DOI: 10.5061/dryad.f7m0cfz7n
title: Data from: Powerful yet challenging: Mechanistic Niche Models for predicting invasive species potential distribution under climate change

description: Risk assessments of invasive species present one of the most challenging
 applications of species distribution models (SDMs) due to the fundamental
 issues of distributional disequilibrium, niche changes, and truncation.
 Invasive species often occupy only a fraction of their potential
 environmental and geographic ranges, as their spatiotemporal dynamics are
 shaped by intraspecific variability, human-mediated introductions, novel
 biotic interactions, climate change, rapid selection, and ecological niche
 shifts. Traditional correlative SDMs struggle to capture these processes
 because they implicitly assume distributions are at equilibrium and rely
 on observed occurrences that seldom represent the full environmental niche
 of invasive species. Predicting future potential distributions therefore
 requires moving beyond simple climate-matching approaches to models that
 explicitly capture the mechanisms underlying species responses to their
 environment. Mechanistic Niche Models (MNMs) are process-explicit models
 that address these limitations by capturing species' performance
 across environmental gradients. By incorporating physiological constraints
 and vital rates, MNMs offer a mechanistic understanding of
 species-environment relationships and enable more robust predictions onto
 novel environments. However, a unified MNM framework remains elusive. In
 this review, we delve into the theoretical foundations of MNMs,
 emphasizing their advantages over correlative approaches, focusing on
 invasive species. We provide insights into diverse modelling techniques
 across taxa and examine the benefits and limitations of MNMs for
 predicting species distributions under novel conditions. Our systematic
 review reveals that MNMs have been applied sparingly to invasive species,
 focusing primarily on insects and plants, likely due to high data
 requirements. MNMs constitute the most suitable approach for defining
 species distribution limits under novel conditions, but their success
 depends on the relevance of input data and effective parameterisation,
 including genotype selection, model type, experimental conditions, and
 physiological curve-fitting techniques. MNMs offer significant potential
 for advancing ecological research and providing robust tools for
 evidence-based management decisions for populations in disequilibrium
 under changing environmental conditions.
description: # Powerful yet challenging: Mechanistic Niche Models for predicting
 invasive species potential distribution under climate change Dataset DOI:
 [10.5061/dryad.f7m0cfz7n](10.5061/dryad.f7m0cfz7n) ## Description of the
 data and file structure This repository contains the data of the article
 published in Ecography 10.1002/ecog.07775 entitled "Powerful yet
 challenging: Mechanistic Niche Models for predicting invasive species
 potential distribution under climate change" by Fenollosa, Erola;
 Pang, Sean Eng Howe; Briscoe, Natalie; Guisan, Antoine; Salguero-Gómez,
 Roberto" To **evaluate the existence of literature using mechanistic
 niche models to forecast invasive species distribution**, we performed a
 systematic review. The current data includes the protocol followed to
 perform the systematic review and the resulting articles, including all
 information extracted from them. The systematic search was performed on
 the 15th of September 2024.  Any questions about the data or methods could
 be sent to the corresponding and first author: Dr. Erola Fenollosa
 ([erola.fenollosa@gmail.com](mailto:erola.fenollosa@gmail.com)).  ###
 Files and variables #### File: SupplementaryMaterial\_2\_v1.xlsx
 **Description:** List of articles and extracted data that resulted of the
 systematic review of mechanistic niche models on invasive species. This
 excel file contains two sheets: - Data: List of articles and attributes
 extracted from each - Metadata: List of variables and their description
 included in the "Data" tab. #### File:
 Supplementary\_Material\_1\_v1.docx **Description:** Description of the
 methodology followed to obtain the data in
 SupplementaryMaterial_2_v1.xlsx, result of a systematic review. #### File:
 ReadMe.txt **Description:** An overall description of the data including
 the information found here.