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Melinda Laituri

Publications and source records attributed to Melinda Laituri.

3 recordsLinked to original sources

Vulnerability of subsistence systems due to social and environmental change: A case study in the Yukon-Kuskokwim Delta, Alaska

Arctic Indigenous communities have been classified as highly vulnerable to climate change impacts. The remoteness of Arctic communities, their dependence upon local species and habitats, and the historical marginalization of Indigenous peoples, enhances this characterization of vulnerability. However, vulnerability is a result of diverse historical, social, economic, political, cultural, institutional, natural resource, and environmental conditions and processes and is not easily reduced to a single metric. Furthermore, despite the widespread characterization of vulnerability, Arctic Indigenous communities are extremely resilient as evidenced by subsistence institutions that have been developed over thousands of years. We explored the vulnerability of subsistence systems in the Cup’ik village of Chevak and Yup’ik village of Kotlik through the lens of the strong seasonal dimensions of resource availability. In the context of subsistence harvesting in Alaskan Native Villages, vulnerability may be determined by analyzing the exposure of subsistence resources to climate change impacts, the sensitivity of a community to those impacts, and the capacity of subsistence institutions to absorb these impacts. Subsistence resources, their seasonality, and perceived impacts to these resources were investigated via semi-structured interviews and participatory mapping-calendar workshops. Results suggest that while these communities are experiencing disproportionate impacts of climate change, Indigenous ingenuity and adaptability provide an avenue for culturally appropriate adaptation strategies. However, despite this capacity for resiliency, rapid socio-cultural changes have the potential to be a barrier to community adaptation and the recent, ongoing shifts in seasonal weather patterns, may make seasonally specific subsistence adaptations to landscape particularly vulnerable.

Alaska

Using NDVI to measure precipitation in semi-arid landscapes

Measuring precipitation in semi-arid landscapes is important for understanding the processes related to rainfall and run-off; however, measuring precipitation accurately can often be challenging especially within remote regions where precipitation instruments are scarce. Typically, rain-gauges are sparsely distributed and research comparing rain-gauge and RADAR precipitation estimates reveal that RADAR data are often misleading, especially for monsoon season convective storms. This study investigates an alternative way to map the spatial and temporal variation of precipitation inputs along ephemeral stream channels using Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper imagery. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG), a region covering 3,367 km 2 of semiarid landscapes in southwestern Arizona, USA. The change in NDVI from a pre-to post-monsoon season image along ephemeral stream channels explained 73% of the variance in annual monsoonal precipitation totals from a nearby rain-gauge. In addition, large seasonal changes in NDVI along channels were useful in determining when and where flow events have occurred.

Arizona

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 km 2 in the Afar region (AUC = 0.94), while the potential habitat for future infestations is 5,024 km 2 (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.

PLoS ONE