Monitoring and predicting the potential distribution of alien plant species in arid ecosystem using remotely-sensed data

Halmy, Marwa Waseem A.; Fawzy, Manal; Ahmed, Dalia A.; Saeed, Nouran M.; Awad, Mohamed A.

doi:10.1016/j.rsase.2018.10.005

Cited by 9 publications

(5 citation statements)

References 58 publications

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“…estimated actual and potential evapotranspiration; Abatzoglou, 2013; Senay et al., 2013) and those based on remotely sensed information (e.g. indices representing surface water; Halmy et al., 2019). We also included information on topography (e.g.…”

Section: Methodsmentioning

confidence: 99%

Modelling presence versus abundance for invasive species risk assessment

Jarnevich

Sofaer

Engelstad

2021

Diversity and Distributions

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Aim Invasive species prevention and management can be guided by comparisons of invasion risk across space and among species. Species distribution models are widely used to assess invasion risk and typically estimate suitability for species presence. However, suitability for presence may not capture patterns of abundance and impact. We asked how models estimating suitability for presence versus suitability for abundance aligned in their implications for risk assessment. Location Western United States. Methods We developed ensembles of species distribution models for presence and for abundance for four invasive plants. We visualized the distribution of presence and abundance in environmental and geographic space and compared model outputs using criteria relevant for decision‐making: a comparison of risk across management units for each species, and a ranking of risk among species for each management unit. Results We found good overall agreement between models of presence versus abundance in the relative risk across management units and among species. However, the area predicted to be suitable for invasive species presence was often substantially higher than the area predicted to be suitable for abundance, especially within uninvaded management units. Main conclusions Models of suitability for invasive species presence and abundance yielded similar assessments of relative risk in comparisons across space and species. In addition, we found patterns of presence and abundance in environmental space can guide modelling decisions and model interpretation. Suitability for abundance can improve relative risk assessment when abundance locations occupy a well‐defined subset of the environmental space corresponding to presence. Where abundance locations occur throughout this environmental space, as was particularly striking for Taeniatherum caput‐medusae, suitability for presence may better reflect risk of ongoing population increases and spread. This species is at risk of becoming abundant across a substantial portion of the western United States.

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Section: Methodsmentioning

confidence: 99%

Modelling presence versus abundance for invasive species risk assessment

Jarnevich

Sofaer

Engelstad

2021

Diversity and Distributions

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“…Estas diferencias podrían atribuirse a que el nicho climático de las EEI no se ha conservado entre la región nativa y de invasión en Ecuador. Dicho aspecto tendría importantes implicaciones para la invasión, ya que las especies podrían tolerar nuevos climas y expandir su invasión más allá de las condiciones favorables conocidas en las regiones nativas (Halmy et al, 2019). Se conoce que para muchas EEI la similitud climática entre los rangos nativos y de invasión puede facilitar la invasión (Petitpierre et al, 2012;Obiakara y Fourcade, 2018).…”

Section: Discussionunclassified

Invasiones Biológicas en Agroecosistemas De Ecuador Continental: Nicho Ecológico De Especies Exóticas Y Cultivos Agrícolas Bajo Riesgo

Ferrer-Sánchez¹,

Jacho-Saa

Zambrano

et al. 2021

Acta biol. Colomb.

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En Ecuador las especies exóticas invasoras (EEI) provocan consecuencias negativas en los aspectos ecológicos, económicos y de seguridad alimentaria. Los agroecosistemas hacen parte de los sectores productivos a nivel mundial, pero son vulnerables a sufrir invasiones biológicas por la constante actividad humana y por el traslado de vegetación, tierra y semillas, por lo que deben ser constantemente monitoreados, pues desempeñan un papel importante en la economía al ser fuente de empleo. El objetivo de esta investigación fue evaluar la influencia potencial de las EEI sobre los agroecosistemas de Ecuador continental a través del modelado del nicho ecológico. Se usó como método de modelación el algoritmo de máxima entropía y se emplearon los registros de presencia de seis especies de plantas y cuatro de insectos en sus regiones nativas y en zonas invadidas a nivel mundial. Los registros provienen de Global Biodiversity Information Facility y de Tropicos. Como variables explicativas se emplearon 19 variables bioclimáticas y seis variables de vegetación. Se obtuvieron los mapas de distribución geográfica potencial, las áreas de superposición de la distribución de las especies y la delimitación de las zonas de mayor riesgo. Se determinó que las condiciones ambientales de las regiones Sierra y Amazónica son idóneas para una posible invasión de seis y siete especies. Además, más del 50 % de la cobertura agropecuaria del país podría ser afectada por las especies Wasmannia rochai, Spondias purpurea L., Lissachatina fulica y Conium maculatum L., siendo los cultivos de ciclo corto los más vulnerables a la invasión por estas especies.

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“…Given the similar spectral responses among plant species, several studies reported a decrease in classification accuracy when variables derived from RS data were used alone [5,106,110,111]. Conversely, many studies have reported not only that adding a spectral variable to environmental variables increases OCC accuracy [82,[110][111][112], particularly when classifying climate-unstructured plant species [59], but also that the spectral variable often contributes the most [36,46]. Two other studies reported that adding spectral variables did enhance the spatial scale of the map substantially [54,106].…”

Section: Combining Variables Improves Occ Performancementioning

confidence: 99%

One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

Rapinel

Hubert‐Moy

2021

Remote Sensing

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Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.

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Monitoring and predicting the potential distribution of alien plant species in arid ecosystem using remotely-sensed data

Cited by 9 publications

References 58 publications

Modelling presence versus abundance for invasive species risk assessment

Modelling presence versus abundance for invasive species risk assessment

Invasiones Biológicas en Agroecosistemas De Ecuador Continental: Nicho Ecológico De Especies Exóticas Y Cultivos Agrícolas Bajo Riesgo

One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

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