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Funder: Balfour Studentship, Department of Zoology, University of Cambridge, There is an increasing expectation on the private sector to address biodiversity impacts and contribute towards global conservation goals. Appropriate evidence use can help businesses avoid biodiversity losses and realise gains, reduce ineffective or suboptimal action, whilst minimising biodiversity-related risks and securing opportunities from engaging with biodiversity. We review the status of evidence-based action in the private sector, where previous studies have identified concerning trends, and explore the barriers that may currently be hindering practice. To learn from this, and improve the status quo, we propose a set of principles for evidence-based biodiversity impact mitigation. We outline tools and resources that can help businesses move towards evidence-based practice and achieve each of these principles. Meeting these principles would improve the biodiversity outcomes from businesses’ biodiversity related actions. However, for business action to contribute more fully to global conservation goals, broader political and socio-economic issues also need addressing

Funder: Arcadia Fund; Id: http://dx.doi.org/10.13039/100012088, Funder: MAVA Foundation; Id: http://dx.doi.org/10.13039/100013324, Funder: The David and Claudia Harding Foundation, Funder: University of Cambridge, Department of Zoology; Id: http://dx.doi.org/10.13039/501100000735, Making the reasoning and evidence behind conservation management decisions clear and transparent is a key challenge for the conservation community. Similarly, combining evidence from diverse sources (e.g., scientific and local knowledge) into decision-making is also difficult. Our group of conservation researchers and practitioners has co-produced an intuitive tool and template (Evidence-to-Decision [E2D] tool: www.evidence2decisiontool.com) to guide practitioners through a structured process to transparently document and report the evidence and reasoning behind decisions. The tool has three major steps: (1). Define the Decision Context; (2). Gather Evidence; and (3). Make an Evidence-Based Decision. In each step, practitioners enter information (e.g., from the scientific literature, practitioner knowledge and experience, and costs) to inform their decision-making and document their reasoning. The tool packages this information into a customized downloadable report (or is documented if using the offline template), which we hope can stimulate the exchange of information on decisions within and between organizations. By enabling practitioners to revisit how and why past decisions were made, and integrate diverse forms of evidence, we believe our open-access tool's template can help increase the transparency and quality of decision-making in conservation.

Data availability statement: No data was used in this study. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1002/2688-8319.12032. Supporting Information: eso312032-sup-0001-SuppMat.docx (21.1 KB) available at: https://besjournals.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2F2688-8319.12032&file=eso312032-sup-0001-SuppMat.docx. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Copyright © 2021 The Authors. 1. To be effective, the next generation of conservation practitioners and managers need to be critical thinkers with a deep understanding of how to make evidence-based decisions and of the value of evidence synthesis. 2. If, as educators, we do not make these priorities a core part of what we teach, we are failing to prepare our students to make an effective contribution to conservation practice. 3. To help overcome this problem we have created open access online teaching materials in multiple languages that are stored in Applied Ecology Resources. So far, 117 educators from 23 countries have acknowledged the importance of this and are already teaching or about to teach skills in appraising or using evidence in conservation decision-making. This includes 145 undergraduate, postgraduate or professional development courses. 4. We call for wider teaching of the tools and skills that facilitate evidence-based conservation and also suggest that providing online teaching materials in multiple languages could be beneficial for improving global understanding of other subject areas. Making informed conservation and ecosystem management choices is based upon a sound understanding of the relevant evidence. There is an increasing wealth of conservation science available, and access to this is becoming easier. But, are conservation practitioners being trained to utilize this information? In conservation, decision-making is often based upon past experience or expert knowledge, as opposed to the full body of scientific literature (e.g., Pullin, Knight, Stone, & Charman, 2004; Rafidimanantsoa, Poudyal, Ramamonjisoa, & Jones, 2018). The failure to include scientific evidence in decision-making has the potential to reduce the effectiveness of management, or even lead to detrimental actions being undertaken (Walsh, Dicks, & Sutherland, 2015). Evidence-based conservation (EBC) seeks to avoid this by providing tools to facilitate and inform decision-making. To do this, scientific evidence is collated and critically appraised for its quality and relevance, and integrated with other knowledge, experience, values and costs (Sutherland, Pullin, Dolman, & Knight, 2004). Wider adoption of EBC requires conservation professionals to be trained in its principles and taught how to use it to inform conservation decision-making. MAVA Foundation; Arcadia Fund.

Monitoring the impacts of anthropogenic threats and interventions to mitigate these threats is key to understanding how to best conserve biodiversity. Ecologists use many different study designs to monitor such impacts. Simpler designs lacking controls (e.g. Before-After (BA) and After) or pre-impact data (e.g. Control-Impact (CI)) are considered to be less robust than more complex designs (e.g. Before-After Control-Impact (BACI) or Randomised Controlled Trials (RCTs)). However, we lack quantitative estimates of how much less accurate simpler study designs are in ecology. Understanding this could help prioritise research and weight studies by their design’s accuracy in meta-analysis and evidence assessment. 2. We compared how accurately five study designs estimated the true effect of a simulated environmental impact that caused a step-change response in a population’s density. We derived empirical estimates of several simulation parameters from 47 ecological datasets to ensure our simulations were realistic. We measured design performance by determining the percentage of simulations where: (i) the true effect fell within the 95% Confidence Intervals of effect size estimates, and (ii) each design correctly estimated the true effect’s direction and magnitude. We also considered how sample size affected their performance. 3. We demonstrated that BACI designs performed: 1.3-1.8 times better than RCTs; 2.9-4.2 times vs BA; 3.2-4.6 times vs CI; and 7.1-10.1 times vs After designs (depending on sample size), when correctly estimating true effect’s direction and magnitude to within ±30%. Although BACI designs suffered from low power at small sample sizes, they outperformed other designs for almost all performance measures. Increasing sample size improved BACI design accuracy but only increased the precision of simpler designs around biased estimates. 4. Synthesis and applications. We suggest that more investment in more robust designs is needed in ecology since inferences from simpler designs, even with large sample sizes may be misleading. Facilitating this requires longer-term funding and stronger research-practice partnerships. We also propose ‘accuracy weights’ and demonstrate how they can weight studies in three recent meta-analyses by accounting for study design and sample size. We hope these help decision-makers and meta-analysts better account for study design when assessing evidence.