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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.

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.