Nicholas E. Young

How will climate change spatially affect agriculture production in Ethiopia? Case studies of important cereal crops

Nearly all of Ethiopia’s agriculture is dependent on rainfall, particularly the amount and seasonal occurrence. Future climate change predictions agree that changes in rainfall, temperature, and seasonality will impact Ethiopia with dramatic consequences. When, where, and how these changes will transpire has not been adequately addressed. The objective of our study was to model how projected climate change scenarios will spatially and temporally impact cereal production, a dietary staple for millions of Ethiopians. We used Maxent software fit with crop data collected from household surveys and bioclimatic variables from the WorldClim database to develop spatially explicit models of crop production in Ethiopia. Our results were extrapolated to three climate change projections (i.e., Canadian Centre for Climate Modeling and Analysis, Hadley Centre Coupled Model v3, and Commonwealth Scientific and Industrial Research Organization), each having two emission scenarios. Model evaluations indicated that our results had strong predictability for all four cereal crops with area under the curve values of 0.79, 0.81, 0.79, and 0.83 for teff, maize, sorghum, and barley, respectively. As expected, bioclimatic variables related to rainfall were the greatest predictors for all four cereal crops. All models showed similar decreasing spatial trends in cereal production. In addition, there were geographic shifts in land suitability which need to be accounted for when assessing overall vulnerability to climate change. The ability to adapt to climate change will be critical for Ethiopia’s agricultural system and food security. Spatially explicit models will be vital for developing early warning systems, adaptive strategies, and policy to minimize the negative impacts of climate change on food production.

A survival guide to Landsat preprocessing

Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson’s correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.

Demographic Observations of Mountain Nyala Tragelaphus Buxtoni in a Controlled Hunting Area, Ethiopia

The highlands of Ethiopia are inhabited by the culturally and economically significant mountain nyala Tragelaphus buxtoni, an endemic spiral horned antelope. The natural range of this species has become highly fragmented with increasing anthropogenic pressures; driving land conversion in areas previously considered critical mountain nyala habitat. Therefore, baseline demographic data on this species throughout its existing range are needed. Previous studies on mountain nyala demographics have primarily focused on a confined portion of its known range where trophy hunting is not practiced. Our objectives were to estimate group size, proportion of females, age class proportions, and calf and juvenile productivity for a sub-population of mountain nyala where trophy hunting is permitted and compare our results to recent and historical observations. We collected four years of demographic data using direct point counts in a controlled hunting area and summarized the data using the R statistical software. Our results show that estimated proportion of females (0.63; 0.56-0.69) was similar to recent studies of non-hunted populations, but group size (3.74; 3.34-4.13), juvenile productivity (0.47; 0.35-0.62) and age class proportions (calves: 0.17 juveniles: 0.19 adults: 0.64) were considerably different. Our results are more similar to historical accounts than those in a national park. We demonstrate that the mountain nyalas population structure and health varies across its range and may relate to the different management strategies and policies. We recommend using similar methods for remaining under surveyed sub-populations of mountain nyala to inform conservation actions at the landscape scale.