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To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the sdm package operated in RStudio software, we constructed habitat models over a 9-month period, from March to November 2004, using 17 algorithms, with a 70:30 split of training and test data, with bootstrapping and 10 runs as parameter settings for our models. Model performance evaluation was conducted using the area under the curve (AUC) of the receiver operating characteristic (ROC), the point biserial correlation coefficient (COR), the true skill statistic (TSS) and Cohen’s kappa (k) metrics. We analyzed the response curves for each predictor variable per algorithm, the variable importance information and the ROC plots. Ensemble predictions of habitats were weighted with the TSS metric. Model performance varied across various algorithms, with the Support Vector Machines (SVM), Boosted Regression Trees (BRT), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (GAM), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), Recursive Partitioning and Regression Trees (RPART), and Maximum Entropy (MAXENT), showing consistently high performance than other algorithms, while the Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Bioclim (BIOC), Domain (DOM), Maxlike (MAXL), Mahalanobis Distance (MAHA) and Radial Basis Function (RBF) had lower performance. We found inter-algorithm variations in predictor variable responses. We conclude that the multi-algorithm modelling approach enabled us to assess the variability in algorithm performance, hence a data driven basis for building the ensemble model. Given the inter-algorithm variations observed, the ensemble prediction maps indicated a better habitat utilization map of skipjack tuna than would have been achieved by a single algorithm.

The Lake Victoria Basin (LVB) is a significant resource for five states within East Africa, which faces major land use land cover changes that threaten ecosystem integrity and ecosystem services derived from the basin’s resources. To assess land use land cover changes between 1985 and 2014, and subsequently determine the trends and drivers of these changes, we used a series of Landsat images and field data obtained from the LVB. Landsat image pre-processing and band combinations were done in ENVI 5.1. A supervised classification was applied on 118 Landsat scenes using the maximum likelihood classifier in ENVI 5.1. The overall accuracy of classified images was computed for the 2014 images using 124 reference data points collected through stratified random sampling. Computations of area under various land cover classes were calculated between the 1985 and 2014 images. We also correlated the area from natural vegetation classes to farmlands and settlements (urban areas) to explore relationships between land use land cover conversions among these classes. Based on our land cover classifications, we obtained overall accuracy of 71% and a moderate Kappa statistic of 0.56. Our results indicate that the LVB has undergone drastic changes in land use land cover, mainly driven by human activities that led to the conversion of forests, woodlands, grasslands, and wetlands to either farmlands or settlements. We conclude that information from this work is useful not only for basin-scale assessments and monitoring of land cover changes but also for targeting, prioritizing, and monitoring of small scale, community led efforts to restore degraded and fragmented areas in the basin. Such efforts could mitigate the loss of ecosystem services previously derived from large contiguous land covers which are no longer tenable to restore. We recommend adoption of a basin scale, operational, Earth observation-based, land use change monitoring framework. Such a framework can facilitate rapid and frequent assessments of gains and losses in specific land cover classes and thus focus strategic interventions in areas experiencing major losses, through mitigation and compensatory approaches.

Skipjack tuna (SJT) pelagic hotspots in the western North Pacific (WNP) were modelled using fishery and satellite remotely sensed data with Ecological Niche Factor Analysis (ENFA) models. Our objectives were to model and predict habitat hotspots for SJT and assess the monthly changes in sub-surface temperatures and mixed layer depths at fishing locations. SJT presence-only monthly resolved data, sea surface temperature, chlorophyll-a, diffuse attenuation coefficient, sea surface heights and surface wind speed were used to construct ENFA models and generate habitat suitability indices using a short-term dataset from March-November 2004. The suitability indices were then predicted for July-October (2007 and 2008). Monthly aggregated polygons of areas fished by skipjack tuna pole and line vessels were also overlaid on the predicted habitat suitability maps. Distributions of sub-surface temperatures and mixed layer depths (MLD) at fishing locations were also examined. Our results showed good fit for ENFA models, as indicated by the absolute validation index, the contrast validation index and the continuous Boyce index. The predicted hotspots showed varying concurrences when compared with 25-degree polygons derived from fished areas. Northward shifts in SJT hotspots corresponded with declining MLDs from March to September. The MLDs were shallower in summer and deeper in autumn and winter months. The habitat hotspots modeled using ENFA were consistent with the known ecology and seasonal migration pattern of SJT. The findings of this work, derived from a short-term dataset, enable identification of SJT hotspots in the WNP, thus contributing valuable information for future research on SJT habitat prediction models.

