Validation of Satellite Rainfall Products for Western Uganda.

Central equatorial Africa is deficient in long-term, ground-based measurements of rainfall; therefore, the aim of this study is to assess the accuracy of three high-resolution, satellite-based rainfall products in western Uganda for the 2001–10 period. The three products are African Rainfall Climatology, version 2 (ARC2); African Rainfall Estimation Algorithm, version 2 (RFE2); and 3B42 from the Tropical Rainfall Measuring Mission, version 7 (i.e., 3B42v7). Daily rainfall totals from six gauges were used to assess the accuracy of satellite-based rainfall estimates of rainfall days, daily rainfall totals, 10-day rainfall totals, monthly rainfall totals, and seasonal rainfall totals. The northern stations had a mean annual rainfall total of 1390 mm, while the southern stations had a mean annual rainfall total of 900 mm. 3B42v7 was the only product that did not underestimate boreal-summer rainfall at the northern stations, which had ~3 times as much rainfall during boreal summer than did the southern stations. The three products tended to overestimate rainfall days at all stations and were borderline satisfactory at identifying rainfall days at the northern stations; the products did not perform satisfactorily at the southern stations. At the northern stations, 3B42v7 performed satisfactorily at estimating monthly and seasonal rainfall totals, ARC2 was only satisfactory at estimating seasonal rainfall totals, and RFE2 did not perform satisfactorily at any time step. The satellite products performed worst at the two stations located in rain shadows, and 3B42v7 had substantial overestimates at those stations

Trends and Variability in Localized Precipitation Around Kibale National Park, Uganda, Africa

Our objective was to understand and describe local spatial and temporal variability in precipitation around Kibale National Park, a tropical forest area of high conservation value. Continental or regional-scale trends are often relied upon to make policy and management decisions, but these analyses are often at too coarse a resolution to capture important variability at a finer scale where management actions operate. Monthly rainfall data derived from ten long-term station records (1941-1975) were used to evaluate local spatiotemporal variability in seasonal and annual rainfall for the area surrounding Kibale National Park. The magnitude, direction and significance of trends in seasonal and annual rainfall within the area surrounding the park were identified using the Mann-Kendall trend test and Sen’s slope estimator. The standardized precipitation index was calculated at 3- and 12-month periods to identify areas of relative wetness or dryness. Analysis of annual trends and precipitation indices indicated that patterns in annual time series do not reflect the direction and magnitude of seasonal trends nor the spatial variability in intra-annual rainfall at the local scale. Significant negative trends in the seasonal long rains, following dry season and short rains were identified at stations west of Kibale, while significant positive trends in the seasonal short rains occurred at stations north of the park. Stations along the western park boundary tended to have more years in which the two dry seasons were abnormally dry than those stations located further from the park

Population pressure and global markets drive a decade of forest cover change in Africa\u27s Albertine Rift

Africa\u27s Albertine Rift region faces a juxtaposition of rapid human population growth and protected areas, making it one of the world\u27s most vulnerable biodiversity hotspots. Using satellite-derived estimates of forest cover change, we examined national socioeconomic, demographic, agricultural production, and local demographic and geographic variables, to assess multilevel forces driving local forest cover loss and gain outside protected areas during the first decade of this century. Because the processes that drive forest cover loss and gain are expected to be different, and both are of interest, we constructed models of significant change in each direction. Although rates of forest cover change varied by country, national population change was the strongest driver of forest loss for all countries – with a population doubling predicted to cause 2.06% annual cover loss, while doubling tea production predicted to cause 1.90%. The rate of forest cover gain was associated positively with increased production of the local staple crop cassava, but negatively with local population density and meat production, suggesting production drivers at multiple levels affect reforestation. We found a small but significant decrease in loss rate as distance from protected areas increased, supporting studies suggesting higher rates of landscape change near protected areas. While local population density mitigated the rate of forest cover gain, loss was also correlated with lower local population density, an apparent paradox, but consistent with findings that larger scale forces outweigh local drivers of deforestation. This implicates demographic and market forces at national and international scales as critical drivers of change, calling into question the necessary scales of forest protection policy in this biodiversity hotspot. Using a satellite derived estimate of forest cover change for both loss and gain added a dynamic component to more traditionally static and unidirectional studies, significantly improving our understanding of landscape processes and drivers at work

LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions.

