Sadie J. Ryan

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).

Range and habitat selection of African buffalo in South Africa

Abstract We used more than 10 years of data on buffalo herds in a Geographic Information System (GIS) of Klaserie Private Nature Reserve (KPNR) to examine ranging behavior and habitat selection at multiple temporal and geographic scales. We compared 3 methods of empirical home range estimation: minimum convex polygons (MCP); a fixed-kernel method; and a new local nearest-neighbor convex-hull construction method (LoCoH). For 3 herds over 5 years (1995–2000), the southern herd (SH) had the largest range, the focal study herd (FH) had the intermediate range, and the northern herd (NH) had the smallest range. The LoCoH method best-described the ranges because it accommodated user knowledge of known physical barriers, such as fences, whereas the MCP and kernel methods overestimated ranges. Short-term ranges of the FH over 9 years reveal that buffalo travel farther and range wider in the dry season than the wet. Habitat selection analyses on broad vegetation categories showed preference for Acacia shrub veld and Combretum-dominated woodlands. We found no significant selection of habitat at a fine geographic and temporal interval using the remotely sensed normalized difference vegetation index (NDVI), but the index was correlated to ranging behavior at a larger geographic scale. We found that buffalo selected areas within 1 km of water sources, and an isopleth analysis using the new LoCoH method showed preference for riverine areas in both seasons. This suggests that buffalo preferentially select for areas near water, but they may range farther in the dry season for higher-quality food. As KPNR has a higher density of water than the neighboring Kruger National Park (KNP), this study provides a comparison of buffalo response to water availability in a smaller reserve and important information to managing the buffalo population as part of the larger Greater Kruger Management Area (GKMA).

Consequences of Non-Intervention for Infectious Disease in African Great Apes

Infectious disease has recently joined poaching and habitat loss as a major threat to African apes. Both “naturally” occurring pathogens, such as Ebola and Simian Immunodeficiency Virus (SIV), and respiratory pathogens transmitted from humans, have been confirmed as important sources of mortality in wild gorillas and chimpanzees. While awareness of the threat has increased, interventions such as vaccination and treatment remain controversial. Here we explore both the risk of disease to African apes, and the status of potential responses. Through synthesis of published data, we summarize prior disease impact on African apes. We then use a simple demographic model to illustrate the resilience of a well-known gorilla population to disease, modeled on prior documented outbreaks. We found that the predicted recovery time for this specific gorilla population from a single outbreak ranged from 5 years for a low mortality (4%) respiratory outbreak, to 131 years for an Ebola outbreak that killed 96% of the population. This shows that mortality rates comparable to those recently reported for disease outbreaks in wild populations are not sustainable. This is particularly troubling given the rising pathogen risk created by increasing habituation of wild apes for tourism, and the growth of human populations surrounding protected areas. We assess potential future disease spillover risk in terms of vaccination rates amongst humans that may come into contact with wild apes, and the availability of vaccines against potentially threatening diseases. We discuss and evaluate non-interventionist responses such as limiting tourist access to apes, community health programs, and safety, logistic, and cost issues that constrain the potential of vaccination.

Dengue Vector Dynamics (Aedes aegypti) Influenced by Climate and Social Factors in Ecuador: Implications for Targeted Control

Background Dengue fever, a mosquito-borne viral disease, is now the fastest spreading tropical disease globally. Previous studies indicate that climate and human behavior interact to influence dengue virus and vector (Aedes aegypti) population dynamics; however, the relative effects of these variables depends on local ecology and social context. We investigated the roles of climate and socio-ecological factors on Ae. aegypti population dynamics in Machala, a city in southern coastal Ecuador where dengue is hyper-endemic. Methods/Principal findings We studied two proximate urban localities where we monitored weekly Ae. aegypti oviposition activity (Nov. 2010-June 2011), conducted seasonal pupal surveys, and surveyed household to identify dengue risk factors. The results of this study provide evidence that Ae. aegypti population dynamics are influenced by social risk factors that vary by season and lagged climate variables that vary by locality. Best-fit models to predict the presence of Ae. aegypti pupae included parameters for household water storage practices, access to piped water, the number of households per property, condition of the house and patio, and knowledge and perceptions of dengue. Rainfall and minimum temperature were significant predictors of oviposition activity, although the effect of rainfall varied by locality due to differences in types of water storage containers. Conclusions These results indicate the potential to reduce the burden of dengue in this region by conducting focused vector control interventions that target high-risk households and containers in each season and by developing predictive models using climate and non-climate information. These findings provide the regions public health sector with key information for conducting time and location-specific vector control campaigns, and highlight the importance of local socio-ecological studies to understand dengue dynamics. See Text S1 for an executive summary in Spanish.

Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models

Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18–34°C with maximal transmission occurring in a range from 26–29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.

Patterns and Perceptions of Climate Change in a Biodiversity Conservation Hotspot

Quantifying local peoples perceptions to climate change, and their assessments of which changes matter, is fundamental to addressing the dual challenge of land conservation and poverty alleviation in densely populated tropical regions To develop appropriate policies and responses, it will be important not only to anticipate the nature of expected changes, but also how they are perceived, interpreted and adapted to by local residents. The Albertine Rift region in East Africa is one of the worlds most threatened biodiversity hotspots due to dense smallholder agriculture, high levels of land and resource pressures, and habitat loss and conversion. Results of three separate household surveys conducted in the vicinity of Kibale National Park during the late 2000s indicate that farmers are concerned with variable precipitation. Many survey respondents reported that conditions are drier and rainfall timing is becoming less predictable. Analysis of daily rainfall data for the climate normal period 1981 to 2010 indicates that total rainfall both within and across seasons has not changed significantly, although the timing and transitions of seasons has been highly variable. Results of rainfall data analysis also indicate significant changes in the intra-seasonal rainfall distribution, including longer dry periods within rainy seasons, which may contribute to the perceived decrease in rainfall and can compromise food security. Our results highlight the need for fine-scale climate information to assist agro-ecological communities in developing effective adaptive management.

Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010

BackgroundDengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS.MethodsThe following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran’s I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak.ResultsWe found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak.ConclusionsOur findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk.

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 ; 3t imes 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.

Mapping Physiological Suitability Limits for Malaria in Africa Under Climate Change

Abstract We mapped current and future temperature suitability for malaria transmission in Africa using a published model that incorporates nonlinear physiological responses to temperature of the mosquito vector Anopheles gambiae and the malaria parasite Plasmodium falciparum. We found that a larger area of Africa currently experiences the ideal temperature for transmission than previously supposed. Under future climate projections, we predicted a modest increase in the overall area suitable for malaria transmission, but a net decrease in the most suitable area. Combined with human population density projections, our maps suggest that areas with temperatures suitable for year-round, highest-risk transmission will shift from coastal West Africa to the Albertine Rift between the Democratic Republic of Congo and Uganda, whereas areas with seasonal transmission suitability will shift toward sub-Saharan coastal areas. Mapping temperature suitability places important bounds on malaria transmissibility and, along with local level demographic, socioeconomic, and ecological factors, can indicate where resources may be best spent on malaria control.

Landscapes as continuous entities: forest disturbance and recovery in the Albertine Rift landscape

Kibale National Park, within the Albertine Rift, is known for its rich biodiversity. High human population density and agricultural conversion in the surrounding landscape have created enormous resource pressure on forest fragments outside the park. Kibale presents a complex protected forest landscape comprising intact forest inside the park, logged areas inside the park, a game corridor with degraded forest, and forest fragments in the landscape surrounding the park. To explore the effect of these different levels of forest management and protection over time, we assessed forest change over the previous three decades, using both discrete and continuous data analyses of satellite imagery. Park boundaries have remained fairly intact and forest cover has been maintained or increased inside the park, while there has been a high level of deforestation in the landscape surrounding the park. While absolute changes in land cover are important changes in vegetation productivity, within land cover classes are often more telling of longer term changes and future directions of change. The park has lower Normalized Difference Vegetation Index (NDVI) values than the forest fragments outside the park and the formerly logged area—probably due to forest regeneration and early succession stage. The corridor region has lower productivity, which is surprising given this is also a newer regrowth region and so should be similar to the logged and forest fragments. Overall, concern can be raised for the future trajectory of this park. Although forest cover has been maintained, forest health may be an issue, which for future management, climate change, biodiversity, and increased human pressure may signify troubling signs.