Increasing climate variability and change coupled with steady population growth is threatening water resources and livelihoods of communities living in the Wami-Ruvu and Rufiji basins in Tanzania. These basins are host to three large urban centers, namely Dar es Salaam, Dodoma and Morogoro, with a combined total of more than 7 million people. Increased demand for ecosystem services from the available surface water resources and a decreasing supply of clean and safe water are exacerbating the vulnerability of communities in these basins. Several studies have analyzed climate projects in the two basins but little attention has been paid to identify locations that have vulnerable communities in a spatially-explicit form. To address this gap, we worked with stakeholders from national and local government agencies, basin water boards and the Water Resources Integration Development Initiative (WARIDI) project funded by USAID to map the vulnerability of communities to climate variability and change in the two basins. A generalized methodology for mapping social vulnerability to climate change was used to integrate biophysical and socioeconomic indicators of exposure, sensitivity and adaptive capacity and produced climate vulnerability index maps. Our analysis identified vulnerability “hotspots” where communities are at a greater risk from climate stressors. The results from this study were used to identify priority sites and adaptation measures for the implementation of resilience building interventions and to train local government agencies and communities on climate change adaptation measures in the two basins.

We identified the pelagic habitat hotspots of the neon flying squid (Ommastrephes bartramii) in the central North Pacific from May to July and characterized the spatial patterns of squid aggregations in relation to oceanographic features such as mesoscale oceanic eddies and the Transition Zone Chlorophyll-a Front (TZCF). The data used for the habitat model construction and analyses were squid fishery information, remotely-sensed and numerical model-derived environmental data from May to July 1999-2010. Squid habitat hotspots were deduced from the monthly Maximum Entropy (MaxEnt) models and were identified as regions of persistent high suitable habitat across the 12-year period. The distribution of predicted squid habitat hotspots in central North Pacific revealed interesting spatial and temporal patterns likely linked with the presence and dynamics of oceanographic features in squid's putative foraging grounds from late spring to summer. From May to June, the inferred patches of squid habitat hotspots developed within the Kuroshio-Oyashio transition zone (KOTZ; 37-40°N) and further expanded north towards the subarctic frontal zone (SAFZ; 40-44°N) in July. The squid habitat hotspots within the KOTZ and areas west of the dateline (160°W-180°) were likely influenced and associated with the highly dynamic and transient oceanic eddies and could possibly account for lower squid suitable habitat persistence obtained from these regions. However, predicted squid habitat hotspots located in regions east of the dateline (180°-160°W) from June to July, showed predominantly higher squid habitat persistence presumably due to their proximity to the mean position of the seasonally-shifting TZCF and consequent utilization of the highly productive waters of the SAFZ.

We have more data about wildlife trafficking than ever before, but it remains underutilized for decision-making. Central to effective wildlife trafficking interventions is collection, aggregation, and analysis of data across a range of source, transit, and destination geographies. Many data are geospatial, but these data cannot be effectively accessed or aggregated without appropriate geospatial data standards. Our goal was to create geospatial data standards to help advance efforts to combat wildlife trafficking. We achieved our goal using voluntary, participatory, and engagement-based workshops with diverse and multisectoral stakeholders, online portals, and electronic communication with more than 100 participants on three continents. The standards support data-to-decision efforts in the field, for example indictments of key figures within wildlife trafficking, and disruption of their networks. Geospatial data standards help enable broader utilization of wildlife trafficking data across disciplines and sectors, accelerate aggregation and analysis of data across space and time, advance evidence-based decision making, and reduce wildlife trafficking.

The Kenyan rangelands contribute significantly to the country’s GDP through livestock production and tourism. With dependence on rain-fed pastures, climate variability coupled with human induced factors such as overgrazing have adversely affected the rangeland ecosystems. And while indigenous communities and conservation experts already use their knowledge of the landscape to make decisions, this information is usually localized. Earth observation imagery provides a bigger picture that can complement indigenous knowledge and improve decision making. This research leverages on data from the MODIS receiver located at the Regional Centre for Mapping of Resources for Development(RCMRD) to develop the indices for the web-based Rangelands Decision Support Tool(RDST) . The tool automates data processing and provides an easy to use interface for accessing indices for rangeland monitoring. MODIS Normalized Difference Vegetation Index (NDVI), anomalies and deviation indices are provided on the tool at decadal, monthly and seasonal time steps .Users begin their assessments by selecting their monitoring units and an NDVI index that responds to their specific questions. These questions respond to assessing current conditions, monitoring trends and changes in vegetation, and evaluating proxies for drought conditions . The information can then be overlaid with other ancillary data sets (roads, water sources, invasive species, protected areas, conflict areas), for context. At the click of a button, the information can be downloaded as a map for further analysis or application in sub regional decision making. Information generated by the tool are being used decision making tool by rangeland managers and county officers. Specifically, to inform adjustments to existing grazing plans, managing movement of livestock from designated grazing areas in wet and dry season, monitoring the success of rehabilitation efforts and resilience of the rangeland ecosystems, monitoring drought, managing scarce water resources and monitoring the spread of invasive species. Successful implementation and application for decision making has relied heavily on local indigenous knowledge and capacity building on use of the earth observation indices. The SERVIR project service planning engagement approach was used in engagements with stakeholders. This improved their participation in co-development of the tool and indices; and in adoption of the tools for decision making.