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: "fixed sphere-of-influence," or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an "adaptive sphere-of-influence," or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original "fixed-number-of-points," or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu)

Scoping Review of Distribution Models for Selected \u3ci\u3eAmblyomma\u3c/i\u3e Ticks and Rickettsial Group Pathogens

The rising prevalence of tick-borne diseases in humans in recent decades has called attention to the need for more information on geographic risk for public health planning. Species distribution models (SDMs) are an increasingly utilized method of constructing potential geographic ranges. There are many knowledge gaps in our understanding of risk of exposure to tick-borne pathogens, particularly for those in the rickettsial group. Here, we conducted a systematic scoping review of the SDM literature for rickettsial pathogens and tick vectors in the genus Amblyomma. Of the 174 reviewed articles, only 24 studies used SDMs to estimate the potential extent of vector and/or pathogen ranges. The majority of studies (79%) estimated only tick distributions using vector presence as a proxy for pathogen exposure. Studies were conducted at different scales and across multiple continents. Few studies undertook original data collection, and SDMs were mostly built with presence-only datasets from public database or surveillance sources. The reliance on existing data sources, using ticks as a proxy for disease risk, may simply reflect a lag in new data acquisition and a thorough understanding of the tick-pathogen ecology involved

Newer Surveillance Data Extends Our Understanding of the Niche of \u3ci\u3eRickettsia montanensis\u3c/i\u3e (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae) in the United States

Background: Understanding the geographic distribution of Rickettsia montanensis infections in Dermacentor variabilis is important for tick-borne disease management in the United States, as both a tick-borne agent of interest and a potential confounder in surveillance of other rickettsial diseases. Two previous studies modeled niche suitability for D. variabilis with and without R. montanensis, from 2002-2012, indicating that the D. variabilis niche overestimates the infected niche. This study updates these, adding data since 2012. Methods: Newer surveillance and testing data were used to update Species Distribution Models (SDMs) of D. variabilis, and R. montanensis infected D. variabilis, in the United States. Using random forest (RF) models, found to perform best in previous work, we updated the SDMs and compared them with prior results. Warren’s I niche overlap metric was used to compare between predicted suitability for all ticks and ‘pathogen positive niche’ models across datasets. Results: Warren’s I indicated \u3c 2% change in predicted niche, and there was no change in order of importance of environmental predictors, for D. variabilis or R. montanensis positive niche. The updated D. variabilis niche model overpredicted suitability compared to the updated R. montanensis positive niche in key peripheral parts of the range, but slightly underpredicted through the northern and midwestern parts of the range. This reinforces previous findings of a more constrained pathogen-positive niche than predicted by D. variabilis records alone. Conclusions: The consistency of predicted niche suitability for D. variabilis in the United States, with the addition of nearly a decade of new data, corroborates this is a species with generalist habitat requirements. Yet a slight shift in updated niche distribution, even of low suitability, included more southern areas, pointing to a need for continued and extended monitoring and surveillance. This further underscores the importance of revisiting vector and vector-borne disease distribution maps

Changing measurements or changing movements? Sampling scale and movement model identifiability across generations of biologging technology

Animal movement patterns contribute to our understanding of variation in breeding success and survival of individuals, and the implications for population dynamics. Over time, sensor technology for measuring movement patterns has improved. Although older technologies may be rendered obsolete, the existing data are still valuable, especially if new and old data can be compared to test whether a behavior has changed over time. We used simulated data to assess the ability to quantify and correctly identify patterns of seabird flight lengths under observational regimes used in successive generations of wet/dry logging technology. Care must be taken when comparing data collected at differing timescales, even when using inference procedures that incorporate the observational process, as model selection and parameter estimation may be biased. In practice, comparisons may only be valid when degrading all data to match the lowest resolution in a set. Changes in tracking technology, such as the wet/dry loggers explored here, that lead to aggregation of measurements at different temporal scales make comparisons challenging. We therefore urge ecologists to use synthetic data to assess whether accurate parameter estimation is possible for models comparing disparate data sets before planning experiments and conducting analyses such as responses to environmental changes or the assessment of management actions

Estimating the Distribution of \u3ci\u3eOryzomys palustris\u3c/i\u3e, A Potential Key Host in Expanding Rickettsial Tick-Borne Disease Risk

Increasingly, geographic approaches to assessing the risk of tick-borne diseases are being used to inform public health decision-making and surveillance efforts. The distributions of key tick species of medical importance are often modeled as a function of environmental factors, using niche modeling approaches to capture habitat suitability. However, this is often disconnected from the potential distribution of key host species, which may play an important role in the actual transmission cycle and risk potential in expanding tick-borne disease risk. Using species distribution modeling, we explore the potential geographic range of Oryzomys palustris, the marsh rice rat, which has been implicated as a potential reservoir host of Rickettsia parkeri, a pathogen transmitted by the Gulf Coast tick (Amblyomma maculatum) in the southeastern United States. Due to recent taxonomic reclassification of O. palustris subspecies, we reclassified geolocated collections records into the newer clade definitions. We modeled the distribution of the two updated clades in the region, establishing for the first time, range maps and distributions of these two clades. The predicted distribution of both clades indicates a largely Gulf and southeastern coastal distribution. Estimated suitable habitat for O. palustris extends into the southern portion of the Mid-Atlantic region, with a discontinuous, limited area of suitability in coastal California. Broader distribution predictions suggest potential incursions along the Mississippi River. We found considerable overlap of predicted O. palustris ranges with the distribution of A. maculatum, indicating the potential need for extended surveillance efforts in those overlapping areas and attention to the role of hosts in transmission cycles