaluating monitoring programs. Here, we developed a simulation framework to perform spatially explicit statistical power analysis of biological monitoring programs for detecting temporal trends in occupancy for multiple species. Using raster layers representing the spatial variation in current occupancy and species‐level detectability for one or multiple observation methods, our framework simulates changes in occupancy over space and time, with the capacity to explicitly model stochastic disturbances at monitoring sites (i.e., dynamic landscapes). Once users specify the number and location of sites, the frequency and duration of surveys, and the type of detection method(s) for each species, our framework estimates power to detect occupancy trends, both across the landscape and/or within nested management units. As a case study, we evaluated the power of a long‐term monitoring program to detect trends in occupancy for 136 species (83 birds, 33 reptiles, and 20 mammals) across and within Kakadu, Litchfield, and Nitmiluk National Parks in northern Australia. We assumed continuation of an original monitoring design implemented since 1996, with the addition of camera trapping. As expected, power to detect trends was sensitive to the direction and magnitude of the change in occupancy, detectability, initial occupancy levels, and the rarity of species. Our simulations suggest that monitoring has at least an 80% chance at detecting a 50% decline in occupancy for 22% of the modeled species across the three parks over the next 15 yr. Monitoring is more likely to detect increasing occupancy trends, with at least an 80% chance at detecting a 50% increase in 87% of species. The addition of camera‐trapping increased average power to detect a 50% decline in mammals compared with using only live trapping by 63%. We provide a flexible tool that can help decision‐makers design and evaluate monitoring programs for hundreds of species at a time in a range of ecological settings, while explicitly considering the distribution of species and alternative sampling methods. [ABSTRACT FROM AUTHOR]

Monitoring is an essential component of adaptive management, and a carefully designed program is needed to ensure high-quality data and inferences over realistic time scales. Co-operation among agencies and incorporating citizen science may help enhance learning while reducing the financial costs of monitoring. We seek to realise this potential while conserving the Australian malleefowl (Leipoa ocellata). An established network of citizen scientists provide low-cost, sustainable annual monitoring data, yet the most effective actions for conserving malleefowl remain highly uncertain. The continent-wide species' distribution presents significant challenges, including multiple environmental strata to sample and numerous management jurisdictions. We outline an adaptive management framework that aims to unify malleefowl conservation priorities nationally, and target monitoring efforts. We elicited a model structure for the drivers of, and threats to, malleefowl persistence in a workshop with land managers and advocates. We parameterised 80 uncertain interactions within this structure using novel ensemble modelling techniques and identified the effectiveness of predator control as a critical uncertainty affecting malleefowl persistence. We developed a classical, spatially replicated experimental design to test whether malleefowl breed more frequently where predators are suppressed. The proposed monitoring design will rely on the contributions of several dozen land managers and 200–300 citizen scientists annually. We have developed a broad stakeholder base, a proactive communication strategy, and an agile approach to accessing resources to foster resilience and longevity in the monitoring program. If malleefowl conservation successfully adapts in response to monitoring outcomes, it will become one of the largest adaptive management programs on the planet. • Our adaptive management plan unifies work from many agencies and citizen scientists. • Malleefowl response to predator control is a critical and reducible uncertainty. • Monitoring of 36 sites can detect a 22% increase in breeding activity with 89% probability. • We take a proactive, flexible approach to sustain funding and to respond to change. • If new data prompt new actions, we will achieve successful adaptive management. [ABSTRACT FROM AUTHOR]

Large-scale disturbance events are forecast to increase in severity and frequency due to climate change. On-ground surveys are crucial for assessing the immediate impact of disturbances on biodiversity and for informing management responses. However, there are few examples where quantitative tools have guided post-disturbance survey design. In this study, we integrated species distribution modelling and spatial prioritisation to identify taxonomic and spatial gaps in surveys for 92 priority vertebrates 6 months after the 2019–20 wildfires in Australia. We predicted the pre-fire distribution of priority species, mapped locations of post-wildfire surveys that were already underway, and integrated this information with remotely-sensed fire severity maps in the tool, Zonation, to prioritise locations for new surveys across three fire severity classes (unburnt, low severity, high severity). Our results suggest that 6 months after the wildfires, surveys by government agencies had targeted 17 of 20 mammals (85%); 11 of 17 birds (65%); 10 of 17 frogs (59%); 10 of 23 reptiles (43%) and 5 of 17 fish (29%). We developed species distribution models for 63 of these species after collating 120,118 occurrence records from 6 data repositories. By predicting their distribution before the wildfires, we most efficiently identified gaps in survey effort while ensuring representation across species and fire severity classes. Our analysis provided an important 'stocktake' of the response effort to the 2019–20 wildfires in Australia and helped inform the allocation of government-funded wildfire recovery programs. Although we focus on wildfire, our approach could assess gaps in survey effort following any large-scale disturbance. • Post-fire survey effort was inconsistent across taxonomic groups. • There was little national coordination of post-fire surveys. • The representation of surveys could be drastically increased with small amounts of additional effort. • Many species and regions are yet to be surveyed after the 2019–20 megafires. [ABSTRACT FROM AUTHOR]