Distribution and economic analysis of Prosopis juliflora in Ethiopia

Includes bibliographical references.2015 Fall.Invasive species are one of the drivers of biological and socio-economic changes around the world. Over the past 30-40 years, the non-native Prosopis juliflora plant has emerged as a major invader of the arid and semi-arid regions of Ethiopia. Information on its distribution, impact, use and management is highly needed to contain and prevent the spread of this highly invasive plant. In the first study, I used a correlative modeling framework to track and map the current and potential distribution of P. juliflora in Afar, north-eastern Ethiopia. Specifically, I used time-series of Moderate Resolution Imaging Specrtoradiometer (MODIS) satellite imagery, 143 species-occurrence records and the Maxent modeling technique to map its current distribution. I then used topo-climatic predictors, species-occurrence records and the Maxent software to map its forecasted distribution. I found that the current extent of P. juliflora invasion in the Afar region is approximately 3,605 Km2, while its predicted distribution is approximately 5,024 Km2. My findings demonstrates that 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 its potential habitat in Ethiopia. In the second study, I used a participatory research framework to map P. juliflora over a fine geographic scale, and to identify the major resource related problems in the region. I learned about the introduction history, spread, impacts, uses and traditional management practices of P. juliflora in Afar by interviewing 108 pastoralists and agro-pastoralists. Additionally, I detected the land-cover categories most affected by P. juliflora invasion by superimposing community produced maps on ancillary land-cover layers, and performing overlay analysis. Prosopis juliflora has highly invaded grasslands and open areas in Afar. The species displaces useful native grass and forage species, which are important for sustaining the region's wildlife and livestock resources. In addition to threats from invasive species, Afar people face conflicts from neighboring Issa ethnic groups, and land-grabs from the central government and foreign investors. The findings demonstrates that participatory mapping methods are suitable for mapping species distribution, detecting land-cover changes, and managing invasive plants. High invasive species control costs have swayed most developing countries to adopt cost effective P. juliflora eradication and utilization practices. However, the effectiveness and economic viability of these new approaches have not been thoroughly tested. In the third study, I used an economic analysis framework to assess the economic feasibility of selected P. juliflora eradication and utilization methods that are practiced in southern Afar. The dominant P. juliflora eradication option was to convert infested lands into irrigated farms, while the preferred utilization options were to make animal fodder from P. juliflora seed pods, and to produce charcoal from P. juliflora wood. I interviewed 19 enterprise owners (i.e., farmers, flour producers and charcoal makers) and collected primary data on prices, yields, costs and revenues. I assessed the economic feasibility of the selected methods by performing enterprise, profitability, sensitivity and risk analyses over 10 years and an interest rate of 10% per year. Converting P. juliflora infested lands into irrigated agriculture is a profitable and risky P. juliflora eradication approach. Charcoal making is a moderately profitable and less risky utilization approach, while flour production is a risky and an un-profitable utilization approach. Introducing new changes in the production and management steps of flour production may be needed to make flour enterprises profitable. My overall economic analysis suggests that control through utilization may be one of the effective and economically viable P. juliflora management strategies currently accessible to Ethiopia. I generated reliable information on the distribution and impacts of P. juliflora in Afar by employing a wide variety of scientific approaches. My results can guide local level P. juliflora utilization and control efforts in Afar, while my methodologies can be replicated for managing invasive plants in other developing countries

The Establishment Risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and Globally

Native to Asia, the spotted lanternfly, Lycorma delicatula (White), is an emerging pest of many commercially important plants in Korea, Japan, and the United States. Determining its potential distribution is important for proactive measures to protect commercially important commodities. The objective of this study was to assess the establishment risk of L. delicatula globally and in the United States using the ecological niche model MAXENT, with a focus on Washington State (WA), where large fruit industries exist. The MAXENT model predicted highly suitable areas for L. delicatula in Asia, Oceania, South America, North America, Africa, and Europe, but also predicted that tropical habitats are not suitable for its establishment, contrary to published information. Within the United States, the MAXENT model predicted that L. delicatula can establish in most of New England and the mid-Atlantic states, the central United States and the Pacific Coast states, including WA. If introduced, L. delicatula is likely to establish in fruit-growing regions of the Pacific Northwest. The most important environmental variables for predicting the potential distribution of L. delicatula were mean temperature of driest quarter, elevation, degree-days with a lower developmental threshold value of 11°C, isothermality, and precipitation of coldest quarter. Results of this study can be used by regulatory agencies to guide L. delicatula surveys and prioritize management interventions for this pest

