Can distribution modeling inform rare and endangered species monitoring in Mediterranean islands?

Mousikos, A.; Manolaki, P.; Knez, N.; Vogiatzakis, Ioannis Ν.

doi:10.1016/j.ecoinf.2021.101434

Cited by 5 publications

(7 citation statements)

References 74 publications

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“…In recent years, human activities and the impacts of the external natural environment have reduced the population sizes and distributional area of some rare and endangered species; therefore, understanding geographic distributions and habitat suitability for these species is key to protecting them (Mousikos et al, 2021;Sousa-Silva et al, 2014;Thompson et al, 2020). Detailed assessment of habitat suitability is considered as a first step in effective conservation and scientific management and can provide valuable information for future conservation F I G U R E 4 Diagrammatic summary of the framework of methods applied in this study strategies.…”

Section: Conservation Status and Protection Of D Pseudocrinitusmentioning

confidence: 99%

“…Some methods have been applied to the particular challenge of applications to species with small sample sizes, such as the jackknife procedure (Pearson et al, 2007) and the idea of creating many simple models for rare species (Breiner et al, 2015). Some of these methods have been explored using different environmental variables to improve predictions of occurrence (Ferrer‐Sánchez & Rodríguez‐Estrella, 2016; Mousikos et al, 2021; Sousa‐Silva et al, 2014; Thompson et al, 2020). Integration of ENM methods with remote sensing products allows development of models at different spatial and temporal scales (Cerrejón et al, 2021; Feilhauer et al, 2012; Guillera‐Arroita et al, 2015; Valerio et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Good news for a rare plant: Fine‐resolution distributional predictions and field testing for the critically endangered plant Dianthus pseudocrinitus

Behroozian

Peterson

Joharchi

et al. 2022

Conservat Sci and Prac

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Many endangered species are distributed in narrow geographic ranges, often at severe risk of extinction. Ecological niche modeling can be used to identify suitable areas, which can be surveyed to detect additional populations. We combined remotely sensed data (Normalized Difference Vegetation Index) and field sampling to increase distributional knowledge of the species and improve understanding of its conservation status. We identified numerous new sites for a rare plant (Dianthus pseudocrinitus) and tested model-based predictions of its full geographic distribution. Using only eight occurrences, we developed initial models, which guided detailed field sampling: 11 new sites were identified, but models were unable to predict better than random expectations. A second round of models did show significant predictive ability and pointed to a broader geographic and environmental distribution of the species. As a consequence, the conservation status of the species should be upgraded from Critically Endangered to Endangered. We identified six protected areas (Golestan, Ghorkhoud, Miandasht, Salouk, Sarani, Sarigol) that likely hold populations of this species; one area adjacent to Ghorkhoud Protected Area, if protected, would protect the best-known populations of the species. Our results highlight the importance of combining modeling with field sampling in characterizing distributions of rare and endangered species.

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Section: Conservation Status and Protection Of D Pseudocrinitusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Good news for a rare plant: Fine‐resolution distributional predictions and field testing for the critically endangered plant Dianthus pseudocrinitus

Behroozian

Peterson

Joharchi

et al. 2022

Conservat Sci and Prac

View full text Add to dashboard Cite

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“…Extinction risk is often higher for these species due to narrow geographic ranges, small population sizes, specialized habitat preferences, reduced gene flow, and higher vulnerability to environmental changes (Kruckeberg & Rabinowitz, 1985; Lavergne et al, 2005; Lomba et al, 2010; Mousikos et al, 2021). Therefore, there is great urgency for identifying and protecting habitat for rare species (Mousikos et al, 2021; Singh, 2013). However, despite being in the most need of predictive distribution modeling, rare species remain the most challenging to model.…”

