The area under the curve (AUC) of the receiving‐operating characteristic (or certain modifications of it) is almost universally used to assess the performance of species distribution models (SDMs), despite the well‐recognized problems encountered with this approach, mainly present when dealing with presence‐only data. We present a probabilistic treatment of the presence‐only problem and derive a method to assess the performance of SDMs based on the analysis of an area‐presence plot and the SDM outputs represented in both geographic and environmental spaces. We show how our method is useful to solve the two main tasks for which the AUC is used: assessing the performance of an SDM and comparing the performance of different SDMs. Our results build on previous work and constitute a rigorous method for assessing the performance of SDMs in relation to a random classifier. We establish comparisons with two of the most popular approaches used to assess the performance of an SDM, the AUC and the Boyce index, and identified cases in which our method has advantages over these two approaches. We suggest that the performance of an algorithm that classifies presence‐only data can be assessed by two factors: (a) the degree of non‐randomness of the classification at every step in the accumulation curve of presences, and (b) the amount of uninformative niche space used for the classification. The method we developed can be applied to any SDM output by using the R functions available at:
This content will become publicly available on February 1, 2025
- Award ID(s):
- 1945013
- NSF-PAR ID:
- 10488887
- Publisher / Repository:
- Elsevier B.V.
- Date Published:
- Journal Name:
- Ecological Modelling
- Volume:
- 488
- Issue:
- C
- ISSN:
- 0304-3800
- Page Range / eLocation ID:
- 110604
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract https://github.com/LauraJim/SDM‐hyperTest . -
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Abstract Aim Species distribution models (SDMs) that integrate presence‐only and presence–absence data offer a promising avenue to improve information on species' geographic distributions. The use of such ‘integrated SDMs’ on a species range‐wide extent has been constrained by the often limited presence–absence data and by the heterogeneous sampling of the presence‐only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map‐based evaluation. We build new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.
Location South and Central America.
Time Period 1979–2017.
Major Taxa Studied Hummingbirds.
Methods We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process. We validate SDMs with two schemes: (i) cross‐validation with presence–absence data and (ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.
Results The integrated SDM accounting for the spatially varying sampling intensity of the presence‐only data was one of the top performing models in both model validation schemes. Presence‐only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence‐only data for species which had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.
Main Conclusions Integrated SDMs combining presence‐only and presence–absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence–absence data.
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