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

Drilling through Conservation Policy: Oil Exploration in Murchison Falls Protected Area, Uganda

Approximately 2.5 billion barrels of commercially-viable oil, worth $2 billion in annual revenue for 20 years, were discovered under the Ugandan portion of the Albertine Rift in 2006. The region also contains seven of Uganda's protected areas and a growing ecotourism industry. We conducted interviews and focus groups in and around Murchison Falls Protected Area, Uganda's largest, oldest, and most visited protected area, to assess the interaction of oil exploration with the three primary conservation policies employed by Uganda Wildlife Authority: protectionism, neoliberal capital accumulation, and community-based conservation. We find that oil extraction is legally permitted inside protected areas in Uganda, like many other African countries, and that the wildlife authority and oil companies are adapting to co-exist inside a protected area. Our primary argument is that neoliberal capital accumulation as a conservation policy actually makes protected areas more vulnerable to industrial exploitation because nature is commodified, allowing economic value and profitability of land uses to determine how nature is exploited. Our secondary argument is that the conditional nature of protected area access inherent within the protectionist policy permits oil extraction within Murchison Falls Protected Area. Finally, we argue that community-based conservation, as operationalized in Uganda, has no role in defending protected areas against oil industrialisation

Understanding Long-Term Savanna Vegetation Persistence across Three Drainage Basins in Southern Africa

Across savanna landscapes of southern Africa, people are strongly tied to the environment, meaning alterations to the landscape would impact livelihoods and socioecological development. Given the human–environment connection, it is essential to further our understanding of the drivers of savanna vegetation dynamics, and under increasing climate variability, to better understand the vegetation–climate relationship. Monthly time series of Advanced Very High-Resolution Radiometer (AVHRR)- and Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation indices, available from as early as the 1980s, holds promise for the large-scale quantification of complex vegetation–climate dynamics and regional analyses of landscape change as related to global environmental changes. In this work, we employ time series based analyses to examine landscape-level vegetation greening patterns over time and across a significant precipitation gradient. In this study, we show that climate induced reductions in Normalized Difference Vegetation Index (NDVI; i.e., degradation or biomass decline) have had large spatial and temporal impacts across the Kwando, Okavango, and Zambezi catchments of southern Africa. We conclude that over time there have been alterations in the available soil moisture resulting from increases in temperature in every season. Such changes in the ecosystem dynamics of all three basins has led to system-wide changes in landscape greening patterns

Determinants of home range size of imperiled New England and introduced eastern cottontails

In fragmented habitat, population persistence depends in part on patch quality and patch size relative to home range size. The imperiled New England cottontail (Sylvilagus transitionalis (Bangs, 1895)) is an obligate user of shrublands in the northeastern United States, a highly fragmented and declining ecosystem. New England cottontail conservation efforts have targeted habitat creation; however, efforts are hindered by a limited knowledge of seasonal space use and its relationship to habitat quality, which could help inform minimum patch size requirements and implications of competition with non-native eastern cottontails (Sylvilagus floridanus (J. A. Allen, 1890)). To address these uncertainties, we modeled home range areas for both species as a function of season, patch size, sex, and two indicators of forage and cover availability. Home range was generally inversely correlated with measures of forage and cover resources and the response differed by season and species and did not vary with patch size. Instead, inclusion of matrix habitat within home ranges increased with decreasing patch size, placing individuals within smaller patches at a high risk of mortality. These risks may be mitigated in patches > 7 ha, and absent in patches >20-25 ha where predicted inclusion of matrix is lower or absent.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Using a coupled dynamic factor - random forest analysis (DFRFA) to reveal drivers of spatiotemporal heterogeneity in the semi-arid regions of southern Africa.

Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude, and spatial distribution of the key environmental and socioeconomic factors driving vegetation change in a southern African savanna. This research was conducted across the Kwando, Okavango and Zambezi catchments of southern Africa (Angola, Namibia, Botswana and Zambia) and explored vegetation cover change across the region from 2001-2010. A novel coupled analysis was applied to model the dynamic biophysical factors then to determine the discrete / social drivers of spatial heterogeneity on vegetation. Previous research applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique, to ten years of monthly remotely sensed vegetation data (MODIS-derived normalized difference vegetation index, NDVI), and a suite of time-series (monthly) environmental covariates: precipitation, mean, minimum and maximum air temperature, soil moisture, relative humidity, fire and potential evapotranspiration. This initial research was performed at a regional scale to develop meso-scale models explaining mean regional NDVI patterns. The regional DFA predictions were compared to the fine-scale MODIS time series using Kendall's Tau and Sen's Slope to identify pixels where the DFA model we had developed, under or over predicted NDVI. Once identified, a Random Forest (RF) analysis using a series of static social and physical variables was applied to explain these remaining areas of under- and over- prediction to fully explore the drivers of heterogeneity in this savanna system. The RF analysis revealed the importance of protected areas, elevation, soil type, locations of higher population, roads, and settlements, in explaining fine scale differences in vegetation biomass. While the previously applied DFA generated a model of environmental variables driving NDVI, the RF work developed here highlighted human influences dominating that signal. The combined DFRFA model approach explains almost 90% of the variance in NDVI across this landscape from 2001-2010. Our methodology presents a unique coupling of dynamic and static factor analyses, yielding novel insights into savanna heterogeneity, and providing a tool of great potential for researchers and managers alike

Population status of Pan troglodytes verus in Lagoas de Cufada Natural Park, Guinea-Bissau

The western chimpanzee, Pan troglodytes verus, has been classified as Endangered on the IUCN Red List since 1988. Intensive agriculture, commercial plantations, logging, and mining have eliminated or degraded the habitats suitable for P. t. verus over a large part of its range. In this study we assessed the effect of land-use change on the population size and density of chimpanzees at Lagoas de Cufada Natural Park (LCNP), Guinea-Bissau. We further explored chimpanzee distribution in relation to landscape-level proxies of human disturbance. Nest count and distance-sampling methods were employed along 11 systematically placed linear transects in 2010 and 2011. Estimated nest decay rate was 293.9 days (%CV = 58.8). Based on this estimate of decay time and using the Standing-Crop Nest Count Method, we obtained a habitat-weighted average chimpanzee density estimate for 2011 of 0.22 nest building chimpanzees/km2 (95% CI 0.08–0.62), corresponding to 137 (95% CI 51.0–390.0) chimpanzees for LCNP. Human disturbance had a negative influence on chimpanzee distribution as nests were built farther away from human settlements, roads, and rivers than if they were randomly distributed, coinciding with the distribution of the remaining patches of dense canopy forest. We conclude that the continuous disappearance of suitable habitat (e.g. the replacement of LCNP's dense forests by monocultures of cashew plantations) may be compromising the future of one of the most threatened Guinean coastal chimpanzee populations. We discuss strategies to ensure long-term conservation in this important refuge for this chimpanzee subspecies at its westernmost margin of geographic distribution.Publisher PDFPeer reviewe

A systematic review of the data, methods and environmental covariates used to map Aedes -borne arbovirus transmission risk

Acknowledgements: This work was discussed with the Technical Advisory Group on arboviruses (TAG-Arbovirus), the Secretariat of the Global Arbovirus Initiative (Raman Velayudhan, Laurence Cibrelus, Jennifer Horton, Marie-Eve Raguenaud, Maria Van Kerkhove, Qingxia Zhong), and the participants of the arbovirus risk mapping meeting held in Seattle in October 2022 as part of the ASTMH (Isabel Rodriguez-Barraquer, Leo Bastos, Simon Cauchemez, Ilaria Dorigatti, Neil Ferguson, Simon Hay, Wenbiao Hu, Axel Kroeger, Velma Lopez, A. Townsend Peterson, Maile Philips, David Pigott, Krystina Rysava, Sophie von Dobschütz, and Anna Winters).Funder: Princeton University Climate and Disease program with funding from High Meadows Environmental Institute Grand Challenges and Environmental Studies Strategic Fund and the Joseph & Susan Gatto FoundationFunder: Stanford Center for Innovation in Global Health, and the Stanford Woods Institute for the EnvironmentFunder: NSF CIBR: VectorByte: A Global Informatics Platform for studying the Ecology of Vector-Borne Diseases; Grant(s): NSF DBI 2016265Background: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). Results: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002–2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

Background: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). Results: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002–2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping