913 may also lead to dependence between species (phylogenetic structure) or populations of species (genetic structure) with more recent divergence will tend to be more similar than those which diverged longer ago (Harvey and Pagel 1991). While such underlying structures in the data are not fundamentally problematic for statistical analyses, they tend to create two undesirable outcomes. First, model error, as well as neglected processes and variables connected to these structures, often leads to dependence structures in the model residuals, which violates the critical assumption of independence present in many models and methods (Legendre and Fortin 1989, Miller et al. 2007). Second, because predictor variables are often correlated with underlying dependence structures (e.g. climate with space), models may use predic-tors to overfit the residual dependence structure and thereby remove it, partially or completely.

Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically‐based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta‐analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web‐based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise. Species Distribution Models in EcologyLarge-scale ecological models of how species distributions and abundances vary over space and time are a critical tool in macroecology, biogeography, and conservation biology. They underpin our understanding of how biodiversity is shaped, how it is responding to anthropogenic activities, and how it might change in the future [1][2][3]. There is now a substantial literature on statistical tools for building species distribution models (SDMs) (see Glossary) and best practice in how to fit them [4][5][6][7]. SDMs also form a building block upon which more complex models, incorporating occupancy and/or abundance in space and time, can be built [8,9].

Aim:The idea of combining predictions from different models into an ensemble has gained considerable popularity in species distribution modelling, partly due to free and comprehensive software such as the R package BIOMOD. However, despite proliferation of ensemble models, we lack oversight of how and where they are used for modelling distributions, and how well they perform. Here, we present such an overview. Location: Global.Methods: Since BIOMOD is freely available and widely used by ensemble species distribution modellers, we focused on articles that apply BIOMOD, filtering the initial 852 papers identified in our structured literature search to a relevant final subset of 224 eligible peer-reviewed journal articles. Results: BIOMOD-based ensembles are used across many taxa and locations, with terrestrial plants being the most represented group of species (n = 72) and Europe being the most represented continent (n = 106). These studies often focus on forecasting distributions in the future (n = 109), and commonly use presence-only species data (n = 139) and climatic environmental predictors (n = 219). An average of six models are used in ensembles, and approximately half of ensembles weight contributions of models by their cross-validation performance. However, discussion about choices made in the modelling process and unambiguous information on the performance of ensemble models versus individual models are limited. The use of independent data to validate model performance is particularly uncommon. Main conclusions:We document the breadth of ensemble applications, but could not draw strong quantitative conclusions about the predictive performance of ensemble models, due to lack of unambiguous information reported. Understanding how and where ensembles are best used when modelling species distributions is important for enabling best choices for different applications. To enable this objective to be achieved, we provide recommendations for thorough reporting practices in a BIOMOD-based ensemble workflow. K E Y W O R D SBIOMOD, consensus forecast, ecological niche models, ensemble, habitat suitability models, species distribution model