MaxEnt is a program for modelling species distributions from presence‐only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence‐only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south‐west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.

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.

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To make progress scientists need to know what other researchers have found and how they found it. However, transparency is often insufficient across much of ecology and evolution. Researchers often fail to report results and methods in detail sufficient to permit interpretation and meta-analysis, and many results go entirely unreported. Further, these unreported results are often a biased subset. Thus the conclusions we can draw from the published literature are themselves often biased and sometimes might be entirely incorrect. Fortunately there is a movement across empirical disciplines, and now within ecology and evolution, to shape editorial policies to better promote transparency. This can be done by either requiring more disclosure by scientists or by developing incentives to encourage disclosure.

Recent replication projects in other disciplines have uncovered disturbingly low levels of reproducibility, suggesting that those research literatures may contain unverifiable claims. The conditions contributing to irreproducibility in other disciplines are also present in ecology. These include a large discrepancy between the proportion of “positive” or “significant” results and the average statistical power of empirical research, incomplete reporting of sampling stopping rules and results, journal policies that discourage replication studies, and a prevailing publish-or-perish research culture that encourages questionable research practices. We argue that these conditions constitute sufficient reason to systematically evaluate the reproducibility of the evidence base in ecology and evolution. In some cases, the direct replication of ecological research is difficult because of strong temporal and spatial dependencies, so here, we propose metaresearch projects that will provide proxy measures of reproducibility.