The rise and fall of an alien: why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea

Bosso, Luciano; Smeraldo, Sonia; Russo, Danilo; Chiusano, Maria Luisa; Bertorelle, Giorgio; Johannesson, Kerstin; Butlin, Roger K.; Danovaro, Roberto; Raffini, Francesca

doi:10.1007/s10530-022-02838-y

Cited by 50 publications

(37 citation statements)

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“…Subsequently, these data have been digitised and incorporated into data management and information systems, such as WormBase 33 , which was developed in the 1990s to aid in the prediction of AAW outbreaks. In the present study, we use forty years of AAW outbreak data to model the environmental suitability of the pest.Species distribution models (SDMs) are modern tools that are used to characterize and predict the present and future distribution of a species, using species distribution data and environmental variables that affect, directly or indirectly, the species' ecological niche or environmental suitability [34][35][36] . This provides a very useful tool for pest management activities, as it can help identify areas where the species might be present or vulnerable areas for the pest [37][38][39] .…”

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confidence: 99%

“…Species distribution models (SDMs) are modern tools that are used to characterize and predict the present and future distribution of a species, using species distribution data and environmental variables that affect, directly or indirectly, the species' ecological niche or environmental suitability [34][35][36] . This provides a very useful tool for pest management activities, as it can help identify areas where the species might be present or vulnerable areas for the pest [37][38][39] .…”

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Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

Gómez-Undiano

Musavi²,

Mushobozi³

et al. 2022

Sci Rep

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Invasive species have historically been a problem derived from global trade and transport. To aid in the control and management of these species, species distribution models (SDMs) have been used to help predict possible areas of expansion. Our focal organism, the African Armyworm (AAW), has historically been known as an important pest species in Africa, occurring at high larval densities and causing outbreaks that can cause enormous economic damage to staple crops. The goal of this study is to map the AAW’s present and potential distribution in three future scenarios for the region, and the potential global distribution if the species were to invade other territories, using 40 years of data on more than 700 larval outbreak reports from Kenya and Tanzania. The present distribution in East Africa coincides with its previously known distribution, as well as other areas of grassland and cropland, which are the host plants for this species. The different future climatic scenarios show broadly similar potential distributions in East Africa to the present day. The predicted global distribution shows areas where the AAW has already been reported, but also shows many potential areas in the Americas where, if transported, environmental conditions are suitable for AAW to thrive and where it could become an invasive species.

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confidence: 99%

Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

Gómez-Undiano

Musavi²,

Mushobozi³

et al. 2022

Sci Rep

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“…We achieved the analysis with the SDMtoolbox package in ArcGIS v. 10.8 (Brown et al, 2017 ). We implemented a Gaussian Kernel (Bosso et al, 2022 ; Mushtaq et al, 2021 ; Zhang et al, 2018 ) using QGIS software to avoid a sampling bias and help identify the highest potential suitability areas. With this, we selected the high suitable priority areas (hotspots) for conservation.…”

Section: Methodsmentioning

confidence: 99%

The effect of climate change on Arcto‐Tertiary Mexican beech forests: Exploring their past, present, and future distribution

Ames-Martínez

Luna‐Vega

Dieringer

et al. 2022

Ecology and Evolution

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Fagus mexicana Martínez (Mexican beech) is an endangered Arcto‐Tertiary Geoflora tree species that inhabit isolated and fragmented tropical montane cloud forests in eastern Mexico. Exploring past, present, and future climate change effects on the distribution of Mexican beech involves the study of spatial ecology and temporal patterns to develop conservation plans. These are key to understanding the niche conservatism of other forest communities with similar environmental requirements. For this study, we used species distribution models by combining occurrence records, to assess the distribution patterns and changes of the past (Last Glacial Maximum), present (1981–2010), and future (2040–2070) periods under two climate scenarios (SSP 3‐7.0 & SSP 5‐8.5). Next, we determined the habitat suitability and priority conservation areas of Mexican beech as associated with topography, land cover use, distance to the nearest town, and environmental variables. By considering the distribution of Mexican beech during different periods and under different climate scenarios, our study estimated that high‐impact areas of Mexican beech forests were restricted to specific areas of the Sierra Madre Oriental that constitute refugia from the Last Glacial Maximum. Regrettably, our results exhibited that Mexican beech distribution has decreased 71.3% since the Last Glacial Maximum and this trend will for the next 50 years, migrating to specific refugia at higher altitudes. This suggests that the states of Hidalgo, Veracruz, and Puebla will preserve the habitat suitability features as ecological refugia, related to high moisture and north‐facing slopes. For isolated and difficult‐to‐access areas, the proposed methods are powerful tools for relict‐tree species, which deserve further conservation.

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“…Ensemble forecasting is a well-established procedure that reduces uncertainty of predictions by single model algorithms (Watling et al 2015). We considered three modeling techniques: Generalised Linear Models (GLMs), Random Forests (RFs), and Maximum Entropy Models (Maxent), performing 10 runs for each technique (Tagliari et al 2021, Bosso et al 2022. The three algorithms were selected as they are considered among the best performing ones and, taken together, provide robust and reliable predictions (Kaky et al 2020).…”

Section: Species Distribution Modelingmentioning

confidence: 99%

A tale of two crickets: global climate and local competition shape the distribution of European Oecanthus species (Orthoptera, Gryllidae)

Labadessa¹,

Ancillotto²

2022

Frontiers of Biogeography

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The rise and fall of an alien: why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea

Cited by 50 publications

References 100 publications

Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

The effect of climate change on Arcto‐Tertiary Mexican beech forests: Exploring their past, present, and future distribution

A tale of two crickets: global climate and local competition shape the distribution of European Oecanthus species (Orthoptera, Gryllidae)

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