Aim Decision-making for conservation management often involves evaluating risks in the face of environmental uncertainty. Models support decision-making by (1) synthesizing available knowledge in a systematic, rational and transparent way and (2) providing a platform for exploring and resolving uncertainty about the consequences of management decisions. Despite their benefits, models are still not used in many conservation decision-making contexts. In this article, we provide evidence of common objections to the use of models in environmental decision-making. In response, we present a series of practical solutions for modellers to help improve the effectiveness and relevance of their work in conservation decision-making.Location Global review.Methods We reviewed scientific and grey literature for evidence of common objections to the use of models in conservation decision-making. We present a set of practical solutions based on theory, empirical evidence and best-practice examples to help modellers substantively address these objections.Results We recommend using a structured decision-making framework to guide good modelling practice in decision-making and highlight a variety of modelling techniques that can be used to support the process. We emphasize the importance of participatory decision-making to improve the knowledgebase and social acceptance of decisions and to facilitate better conservation outcomes. Improving communication and building trust are key to successfully engaging participants, and we suggest some practical solutions to help modellers develop these skills.Main conclusions If implemented, we believe these practical solutions could help broaden the use of models, forging deeper and more appropriate linkages between science and management for the improvement of conservation decision-making.

Plant and animal survey detection rates are important for ecological surveys, environmental impact assessment, invasive species monitoring, and modeling species distributions. Species can be difficult to detect when rare but, in general, how detection probabilities vary with abundance is unknown. We developed a new detectability model based on the time to detection of the first individual of a species. Based on this model, the predicted detection rate is proportional to a power function of abundance with a scaling exponent between zero and one that depends on clustering of individuals. We estimated the model parameters with data from three independent datasets: searches for chenopod shrub species and coins, experimental searches for planted seedlings, and frog surveys at multiple sites in sub‐tropical forests of eastern Australia. Analyses based on the detection time and detection probability suggest that detection rate increases with abundance as predicted. The model provides a way to scale detection rates to cases of low abundance when direct estimation of detection rates is often impractical.

Substantial advances have been made in our understanding of the movement of species, including processes such as dispersal and migration. This knowledge has the potential to improve decisions about biodiversity policy and management, but it can be difficult for decision makers to readily access and integrate the growing body of movement science. This is, in part, due to a lack of synthesis of information that is sufficiently contextualized for a policy audience. Here, we identify key species movement concepts, including mechanisms, types, and moderators of movement, and review their relevance to (1) national biodiversity policies and strategies, (2) reserve planning and management, (3) threatened species protection and recovery, (4) impact and risk assessments, and (5) the prioritization of restoration actions. Based on the review, and considering recent developments in movement ecology, we provide a new framework that draws links between aspects of movement knowledge that are likely the most relevant to each biodiversity policy category. Our framework also shows that there is substantial opportunity for collaboration between researchers and government decision makers in the use of movement science to promote positive biodiversity outcomes.

Species’ movements affect their response to environmental change but movement knowledge is often highly uncertain. We now have well‐established methods to integrate movement knowledge into conservation practice but still lack a framework to deal with uncertainty in movement knowledge for environmental decisions. We provide a framework that distinguishes two dimensions of species’ movement that are heavily influenced by uncertainty: knowledge about movement and relevance of movement to environmental decisions. Management decisions can be informed by their position in this knowledge‐relevance space. We then outline a framework to support decisions around (1) increasing understanding of the relevance of movement knowledge, (2) increasing robustness of decisions to uncertainties and (3) improving knowledge on species’ movement. Our decision‐support framework provides guidance for managing movement‐related uncertainty in systematic conservation planning, agri‐environment schemes, habitat restoration and international biodiversity policy. It caters to different resource levels (time and funding) so that species’ movement knowledge can be more effectively integrated into environmental decisions.

Participants in the grains industry undertake general surveillance monitoring of grain crops for early detection of pests and diseases. Evaluating the adequacy of monitoring to ensure successful early detection relies on understanding the probability of detection of the relevant exotic crop pests and diseases. Empirical data on probability of detection is often not available. Our aim was to both gain a better understanding of how agronomists undertake visual crop surveillance, and use this insight to help inform structured expert judgments about the probability of early detection of various exotic grain pests and diseases. In our study we surveyed agronomists under a state funded program to identify survey methods used to undertake visual inspection of grain crops, and their confidence in detecting pests and diseases using the associated methods. We then elicited expert judgments on the probabilities of visual detection by agronomists of key exotic pests and diseases, and compared these estimates with the self-assessments of confidence made by agronomists. Results showed that agronomists used a systematic approach to visual crop inspection but that they were not confident in detecting exotic pests and diseases, with the exception of pest and diseases that affect leaves. They were most confident in visually detecting Barley stripe rust and Russian wheat aphid; however, confidence in detecting the latter was influenced by recent training. Expert judgments on the ability of agronomists to visually detect exotic pests and diseases early was in accordance with agronomists’ self-rated confidence of detection but highlighted uncertainty around the ability of agronomists in detecting non-leaf pests and diseases. The outcomes of the study demonstrated the utility of structured expert elicitation as a cost-effective tool for reducing knowledge gaps around the sensitivity of general surveillance for early detection, which in turn improves area freedom estimates.

No abstract