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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]

By mapping land use under projections of socio-economic change, ecological changes can be predicted to inform conservation decision-making. We present a land use model that enables the fine-scale mapping of land use change under future scenarios. Its predictions can be used as input to virtually all existing spatially-explicit ecological models. Our model maps the fractional cover of land use within each grid cell, providing higher information content than discrete classes at the same spatial resolution. The method accurately reproduced land use patterns observed in the Amazon, both in terms of the allocated fractional amounts and also the direction of predicted land use changes. A small case study showcases the application of our model to reproduce patterns of agricultural expansion and natural habitat declines. The model source code is provided as an open-source R package, making this new method available to bridge the gap between socio-economic, land use and biodiversity modelling. • A model to predict fine-resolution fractional land use change, based on future land use demands. • Development version implemented as R package, making the model accessible to R-trained users. • Validation shows high prediction accuracy in Amazon basin, but the model can be fitted anywhere to predict land use change. [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]

Summary: Indigenous people are the custodians of knowledge systems that hold detailed awareness of the environment, including applications for monitoring and management to improve biodiversity and cultural outcomes. Indigenous communities are increasingly participating in programs to monitor populations of wildlife. There is a need for frameworks to guide how Indigenous priorities, aspirations and culture can be respected within monitoring programs, as well as case studies that demonstrate how Indigenous knowledge and practice can provide opportunities together with Western science practice to improve the rigour and outcomes of wildlife monitoring. Here, we describe the process of developing a monitoring program that was tailored to be carried out by Kanyirninpa Jukurrpa Indigenous ranger teams to assess the status, trend and response to the management of a threatened and culturally significant species Mankarr (Greater Bilby; Macrotis lagotis). We applied a collaborative two‐way approach, using iterative consultations, elicitations and field trials involving Indigenous and non‐Indigenous project partners to define monitoring objectives, record biocultural knowledge and tailor a sampling methodology to fit the requirements of Martu Traditional Owners. Our project focused on creating a method that would be engaging, accessible and useful for rangers who would carry out the program, and prioritized collection of relevant data for community decision‐making regarding management. We outline our key learnings for co‐design of wildlife monitoring programs on Indigenous lands. Our approach provides insights that will assist in designing other cross‐cultural or participatory monitoring programs. [ABSTRACT FROM AUTHOR]

Climate change threatens biodiversity directly by influencing biophysical variables that drive species' geographic distributions and indirectly through socio-economic changes that influence land use patterns, driven by global consumption, production and climate. To date, no detailed analyses have been produced that assess the relative importance of, or interaction between, these direct and indirect climate change impacts on biodiversity at large scales. Here, we apply a new integrated modelling framework to quantify the relative influence of biophysical and socio-economically mediated impacts on avian species in Vietnam and Australia and we find that socio-economically mediated impacts on suitable ranges are largely outweighed by biophysical impacts. However, by translating economic futures and shocks into spatially explicit predictions of biodiversity change, we now have the power to analyse in a consistent way outcomes for nature and people of any change to policy, regulation, trading conditions or consumption trend at any scale from sub-national to global. [ABSTRACT FROM AUTHOR]

The unique platypus is currently listed as 'Near-Threatened' under the IUCN Red List based on observed population declines and local extinctions, though significant uncertainty exists about its current distribution and abundance. We did the first population viability analysis across its entire range, using distribution and metapopulation data and models that integrate key threatening processes. We quantified the individual and synergistic impacts of water resource development, land clearing and invasive species on population viability of the platypus. Under current climate and threats, platypus abundance and metapopulation occupancy were predicted to respectively decline by 47%–66% and 22%–32% over 50 years. This would cause extinction of local populations across about 40% of the range. Under climate change projections (2070), increased extreme drought frequencies and duration were predicted to further expose platypuses to increased local extinctions, reducing abundance and metapopulation occupancy by 51–73% and 36–56% within 50 years respectively. Predicted estimates of key threatening processes on platypus populations strongly suggested increased risk of extinction, including listing as 'Vulnerable', under IUCN criterion A. This adds to the increasing evidence of decline and local extinction of platypus populations. There is an urgent need to implement national conservation efforts for this unique mammal by increasing surveys, tracking trends, mitigating threats and improving management of platypus habitat in rivers. [ABSTRACT FROM AUTHOR]