Kenya is composed of diverse geographic regions and is impacted by climatic variability. This diversity in conditions has led to a diverse number of plants and animals. Invasive species however, threatens this biodiversity. This study mapped the current distribution of A. reficiens and Opuntia species using occurrence data, then applied the model to identify where suitable environments the species are likely to occur under current and future climatic conditions under Representative Climate Pathways (RCPs) 2.6 and 8.5. The current distribution of the two invasive plant species was sampled in the field using an android based application and a GPS (Global Positioning System) device. Distal variables including: Elevation, human settlements, distance to rivers and vegetation indices (Monthly Normalized Difference Vegetation Indices (NDVI) and, Enhanced Vegetation Indices (EVI) derived from MODIS products 1km spatial resolution) were used as predictors. A mean of 25 replicates was used in identifying suitable niches. The model performance was evaluated using the average test AUC, average testing omission rate metrics and average regularized training gain. The predictive models for both species performed better than random prediction (P < 0.05). Average test AUC values (0.96 and 0.97 A.reficiens and Opuntia species respectively) and their associated 95% confidence intervals showed the fitted models had high discriminative ability to differentiate suitable environments for invasive plant species from random background points. The average test AUC values for A. reficiens (0.97 ± 0.01) and Opuntia species (0.96 ± 0.02) were high. Both models yielded moderate model gain values of 2.4 and 2.7 respectively. The model predictions show distribution of both A.reficiens and Opuntia species are likely to extend under future climatic scenarios; with current extents estimated at 291,900 ha and 28,700 ha respectively. Data on mapping, monitoring and assessment of the invasive species can provide governments with insight into how the poor and vulnerable are affected by the loss and degradation of biodiversity and ecosystems due to the spread of such species. This will directly or indirectly help in a achieving the Sustainable Development Goals (SDG) of the UN’s Agenda for SDG 15: Protect, restore and promote sustainable use of terrestrial ecosystems.

Skipjack tuna (SJT) pelagic hotspots in the western North Pacific (WNP) were modelled using fishery and satellite remotely sensed data with Ecological Niche Factor Analysis (ENFA) models. Our objectives were to model and predict habitat hotspots for SJT and assess the monthly changes in sub-surface temperatures and mixed layer depths at fishing locations. SJT presence-only monthly resolved data, sea surface temperature, chlorophyll-a, diffuse attenuation coefficient, sea surface heights and surface wind speed were used to construct ENFA models and generate habitat suitability indices using a short-term dataset from March-November 2004. The suitability indices were then predicted for July-October (2007 and 2008). Monthly aggregated polygons of areas fished by skipjack tuna pole and line vessels were also overlaid on the predicted habitat suitability maps. Distributions of sub-surface temperatures and mixed layer depths (MLD) at fishing locations were also examined. Our results showed good fit for ENFA models, as indicated by the absolute validation index, the contrast validation index and the continuous Boyce index. The predicted hotspots showed varying concurrences when compared with 25-degree polygons derived from fished areas. Northward shifts in SJT hotspots corresponded with declining MLDs from March to September. The MLDs were shallower in summer and deeper in autumn and winter months. The habitat hotspots modeled using ENFA were consistent with the known ecology and seasonal migration pattern of SJT. The findings of this work, derived from a short-term dataset, enable identification of SJT hotspots in the WNP, thus contributing valuable information for future research on SJT habitat prediction models.

Capacity building using Earth observing (EO) systems and data (i.e., from orbital and nonorbital platforms) to enable societal applications includes the network of human, nonhuman, technical, nontechnical, hardware, and software dimensions that are necessary to successfully cross the valley [of death; see NRC (2001)] between science and research (port of departure) and societal application (port of arrival). In many parts of the world (especially where ground-based measurements are scarce or insufficient), applications of EO data still struggle for longevity or continuity for a variety of reasons, foremost among them being the lack of resilient capacity. An organization is said to have resilient capacity when it can retain and continue to build capacity in the face of unexpected shocks or stresses. Stresses can include intermittent power and limited Internet bandwidth, constant need for education on ever-increasing complexity of EO systems and data, communication challenges between the ports of departure and arrival (especially across time zones), and financial limitations and instability. Shocks may also include extreme events such as disasters and losing key staff with technical and institutional knowledge.