Species distribution models (SDMs) constitute the most common class of models
across ecology, evolution and conservation. The advent of ready-to-use software pack
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ages 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 model
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ing 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.

/pdf/a-standard-protocol-for-reporting-species-distribution-gm7sgcp5n5.pdf

A standard protocol for reporting species distribution models

https://research.vu.nl/ws/files/121837637/Monitoring_biodiversity_in_the_Anthropocene_using_remote_sensing_in_species_distribution_models.pdf

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Ecological niche models (ENMs) are widely used statistical methods to estimate various types of species niches. After lecturing several editions of introductory courses on ENMs and reviewing numero...

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2020.1798968

Common mistakes in ecological niche models

Predictive modelling using machine learning has become very popular for spatial mapping of the environment. Models are often applied to make predictions far beyond sampling locations where new geographic locations might considerably differ from the training data in their environmental properties. However, areas in the predictor space without support of training data are problematic. Since the model has no knowledge about these environments, predictions have to be considered uncertain. 
Estimating the area to which a prediction model can be reliably applied is required. Here, we suggest a methodology that delineates the "area of applicability" (AOA) that we define as the area, for which the cross-validation error of the model applies. We first propose a "dissimilarity index" (DI) that is based on the minimum distance to the training data in the predictor space, with predictors being weighted by their respective importance in the model. The AOA is then derived by applying a threshold based on the DI of the training data where the DI is calculated with respect to the cross-validation strategy used for model training. We test for the ideal threshold by using simulated data and compare the prediction error within the AOA with the cross-validation error of the model. We illustrate the approach using a simulated case study. 
Our simulation study suggests a threshold on DI to define the AOA at the .95 quantile of the DI in the training data. Using this threshold, the prediction error within the AOA is comparable to the cross-validation RMSE of the model, while the cross-validation error does not apply outside the AOA. This applies to models being trained with randomly distributed training data, as well as when training data are clustered in space and where spatial cross-validation is applied. 
We suggest to report the AOA alongside predictions, complementary to validation measures.

/pdf/predicting-into-unknown-space-estimating-the-area-of-17juhqhm3w.pdf

Predicting into unknown space? Estimating the area of applicability of spatial prediction models

https://www.dora.lib4ri.ch/eawag/islandora/object/eawag%3A19140/datastream/PDF/Schuwirth-2019-How_to_make_ecological_models-%28published_version%29.pdf

How to make ecological models useful for environmental management

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world However, limited understanding of the accuracy and precision of models transferred to novel conditions (their ‘transferability’) undermines confidence in their predictions Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions

https://hal.archives-ouvertes.fr/hal-02346319/document

Stephen Ban

Papers

Outstanding Challenges in the Transferability of Ecological Models.