A problem with variable selection in a comparison of correlative and process‐based species distribution models: Comments on Higgins et al., 2020

Booth, Trevor H.

doi:10.1002/ece3.7496

Cited by 4 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 99%

“…For example, models that integrate biological knowledge in addition to climatic variables produce more accurate models than those based on climate data alone (Low et al, 2020 ). In addition, the choice of climate variables can have important implications on results (see Booth, 2021 ). Furthermore, climate variables have been found to be more closely associated with some species compared to others, even when the species have overlapping distributions (Shabani et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Climate suitability analyses compare the distributions of invasive knotweeds in Europe and North America with the source localities of their introduced biological control agents

Andersen,

Elkinton

2023

Ecology and Evolution

View full text Add to dashboard Cite

Climate suitability analyses based on ecological niche modeling provide a powerful tool for biological control practitioners to assess the likelihood of establishment of different candidate agents prior to their introduction in the field. These same analyses could also be performed to understand why some agents establish more easily than others. The release of three strains of Aphalara itadori (Shinji) (Hemiptera: Pysllidae), each from a different source locality in Japan, for the biological control of invasive knotweed species, Reynoutria spp. Houtt. (Caryophyllales: Polygonaceae), provides an important opportunity to compare the utility of climate suitability analyses for identifying potential climate‐based limitations for successful biological control introductions. Here, we predict climate suitability envelopes for three target species of knotweed in Europe and two target species of knotweed in North America and compare these suitability estimates for each of these species to the source localities of each A. itadori strain. We find that source locality of one strain, the Kyushu strain, has little‐to‐no suitability compared to other locations in Japan based on knotweed records from Europe, supporting an earlier study based on North American Japanese knotweed records. The source locality of a second strain, the Murakami strain, was predicted to have medium‐to‐high suitability based on records of knotweeds from North America. In contrast, European records of Reynoutria × bohemica Chrtek & Chrtková and Reynoutria sachalinensis (F. Schmidt) Nakai predicted no suitability for this locality compared to other locations in Japan, while European records for Reynoutria japonica Houtt. predicted low suitability. The source locality of the final strain, the Hokkaido strain, was predicted as having medium‐to‐high suitability based on knotweed records of all examined species from both North America and Europe.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Climate suitability analyses compare the distributions of invasive knotweeds in Europe and North America with the source localities of their introduced biological control agents

Andersen,

Elkinton

2023

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…Furthermore, we show that we cannot just rely on model performance and/or validation metrics to tell us if our model predicts the species' habitat requirements correctly. We first need to choose both the variables (Austin & Van Niel, 2011; Booth, 2021; Gardner et al, 2019) and the occurrence data that are relevant to our research question (Guillera‐Arroita et al, 2015) based on the basic knowledge of our species' ecology, in order to obtain appropriate results on which to base applied management and conservation decisions. This is especially important if their application is required at a local level and for species that react to the environment at large spatial scales.…”

Section: Discussionmentioning

confidence: 99%

“…Selecting appropriate environmental variables is the key to building proper models. If such models are not ecologically relevant to the species and cannot properly represent conditions that drive species' distribution, results and conclusions can be severely prejudiced (Austin & Van Niel, 2011; Booth, 2021; Gardner et al, 2019). Our results confirm in a real‐world case that we cannot trust the models to choose the most important variables if they are not previously “groomed” by expert opinion and knowledge of the species.…”

Section: Discussionmentioning

confidence: 99%

Importance of data selection and filtering in species distribution models: A case study on the Cantabrian brown bear

et al. 2022

View full text Add to dashboard Cite

Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers and filtering species' occurrences taking their biases into account are key factors to consider before modeling. In this case study, we address five common problems arising during the selection of input data for presence‐only SDMs on an example of a generalist species: the endangered Cantabrian brown bear. First, we focus on the selection of environmental variables that may drive its distribution, testing if climatic variables should be considered at a 1‐km analysis grain. Second, we investigate how filtering the species' data in view of (1) their collection procedures, (2) different time frames, (3) dispersal areas, and (4) subpopulations affects the performance and outputs of the models at three different spatial analysis grains (500 m, 1 km, and 5 km). Our results show that models with different input data yielded only minor differences in performance and behaved properly in terms of model validation, although coarsening the analysis grain deteriorated model performance. Still, the contribution of individual variables and the habitat suitability predictions differed among models. We show that a combination of limited data availability and poor selection of environmental variables can lead to inaccurate predictions. Specifically for the brown bear, we conclude that climatic variables should not be considered for exploring habitat suitability and that the best input data for modeling habitat suitability in the study area originate from (1) observations and traces from the (2) most recent period (2006–2019) in which the population is expanding, (3) not considering cells of dispersing bear occurrences and (4) modeling subpopulations independently (as they show distinct habitat preferences). In conclusion, SDMs can serve as a useful tool for generalist species including all available data; still, expert evaluation from the perspective of data suitability for the purpose of modeling and possible biases is recommended. This is especially important when the results are intended for management and conservation purposes at the local level, and for species that respond to the environment at coarse analysis grains.

show abstract

Transferability of correlative and process‐based species distribution models revisited: A response to Booth

Higgins

Larcombe

Beeton

et al. 2021

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

A problem with variable selection in a comparison of correlative and process‐based species distribution models: Comments on Higgins et al., 2020

Cited by 4 publications

References 9 publications

Climate suitability analyses compare the distributions of invasive knotweeds in Europe and North America with the source localities of their introduced biological control agents

Climate suitability analyses compare the distributions of invasive knotweeds in Europe and North America with the source localities of their introduced biological control agents

Importance of data selection and filtering in species distribution models: A case study on the Cantabrian brown bear

Transferability of correlative and process‐based species distribution models revisited: A response to Booth

Contact Info

Product

Resources

About