The Establishment Risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and Globally

Native to Asia, the spotted lanternfly, Lycorma delicatula (White), is an emerging pest of many commercially important plants in Korea, Japan, and the United States. Determining its potential distribution is important for proactive measures to protect commercially important commodities. The objective of this study was to assess the establishment risk of L. delicatula globally and in the United States using the ecological niche model MAXENT, with a focus on Washington State (WA), where large fruit industries exist. The MAXENT model predicted highly suitable areas for L. delicatula in Asia, Oceania, South America, North America, Africa, and Europe, but also predicted that tropical habitats are not suitable for its establishment, contrary to published information. Within the United States, the MAXENT model predicted that L. delicatula can establish in most of New England and the mid-Atlantic states, the central United States and the Pacific Coast states, including WA. If introduced, L. delicatula is likely to establish in fruit-growing regions of the Pacific Northwest. The most important environmental variables for predicting the potential distribution of L. delicatula were mean temperature of driest quarter, elevation, degree-days with a lower developmental threshold value of 11°C, isothermality, and precipitation of coldest quarter. Results of this study can be used by regulatory agencies to guide L. delicatula surveys and prioritize management interventions for this pest

Modeling the abundance of two Rhagoletis fly (Diptera: Tephritidae) pests in Washington State, U.S.A.

Well-adapted and abundant insect pests can negatively affect agricultural production. We modeled the abundance of two Rhagoletis fly (Diptera: Tephritidae) pests, apple maggot fly, Rhagoletis pomonella (Walsh), and western cherry fruit fly, Rhagoletis indifferens Curran, in Washington State (WA), U.S.A. using biologically relevant environmental variables. We tested the hypothesis that abundance of the two species is influenced by different environmental variables, based on the fact that these two species evolved in different environments, have different host plants, and that R. pomonella is an introduced pest in WA while R. indifferens is native. We collected data on fly and host plant abundance at 61 randomly selected sites across WA in 2015 and 2016. We obtained land-cover, climate, and elevation data from online sources and used these data to derive relevant landscape variables and modeled fly abundance using generalized linear models. For R. pomonella, relatively high winter mean minimum temperature, low elevation, and developed land-cover were the top variables positively related to fly abundance. In contrast, for R. indifferens, the top variables related to greater fly abundance were high Hargreaves climatic moisture and annual heat-moisture deficits (indication of drier habitats), high host plant abundance, and developed land-cover. Our results identify key environmental variables driving Rhagoletis fly abundance in WA and can be used for understanding adaptation of insects to non-native and native habitats and for assisting fly quarantine and management decisions

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

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

Percent contribution and permutation importance of remote sensing predictors.

<p>Maxent model was set to 30% random test percentage and <i>sub-sample</i> replication type.</p><p>Percent contribution and permutation importance of remote sensing predictors.</p

AUC and Maximized Kappa Statistic values calculated for an independent data set for both the current and the potential distribution models.

<p>AUC and Maximized Kappa Statistic values calculated for an independent data set for both the current and the potential distribution models.</p

Study Site.

<p>Zones are administrative units that are found within <i>Killils</i> (regions or states) and can have several <i>Woredas</i> (counties). The five zones are referred as Awsi Rasu (Zone 1), Kilbet Rasu (Zone 2), Gabi Rasu (Zone 3), Fantena Rasu (Zone 4) and Hari Rasu (Zone 5).</p

Distribution of <i>P. juliflora</i>.

<p>The current distribution (shown in green) is superimposed on the potential distribution (shown in yellow). The 143 <i>P. juliflora</i> occurrence records used in the model are shown in red. The Multivariate Environmental Similarity Surfaces (MESS) results that indicate areas that are environmentally dissimilar to the training data are shown in light green color.</p

Long term rainfall pattern in Afar.

<p>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.</p