Advancing technology represents an unprecedented opportunity to enhance our capacity to conserve the Earth's biodiversity. However, this great potential is failing to materialize and rarely endures. We contend that unleashing the power of technology for conservation requires an internationally coordinated strategy that connects the conservation community and policy-makers with technologists. We argue an international conservation technology entity could (1) provide vision and leadership, (2) coordinate and deliver key services necessary to ensure translation from innovation to effective deployment and use of technology for on-the-ground conservation across the planet, and (3) help integrate innovation into biodiversity conservation policy from local to global scales, providing tools to monitor outcomes of conservation action and progress towards national and international biodiversity targets. This proposed entity could take the shape of an international alliance of conservation institutions or a formal intergovernmental institution. Active and targeted uptake of emerging technology can help society achieve biodiversity conservation goals. [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]

Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes. Highlights The Strategic Plan for Biodiversity 2011–2020 commits countries to achieve specific conservation targets (the Aichi Targets). How such targets are designed and implemented has far-reaching implications for biodiversity worldwide. Scenarios and models hold great promise for ensuring that conservation targets are well founded, effectively implemented, and lead to good conservation outcomes, but are not currently being used to their full potential. In particular, scenarios and models can ensure quantitative targets are based on scientific evidence. Our review highlights information gaps, where data and models are lacking, and implementation gaps, where the theory and tools exist but have seen little use. Both gaps present opportunities for collaboration between decision-makers and researchers as the global community moves towards new sets of conservation targets beyond 2020. [ABSTRACT FROM AUTHOR]

In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions. [ABSTRACT FROM AUTHOR]

Abstract: Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free. [ABSTRACT FROM AUTHOR]

Earth's extinction crisis is escalating, and threat classification schemes are increasingly important for assessing the prominent drivers and threats causing species declines. However, a complementary framework for assessing the conservation responses needed to abate these threatening processes is lacking. Here we draw on expert knowledge and published literature to develop a threat-abatement framework which groups threats based on the shared conservation goal of the actions needed to abate their impact and apply it to 1532 threatened species across the Australian continent. Our analysis shows that the most important conservation actions across Australia are to retain and restore habitat, due to the threats posed by habitat destruction and degradation (via logging, mining, urbanisation, roads, and agriculture) to 86 % of Australia's threatened species. Most species also require the effective control of invasive species and diseases (82 %) and improved fire management (66 %). Countering individual threats will not be enough to support species survival or recovery, because almost all species (89 %) require multiple, integrated management responses to redress their threats. Our threat abatement framework enables rapid identification of broad conservation responses to aid recovery of threatened species and can be applied in other regions, scales and contexts. • Threat classifications need complementary conservation responses to inform action • We develop a threat-abatement framework and apply it to Australian threatened species • The most important conservation action in Australia is to retain and restore habitat • Control of invasive species/diseases and improved fire management are also important • Greater emphasis on conservation responses is needed to redress the extinction crisis [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]

Abstract: Conservation actions frequently need to be scheduled because both funding and implementation capacity are limited. Two approaches to scheduling are possible. Maximizing gain (MaxGain) which attempts to maximize representation with protected areas, or minimizing loss (MinLoss) which attempts to minimize total loss both inside and outside protected areas. Conservation planners also choose between setting priorities based solely on biodiversity pattern and considering surrogates for biodiversity processes such as connectivity. We address both biodiversity processes and habitat loss in a scheduling framework by comparing four different prioritization strategies defined by MaxGain and MinLoss applied to biodiversity patterns and processes to solve the dynamic area selection problem with variable area cost. We compared each strategy by estimating predicted species’ occurrences within a landscape after 20years of incremental reservation and loss of habitat. By incorporating species-specific responses to fragmentation, we found that you could improve the performance of conservation strategies. MinLoss was the best approach for conserving both biodiversity pattern and process. However, due to the spatial autocorrelation of habitat loss, reserves selected with this approach tended to become more isolated through time; losing up to 40% of occurrences of edge-sensitive species. Additionally, because of the positive correlation between threats and land cost, reserve networks designed with this approach contained smaller and fewer reserves compared with networks designed with a MaxGain approach. We suggest a possible way to account for the negative effect of fragmentation by considering both local and neighbourhood vulnerability to habitat loss. [Copyright &y& Elsevier]

Conservation management actions and decisions are often defined by the location of ecological boundaries, for example, the present range of invasive or threatened species. The position of these boundaries can be cryptic, and managers must therefore directly sample sites, an expensive and time-consuming process. While accurate boundary location techniques have been considered by ecological theorists, the issue of cost-effective, or optimal boundary location has not. We propose a general framework for boundary location which incorporates both cost-efficiency and uncertainty. To illustrate its application, we use it to help locate an infectious disease front in the endangered Tasmanian devil population. The method ensures optimal spatial sampling by maximizing the expected information gained from each sample. When resources are limited, our method provides more accurate estimates of the boundary location than traditional sampling protocols. Using a formal decision theory sampling design encourages economically efficient actions, and provides defensible and transparent rationale for management actions. [Copyright &y& Elsevier]

Consequences of extreme noise exposure are obvious and usually taken into some consideration in the management of many human activities that affect either human or animal populations. However, the more subtle effects such as masking, annoyance and changes in behavior are often overlooked, especially in animals, because these subtleties can be very difficult to detect. To better understand the possible consequences of exposure to noise, this review draws from the available information on human and animal physiology and psychology, and addresses the importance of context (including physiological and psychological state resulting from any previous stressor exposure) in assessing the true meaning of behavioral responses. The current consensus is that the physiological responses to stressors of various natures are fairly stereotyped across the range of species studied. It is thus expected that exposure to noise can also lead to a physiological stress response in other species either directly or indirectly through annoyance, a secondary stressor. In fact many consequences of exposure to noise can result in a cascade of secondary stressors such as increasing the ambiguity in received signals or causing animals to leave a resourceful area, all with potential negative if not disastrous consequences. The context in which stressors are presented was found to be important not only in affecting behavioral responses, but also in affecting the physiological and psychological responses. Young animals may be particularly sensitive to stressors for a number of reasons including the sensitivity of their still-developing brains. Additionally, short exposure to stressors may result in long-term consequences. Furthermore, physiological acclimation to noise exposure cannot be determined from apparent behavioral reactions alone due to contextual influence, and negative impacts may persist or increase as a consequence of such behavioral changes. Despite the lack of information available to managers, uncertainty analysis and modeling tools can be coupled with adaptive management strategies to support decision making and continuous improvements to managing the impacts of noise on free-ranging animals. [ABSTRACT FROM AUTHOR]

Abstract: Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence–absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data—erroneous species presence–absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search. [Copyright &y& Elsevier]

Monitoring of threatened species and threatened ecosystems is critical for determining population trends, identifying urgency of management responses, and assessing the efficacy of management interventions. Yet many threatened species and threatened ecosystems are not monitored and for those that are, the quality of the monitoring is often poor. Here we provide a checklist of factors that need to be considered for inclusion in robust monitoring programs for threatened species and threatened ecosystems. These factors can be grouped under four broad themes – the design of monitoring programs, the structure and governance of monitoring programs, data management and reporting, and appropriate funding and legislative support. We briefly discuss key attributes of our checklist under these themes. Key topics in our first theme of the design of monitoring programs include appropriate objective setting, identification of the most appropriate entities to be measured, consistency in methodology and protocols through time, ensuring monitoring is long-term, and embedding monitoring into management. Under our second theme which focuses on the structure and governance of monitoring programs for threatened species and ecosystems, we touch on the importance of adopting monitoring programs that: test the effectiveness of management interventions, produce results that are relevant to management, and engage with (and are accepted by) the community. Under Theme 3, we discuss why data management is critical and highlight that the costs of data curation, analysis and reporting need to be factored into budgets for monitoring programs. This requires that appropriate levels of funding are made available for monitoring programs, beyond just the cost of data collection – a key topic examined in Theme 4. We provide examples, often from Australia, to highlight the importance of each of the four themes. We recognize that these themes and topics in our checklist are often closely inter-related and therefore provide a conceptual model highlighting these linkages. We suggest that our checklist can help identify the parts of existing monitoring programs for threatened species and threatened ecosystems that are adequate for the purpose or may be deficient and need to be improved. • Monitoring of threatened species and ecosystems is critical for many reasons. • Many threatened species and ecosystems are not monitored or are poorly monitored. • We provide a checklist of factors to guide robust threatened species monitoring. • We group these factors under four broad themes including design and implementation. • The themes are inter-related and we develop a conceptual model highlighting linkages. [ABSTRACT FROM AUTHOR]

In the last 30 years, islands and fenced exclosures free of introduced predators (collectively, havens) have become an increasingly used option for protecting Australian mammals imperiled by predation by introduced cats (Felis catus) and foxes (Vulpes vulpes). However, Australia's network of havens is not expanding in a manner that maximizes representation of all predator‐susceptible taxa, because of continued emphasis on already‐represented taxa. Future additions to the haven network will improve representation of mammals most efficiently if they fill gaps in under‐represented predator‐susceptible taxa, particularly rodents. A systematic approach to expansion could protect at least one population of every Australian predator‐susceptible threatened mammal taxon by the addition of 12 new havens to the current network. Were the current haven network to be doubled in number in a systematic manner, it could protect three populations of every Australian predator‐susceptible threatened mammal taxon. [ABSTRACT FROM AUTHOR]

Indigenous peoples globally are actively seeking better recognition of plants and animals that are of cultural significance, which encompass both species and ecological communities. Acknowledgement and collaborative management of culturally significant entities in biodiversity conservation improves environmental outcomes as well as the health and wellbeing of Indigenous people. The global diversity and complexity of Indigenous knowledge, values and obligations make achieving a universal approach to designating culturally significant entities highly unlikely. Instead, empowering local Indigenous-led governance structures with methods to identify place-based culturally significant entities will yield culturally supported results. Here we used a structured decision-making framework with objectives and biocultural measures developed by Indigenous experts, with the aim of prioritizing place-based culturally significant entities for collaborative management approaches on Bundjalung Country in coastal eastern Australia. We found some congruence and some important differences between culturally significant entities priorities and management compared with the colonial focus of threatened species management underpinned by current laws and policies. We provide reproduceable methods and a demonstration of successful local culturally significant entities designation and prioritization in an Australian context that highlights opportunities for Indigenous leadership, supported by governments in the designation and management of culturally significant entities., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Monitoring is critical to gauge the effect of environmental management interventions as well as to measure the effects of human disturbances such as climate change. Recognition of the critical need for monitoring means that, at irregular intervals, recommendations are made for new government-instigated programs or to revamp existing ones. Using insights from past well-intentioned (but sadly also often failed) attempts to establish and maintain government-instigated monitoring programs in Australia, we outline eight things that should never be done in environmental monitoring programs (if they aim to be useful). These are the following: (1) Never commence a new environmental management initiative without also committing to a monitoring program. (2) Never start a monitoring program without clear questions. (3) Never implement a monitoring program without first doing a proper experimental design. (4) Never ignore the importance of matching the purpose and objectives of a monitoring program to the design of that program. (5) Never change the way you monitor something without ensuring new methods can be calibrated with the old ones. (6) Never try to monitor everything. (7) Never collect data without planning to curate and report on it. (8) If possible, avoid starting a monitoring program without the necessary resources secured. To balance our "nevers", we provide a checklist of actions that will increase the chances a monitoring program will actually measure the effectiveness of environmental management. Scientists and resource management practitioners need to be part of a stronger narrative for, and key participants in, well-designed, implemented, and maintained government-led monitoring programs. We argue that monitoring programs should be mandated in threatened species conservation programs and all new environmental management initiatives., (© 2022. The Author(s).)

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area × habitat suitability; and condition × habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss., (© 2020 Society for Conservation Biology.)

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity., (© 2020 Society for Conservation Biology.)

Indigenous knowledge systems hold detailed information on current and past environments that can inform ecological understanding as well as contemporary environmental management. Despite its applicability, there are limited examples of indigenous knowledge being incorporated in species distribution models, which are widely used in the ecological sciences. In a collaborative manner, we designed a structured elicitation process and statistical framework to combine indigenous knowledge with survey data to model the distribution of a threatened and culturally significant species (greater bilby or mankarr [Macrotis lagotis]). We used Martu (Aboriginal people of the Australian western deserts) occurrence knowledge and presence data from track-based surveys to create predictive species distribution models with the Maxent program. Predictions of species distribution based on Martu knowledge were broader than those created with survey data. Together the Martu and survey models showed potential local declines, which were supported by Martu observation. Both data types were influenced by sampling bias that appeared to affect model predictions and performance. Martu provided additional information on habitat associations and locations of decline and descriptions of the ecosystem dynamics and disturbance regimes that influence occupancy. We concluded that intercultural approaches that draw on multiple sources of knowledge and information types may improve species distribution modeling and inform management of threatened or culturally significant species., (© 2019 Society for Conservation Biology.)

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., (© 2020 Society for Conservation Biology.)

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., (Copyright © 2020. Published by Elsevier Ltd.)

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., (© 2019 John Wiley & Sons Ltd/CNRS.)

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., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Understanding where species occur and how difficult they are to detect during surveys is crucial for designing and evaluating monitoring programs, and has broader applications for conservation planning and management. In this study, we modelled occupancy and the effectiveness of six sampling methods at detecting vertebrates across the Top End of northern Australia. We fitted occupancy-detection models to 136 species (83 birds, 33 reptiles, 20 mammals) of 242 recorded during surveys of 333 sites in eight conservation reserves between 2011 and 2016. For modelled species, mean occupancy was highly variable: birds and reptiles ranged from 0.01-0.81 and 0.01-0.49, respectively, whereas mammal occupancy was lower, ranging from 0.02-0.30. Of the 11 environmental covariates considered as potential predictors of occupancy, topographic ruggedness, elevation, maximum temperature, and fire frequency were retained more readily in the top models. Using these models, we predicted species occupancy across the Top End of northern Australia (293,017 km2) and generated species richness maps for each species group. For mammals and reptiles, high richness was associated with rugged terrain, while bird richness was highest in coastal lowland woodlands. On average, detectability of diurnal birds was higher per day of surveys (0.33 ± 0.09) compared with nocturnal birds per night of spotlighting (0.13 ± 0.06). Detectability of reptiles was similar per day/night of pit trapping (0.30 ± 0.09) as per night of spotlighting (0.29 ± 0.11). On average, mammals were highly detectable using motion-sensor cameras for a week (0.36 ± 0.06), with exception of smaller-bodied species. One night of Elliott trapping (0.20 ± 0.06) and spotlighting (0.19 ± 0.06) was more effective at detecting mammals than cage (0.08 ± 0.03) and pit trapping (0.05 ± 0.04). Our estimates of species occupancy and detectability will help inform decisions about how best to redesign a long-running vertebrate monitoring program in the Top End of northern Australia., Competing Interests: The authors have declared that no competing interests exist.

Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state-and-transition, species-distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade-offs in management outcomes through integrated modeling and structured decision-support approaches has wide application for situations in which trade-offs exist between competing conservation objectives., (© 2017 Society for Conservation Biology.)

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.

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., (© 2016 John Wiley & Sons Ltd.)

Setting aside protected areas is a key strategy for tackling biodiversity loss. Reserve effectiveness depends on the extent to which protected areas capture both known occurrences and areas likely to support the species. We assessed the effectiveness of the existing reserve network for Leadbeater's Possum (Gymnobelideus leadbeateri) and other forest-dependent species, and compared the existing reserve system to a set of plausible reserve expansion options based on area targets implied in a recent Population Viability Analysis (PVA). The existing Leadbeater's Reserve and surrounding reserve system captured 7.6% and 29.6% of cumulative habitat suitability, respectively, across the landscape. Expanded reserve scenarios captured 34% to 62% of cumulative habitat suitability. We found acute trade-offs between conserving Leadbeater's Possum habitat and conserving habitat of other forest-dependent species. Our analysis provides a template for systematically expanding and evaluating reserve expansion options in terms of trade-offs between priority species' needs., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2016 John Wiley & Sons Ltd.)

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., (© 2015 John Wiley & Sons Ltd.)

Accurate survival estimates are needed to construct robust population models, which are a powerful tool for understanding and predicting the fates of species under scenarios of environmental change. Microbats make up 17% of the global mammalian fauna, yet the processes that drive differences in demographics between species are poorly understood. We collected survival estimates for 44 microbat species from the literature and constructed a model to determine the effects of reproductive, feeding and demographic traits on survival. Our trait-based model indicated that bat species which produce more young per year exhibit lower apparent annual survival, as do males and juveniles compared with females and adults, respectively. Using 8 years of monitoring data for two Australian species, we demonstrate how knowledge about the effect of traits on survival can be incorporated into Bayesian survival analyses. This approach can be applied to any group and is not restricted to bats or even mammals. The incorporation of informative priors based on traits can allow for more timely construction of population models to support management decisions and actions., (© 2015 The Author(s).)

