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Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia.

Wakie TT, Evangelista PH, Jarnevich CS, Laituri M - PLoS ONE (2014)

Bottom Line: Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC  = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC  = 0.95).Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora.Our methods can also be replicated for managing invasive species in other East African countries.

View Article: PubMed Central - PubMed

Affiliation: Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado, United States of America; Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado, United States of America.

ABSTRACT
We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m2 spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species- occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC  = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC  = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.

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Long term rainfall pattern in Afar.Average mean monthly precipitation for Melka Werer, Dufti, and Assaita stations (1968–2001). The graph shows a distinct S-N aridity gradient between Melka Werer and Assaita.
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pone-0112854-g004: Long term rainfall pattern in Afar.Average mean monthly precipitation for Melka Werer, Dufti, and Assaita stations (1968–2001). The graph shows a distinct S-N aridity gradient between Melka Werer and Assaita.

Mentions: We found that MODIS Vegetation Indices (VIs) are highly useful for mapping P. juliflora in the extensive land of the Afar. The phenological signals of P. juliflora were best detected by the November EVI and April NDVI MODIS predictors (Table 1). November represents hagay to Afar people, a cold and dry period early in the dry season. During this time, the foliage of most woody shrubs and trees remains green, while herbaceous flora, such as annual grasses and agricultural crops, become less green, creating phenological contrasts for better discrimination of woody vegetation. At the end of the dry season, P. juliflora remains green, while woody shrubs and trees lose most of their foliage or take on a yellow coloration due to water stress (personal observation). In addition, P. juliflora takes advantage of its deep root systems [67] and the moisture from the short rainy season (between March and April and referred by Afar people as hugum) to remain green (Figure 4). These differences were likely detected by the dry season VIs (November, October and December EVIs), and the short rainy season hugum VIs (April and March NDVIs, and March EVI). The trend for NDVI and EVI was similar but EVI values were lower (Figure 2). EVI values are generally lower as they avoid saturation in high biomass areas [29]. In mapping current distributions, we hypothesize that EVI was the top predictor because it was able to detect the dense P. juliflora thickets that often possess high biomass. Wet season NDVI and dry season EVI predictors highly contributed to the model. The observed seasonal variability among EVI and NDVI predictors in model contribution needs further investigation. Our findings suggest that images taken in November and April are highly useful for remotely detecting P. juliflora. In general, our intensive sampling and data collection efforts, the species' distinct canopy architecture and its unique spectral signature have allowed us to detect and map P. juliflora trees with acceptable degree of accuracy (Table 3). Our results support the conclusion made by Viña et al. [68] that MODIS vegetation indices can have considerable potential in mapping distributions of species.


Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia.

Wakie TT, Evangelista PH, Jarnevich CS, Laituri M - PLoS ONE (2014)

Long term rainfall pattern in Afar.Average mean monthly precipitation for Melka Werer, Dufti, and Assaita stations (1968–2001). The graph shows a distinct S-N aridity gradient between Melka Werer and Assaita.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231090&req=5

pone-0112854-g004: Long term rainfall pattern in Afar.Average mean monthly precipitation for Melka Werer, Dufti, and Assaita stations (1968–2001). The graph shows a distinct S-N aridity gradient between Melka Werer and Assaita.
Mentions: We found that MODIS Vegetation Indices (VIs) are highly useful for mapping P. juliflora in the extensive land of the Afar. The phenological signals of P. juliflora were best detected by the November EVI and April NDVI MODIS predictors (Table 1). November represents hagay to Afar people, a cold and dry period early in the dry season. During this time, the foliage of most woody shrubs and trees remains green, while herbaceous flora, such as annual grasses and agricultural crops, become less green, creating phenological contrasts for better discrimination of woody vegetation. At the end of the dry season, P. juliflora remains green, while woody shrubs and trees lose most of their foliage or take on a yellow coloration due to water stress (personal observation). In addition, P. juliflora takes advantage of its deep root systems [67] and the moisture from the short rainy season (between March and April and referred by Afar people as hugum) to remain green (Figure 4). These differences were likely detected by the dry season VIs (November, October and December EVIs), and the short rainy season hugum VIs (April and March NDVIs, and March EVI). The trend for NDVI and EVI was similar but EVI values were lower (Figure 2). EVI values are generally lower as they avoid saturation in high biomass areas [29]. In mapping current distributions, we hypothesize that EVI was the top predictor because it was able to detect the dense P. juliflora thickets that often possess high biomass. Wet season NDVI and dry season EVI predictors highly contributed to the model. The observed seasonal variability among EVI and NDVI predictors in model contribution needs further investigation. Our findings suggest that images taken in November and April are highly useful for remotely detecting P. juliflora. In general, our intensive sampling and data collection efforts, the species' distinct canopy architecture and its unique spectral signature have allowed us to detect and map P. juliflora trees with acceptable degree of accuracy (Table 3). Our results support the conclusion made by Viña et al. [68] that MODIS vegetation indices can have considerable potential in mapping distributions of species.

Bottom Line: Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC  = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC  = 0.95).Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora.Our methods can also be replicated for managing invasive species in other East African countries.

View Article: PubMed Central - PubMed

Affiliation: Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado, United States of America; Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado, United States of America.

ABSTRACT
We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m2 spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species- occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC  = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC  = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.

Show MeSH
Related in: MedlinePlus