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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]

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. [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]

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]

The rising popularity of biodiversity offsetting as a tool for balancing biodiversity losses from development with equivalent gains elsewhere has sparked debate on many fronts. The fundamental questions are the following: Is offsetting good, bad, or at least better than the status quo for biodiversity conservation outcomes, and what do we need to know to decide? We present a concise synthesis of the most contentious issues related to biodiversity offsetting, categorized as ethical, social, technical, or governance challenges. In each case, we discuss avenues for reducing disagreement over these issues and identify those that are likely to remain unresolved. We argue that there are many risks associated with the unscrutinized expansion of offset policy. Nevertheless, governments are increasingly adopting offset policies, so working rapidly to clarify and-- where possible--to resolve these issues is essential. [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]

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. [ABSTRACT FROM AUTHOR]

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]

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).)

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.)

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.)