The nature of conservation challenges can foster a reactive, rather than proactive approach to decision making. Failure to anticipate problems before they escalate results in the need for more costly and time-consuming solutions. Proactive conservation requires forward-looking approaches to decision making that consider possible futures without being overly constrained by the past. Strategic foresight provides a structured process for considering the most desirable future and for mapping the most efficient and effective approaches to promoting that future with tools that facilitate creative thinking. The process involves 6 steps: setting the scope, collecting inputs, analyzing signals, interpreting the information, determining how to act, and implementing the outcomes. Strategic foresight is ideal for seeking, recognizing, and realizing conservation opportunities because it explicitly encourages a broad-minded, forward-looking perspective on an issue. Despite its potential value, the foresight process is rarely used to address conservation issues, and previous attempts have generally failed to influence policy. We present the strategic foresight process as it can be used for proactive conservation planning, describing some of the key tools in the foresight tool kit and how they can be used to identify and exploit different types of conservation opportunities. Scanning is an important tool for collecting and organizing diverse streams of information and can be used to recognize new opportunities and those that could be created. Scenario planning explores how current trends, drivers of change, and key uncertainties might influence the future and can be used to identify barriers to opportunities. Backcasting is used to map out a path to a goal and can determine how to remove barriers to opportunities. We highlight how the foresight process was used to identify conservation opportunities during the development of a strategic plan to address climate change in New York State. The plan identified solutions that should be effective across a range of possible futures. Illustrating the application of strategic foresight to identify conservation opportunities should provide the impetus for decision makers to explore strategic foresight as a way to support more proactive conservation policy, planning, and management., (© 2014 Society for Conservation Biology.)

Advanced warning of potential new opportunities and threats related to biodiversity allows decision-makers to act strategically to maximize benefits or minimize costs. Strategic foresight explores possible futures, their consequences for decisions, and the actions that promote more desirable futures. Foresight tools, such as horizon scanning and scenario planning, are increasingly used by governments and business for long-term strategic planning and capacity building. These tools are now being applied in ecology, although generally not as part of a comprehensive foresight strategy. We highlight several ways foresight could play a more significant role in environmental decisions by: monitoring existing problems, highlighting emerging threats, identifying promising new opportunities, testing the resilience of policies, and defining a research agenda., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The consideration of information on social values in conjunction with biological data is critical for achieving both socially acceptable and scientifically defensible conservation planning outcomes. However, the influence of social values on spatial conservation priorities has received limited attention and is poorly understood. We present an approach that incorporates quantitative data on social values for conservation and social preferences for development into spatial conservation planning. We undertook a public participation GIS survey to spatially represent social values and development preferences and used species distribution models for 7 threatened fauna species to represent biological values. These spatially explicit data were simultaneously included in the conservation planning software Zonation to examine how conservation priorities changed with the inclusion of social data. Integrating spatially explicit information about social values and development preferences with biological data produced prioritizations that differed spatially from the solution based on only biological data. However, the integrated solutions protected a similar proportion of the species' distributions, indicating that Zonation effectively combined the biological and social data to produce socially feasible conservation solutions of approximately equivalent biological value. We were able to identify areas of the landscape where synergies and conflicts between different value sets are likely to occur. Identification of these synergies and conflicts will allow decision makers to target communication strategies to specific areas and ensure effective community engagement and positive conservation outcomes., (© 2014 Society for Conservation Biology.)

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., (© 2014 John Wiley & Sons Ltd.)

In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD's claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.

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., (© 2013 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.)

Population viability analyses (PVAs) contribute to conservation theory, policy, and management. Most PVAs focus on single species within a given landscape and address a specific problem. This specificity often is reflected in the organization of published PVA descriptions. Many lack structure, making them difficult to understand, assess, repeat, or use for drawing generalizations across PVA studies. In an assessment comparing published PVAs and existing guidelines, we found that model selection was rarely justified; important parameters remained neglected or their implementation was described vaguely; limited details were given on parameter ranges, sensitivity analysis, and scenarios; and results were often reported too inconsistently to enable repeatability and comparability. Although many guidelines exist on how to design and implement reliable PVAs and standards exist for documenting and communicating ecological models in general, there is a lack of organized guidelines for designing, applying, and communicating PVAs that account for their diversity of structures and contents. To fill this gap, we integrated published guidelines and recommendations for PVA design and application, protocols for documenting ecological models in general and individual-based models in particular, and our collective experience in developing, applying, and reviewing PVAs. We devised a comprehensive protocol for the design, application, and communication of PVAs (DAC-PVA), which has 3 primary elements. The first defines what a useful PVA is; the second element provides a workflow for the design and application of a useful PVA and highlights important aspects that need to be considered during these processes; and the third element focuses on communication of PVAs to ensure clarity, comprehensiveness, repeatability, and comparability. Thereby, DAC-PVA should strengthen the credibility and relevance of PVAs for policy and management, and improve the capacity to generalize PVA findings across studies., (© 2013 Society for Conservation Biology.)

The importance of accounting for economic costs when making environmental-management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population-management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost-efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making., (© 2013 Society for Conservation Biology.)