Section: Introductionmentioning

confidence: 99%

“…Sample selection bias can often occur when numerous occurrence records are collected in areas more amenable to sampling, such as roadsides or areas with higher human population density (Fletcher & Fortin, 2018). Despite advancements in predictive algorithms and the pressing need for modeling rare species, there are few SDM studies addressing methods for modeling species with limited distribution data (Lomba et al, 2010; Mousikos et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Rare and endemic plant species are particularly threatened and could benefit from applications of SDMs (Breiner et al, 2015; Guisan et al, 2013). Extinction risk is often higher for these species due to narrow geographic ranges, small population sizes, specialized habitat preferences, reduced gene flow, and higher vulnerability to environmental changes (Kruckeberg & Rabinowitz, 1985; Lavergne et al, 2005; Lomba et al, 2010; Mousikos et al, 2021). Therefore, there is great urgency for identifying and protecting habitat for rare species (Mousikos et al, 2021; Singh, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?

Gaier

Resasco

2023

Ecosphere

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Understanding the ranges of rare and endangered species is central to conserving biodiversity in the Anthropocene. Species distribution models (SDMs) have become a common and powerful tool for analyzing species–environment relationships across geographic space. Although evaluating the distribution of rare species is integral to their conservation, this can be difficult when limited distribution data are available. Community science platforms, such as iNaturalist, have emerged as alternative sources for species occurrence data. Although these observations are often thought to be of lower quality than those of natural history collections, they may have potential for improving SDMs for species with few occurrence records from collections. Here, we investigate the utility of iNaturalist data for developing SDMs for a rare high‐elevation plant, Telesonix jamesii. Because methods for modeling rare species are limited in the literature, five different modeling techniques were considered, including profile methods, statistical models, and machine learning algorithms. The inclusion of iNaturalist data doubled the number of usable records for T. jamesii. We found that a random forest (RF) model using ensemble training data performed the highest of any model (area under curve = 0.98). We then compared the performance of RF models that use only natural history training data and those that use a combination of natural history (herbarium specimens) and iNaturalist training data. All models heavily relied on climate data (mean temperature of driest quarter, and precipitation of the warmest quarter), indicating that this species is under threat as climate continues to change. Validation datasets affected model fits as well. Models using only herbarium data performed slightly poorer when evaluated with cross‐validation than when validated externally with iNaturalist data. This study can serve as a model for future SDM studies of species with similar data limitations.

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Predicting habitat suitability of the critically endangered Be'er Sheva fringe‐fingered lizard

Horesh,

Ovadia

2024

Ecology and Evolution

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Anthropogenic changes, such as land use, are the main drivers causing climate change and biodiversity loss, with hundreds of thousands of species lacking sufficient habitats for their populations to persist and likely to go extinct within decades. Endemic species are more susceptible to habitat changes and are at the forefront of the biodiversity crisis. We used species distribution models to generate a relative habitat suitability map and identified the habitat requirements of the critically endangered and endemic Be'er Sheva fringe‐fingered lizard (Acanthodactylus beershebensis). The model showed that the species' suitable habitats are associated with arid loess plains characterized by scattered, low vegetation cover, primarily on north‐facing aspects, suggesting that these species‐specific habitat requirements limit its distribution. The size of the potentially suitable area within the species' historical range is 1350.73 km2. However, anthropogenic changes decreased the remaining suitable habitat to 995.04 km2. Most of this area is unprotected and at risk of further adverse anthropogenic effects. Only 91.72 km2 of this area is protected by the Israel Nature and Parks Authority, and 587.11 km2 may be considered indirectly protected because it is within military firing zones. Our study is the first attempt to map the remaining suitable habitat of A. beershebensis based on the results of a species distribution model. The results of this model can assist in prioritizing the protection of areas needed for the conservation of this critically endangered and endemic lizard species.

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Can distribution modeling inform rare and endangered species monitoring in Mediterranean islands?

Cited by 5 publications

References 74 publications

Good news for a rare plant: Fine‐resolution distributional predictions and field testing for the critically endangered plant Dianthus pseudocrinitus

Good news for a rare plant: Fine‐resolution distributional predictions and field testing for the critically endangered plant Dianthus pseudocrinitus

Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?

Predicting habitat suitability of the critically endangered Be'er Sheva fringe‐fingered lizard

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