Remotely-sensed Environmental Data Research Articles

Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda

BackgroundMalaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey.MethodsThe results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model’s ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated.ResultsModels fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier.ConclusionsThese results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to transmission regularly occurring outside of the home. Additionally, they suggest that when predicting malaria risk the benefit may not outweigh the high costs of attaining detailed information on household predictors. Instead, using remotely-sensed data provides an equally effective, cost-efficient alternative.

Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventions

BackgroundSince 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach.MethodsThis study used individual and household-level data from the 2015 and 2018 annual malaria indicator surveys on Bioko Island, as well as remotely-sensed environmental data in multilevel logistic regression models to quantify the odds of malaria infection. The analyses were stratified by urban and rural settings and by survey year.ResultsMalaria prevalence was higher in 10–14-year-old children and similar between female and male individuals. After adjusting for demographic factors and other covariates, many of the variables investigated showed no significant association with malaria infection. The factor most strongly associated was history of travel to mainland Equatorial Guinea (mEG), which increased the odds significantly both in urban and rural settings (people who travelled had 4 times the odds of infection). Sleeping under a long-lasting insecticidal net decreased significantly the odds of malaria across urban and rural settings and survey years (net users had around 30% less odds of infection), highlighting their contribution to malaria control on the Island. Improved housing conditions indicated some protection, though this was not consistent across settings and survey year.ConclusionsMalaria risk on Bioko Island is heterogeneous and determined by a combination of factors interacting with local mosquito ecology. These interactions grant further investigation in order to better adapt control according to need. The single most important risk factor identified was travel to mEG, in line with previous investigations, and represents a great challenge for the success of malaria control on the Island.

Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria.

Onchocerciasis caused by infection with Onchocerca volvulus is a disease of public health importance and is highly associated with disability. As Nigeria is aiming at eliminating onchocerciasis by 2030, there is a need to develop newer tools to map disease prevalence and identify environmental factors driving disease prevalence, even in places that have not been previously targeted for preventive chemotherapy. This study produced predictive risk-maps of onchocerciasis in Ogun State. Georeferenced onchocerciasis infection data obtained from a cross-sectional survey at 32 locations between March and July 2015 together with remotely-sensed environmental data were analyzed using Ecological Niche Models (ENM). A total of 107 field occurrence points for O. volvulus infection were recorded. A total of 43 positive occurrence points were used for modelling. ENMs were used to estimate the current geographic distribution of O. volvulus in Ogun State. Maximum Entropy distribution modeling (MaxEnt) was used for predicting the potential suitable habitats, using a portion of the occurrence records. A total of 19 environmental variables were used to model the potential geographical distribution area under current climatic conditions. Empirical prevalence of 9.3% was recorded in this study. The geospatial distribution of infection revealed that all communities in Odeda Local Government Area (a peri-urban LGA) showed remarkably high prevalence compared with other LGAs. The predicted high-risk areas (probability > 0.8) of O. volvulus infection were all parts of Odeda, Abeokuta South, and Abeokuta North, southern part of Imeko-Afon, a large part of Yewa North, some parts of Ewekoro and Obafemi-Owode LGAs. The estimated prevalence for these regions were >60% (between 61% and 100%). As predicted, O. volvulus occurrence showed a positive association with variables reflecting precipitation in Ogun State. Our predictive risk-maps has provided useful information for the elimination of onchocerciais, by identifying priority areas for delivery of intervention in Ogun State, Nigeria.

Malaria risk factors in northern Namibia: The importance of occupation, age and mobility in characterizing high-risk populations.

In areas of low and unstable transmission, malaria cases occur in populations with lower access to malaria services and interventions, and in groups with specific malaria risk exposures often away from the household. In support of the Namibian National Vector Borne Disease Program’s drive to better target interventions based upon risk, we implemented a health facility-based case control study aimed to identify risk factors for symptomatic malaria in Zambezi Region, northern Namibia. A total of 770 febrile individuals reporting to 6 health facilities and testing positive by rapid diagnostic test (RDT) between February 2015 and April 2016 were recruited as cases; 641 febrile individuals testing negative by RDT at the same health facilities through June 2016 were recruited as controls. Data on socio-demographics, housing construction, overnight travel, use of malaria prevention and outdoor behaviors at night were collected through interview and recorded on a tablet-based questionnaire. Remotely-sensed environmental data were extracted for geo-located village residence locations. Multivariable logistic regression was conducted to identify risk factors and latent class analyses (LCA) used to identify and characterize high-risk subgroups. The majority of participants (87% of cases and 69% of controls) were recruited during the 2016 transmission season, an outbreak year in Southern Africa. After adjustment, cases were more likely to be cattle herders (Adjusted Odds Ratio (aOR): 4.46 95%CI 1.05–18.96), members of the police or other security personnel (aOR: 4.60 95%CI: 1.16–18.16), and pensioners/unemployed persons (aOR: 2.25 95%CI 1.24–4.08), compared to agricultural workers (most common category). Children (aOR 2.28 95%CI 1.13–4.59) and self-identified students were at higher risk of malaria (aOR: 4.32 95%CI 2.31–8.10). Other actionable risk factors for malaria included housing and behavioral characteristics, including traditional home construction and sleeping in an open structure (versus modern structure: aOR: 2.01 95%CI 1.45–2.79 and aOR: 4.76 95%CI: 2.14–10.57); cross border travel in the prior 30 days (aOR: 10.55 95%CI 2.94–37.84); and outdoor agricultural work at night (aOR: 2.09 95%CI 1.12–3.87). Malaria preventive activities were all protective and included personal use of an insecticide treated net (ITN) (aOR: 0.61 95%CI 0.42–0.87), adequate household ITN coverage (aOR: 0.63 95%CI 0.42–0.94), and household indoor residual spraying (IRS) in the past year (versus never sprayed: (aOR: 0.63 95%CI 0.44–0.90). A number of environmental factors were associated with increased risk of malaria, including lower temperatures, higher rainfall and increased vegetation for the 30 days prior to diagnosis and residing more than 5 minutes from a health facility. LCA identified six classes of cases, with class membership strongly correlated with occupation, age and select behavioral risk factors. Use of ITNs and IRS coverage was similarly low across classes. For malaria elimination these high-risk groups will need targeted and tailored intervention strategies, for example, by implementing alternative delivery methods of interventions through schools and worksites, as well as the use of specific interventions that address outdoor transmission.

Natural surroundings in childhood are associated with lower schizophrenia rates

High exposure to green space and natural environments has previously been associated with lower schizophrenia rates possibly through low air pollution and improved psychological restoration. Exposure to natural environments could explain the negative urban-rural gradient of schizophrenia, but it is unclear if all natural environments are associated with schizophrenia rates. We investigated the association between schizophrenia and growing up surrounded by environments classified as mainly urban, agricultural, near-natural green space, and blue space. Vegetation density and air pollution were assessed as potential pathways. We used the Danish population (943 027 people) and remotely-sensed environmental data to determine land cover exposure and vegetation density around each individual’s residence. Effect sizes were estimated using Cox regression and adjusted for air pollution, socioeconomic status, and urbanization. Our results show that growing up surrounded by non-urban environments is associated with lower schizophrenia rates. Firstly, growing up surrounded by non-built-up areas (agricultural areas, near-natural green and blue space) is associated with lower schizophrenia rates compared to urban areas. Secondly, rates decrease with vegetation density in a dose-response relationship for urban and agricultural areas. Air pollution mitigation more strongly explained the protective association in near-natural green spaces, implying that restorative pathways together with air pollution mitigation may explain lower rates in natural environments. This study suggests that ensuring access to natural environments during childhood may be important for schizophrenia prevention, whilst being the first study to show that natural environments may influence schizophrenia rates through multiple pathways.

Leptospirosis, Climate and Satellite-based Environmental Factors: A Temporal Modeling

ObjectiveTo quantify the effects of climate variability, selected remotely-sensed environmental factors on human leptospirosis in the high-risk counties in China.IntroductionLeptospirosis is a zoonotic disease caused by the pathogenic Leptospira bacteria and is ubiquitously distributed in tropical and subtropical regions. Leptospirosis transmission driven by complex factors include climatic, environmental and local social conditions 1. Each year, there are about 1 million cases of human leptospirosis reported globally and it causes approximately 60,000 people lost their lives due to infection 2. Yunnan Province and Sichuan Province are two of highly endemic areas in the southwest China that had contributed for 47% of the total national reported cases during 2005-2015 3. Factors underlying local leptospirosis transmission in these two areas is far from clear and thus hinder the efficacy of control strategies. Hence, it is essential to assess and identify local key drivers associated with persistent leptospirosis transmission in that areas to lay foundation for the development of early-warning systems. Currently, remote sensing technology provides broad range of physical environment data at various spatial and temporal scales 4, which can be used to understand the leptospirosis epidemiology. Utilizing satellite-based environmental data combined with locally-acquired weather data may potentially enhance existing surveillance programs in China so that the burden of leptospirosis could be reduced.MethodsThis study was carried out in two counties situated in different climatic zone in the southwestern China, Mengla and Yilong County (Fig 1). Total of 543 confirmed leptospirosis cases reported during 2006-2016 from both counties were used in this analysis. Time series decomposition was used to explore the long-term seasonality of leptospirosis incidence in two counties during the period studied. Monthly remotely-sensed environmental data such as normalized difference vegetation index (NDVI), modified normalized water difference index (MNDWI) and land surface temperature (LST) were collected from satellite databases. Climate data include monthly precipitation and relative humidity (RH) data were obtained from local weather stations. Lagged effects of rainfall, humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis was examined. Generalized linear model with negative binomial link was used to assess the relationships of climatic and physical environment factors with leptospirosis. Best-fitted model was determined based on the lowest information criterion and deviance.ResultsLeptospirosis incidence in both counties showed strong and unique annual seasonality. Bi-modal temporal pattern was exhibited in Mengla County while single epidemic curve was persistently demonstrated in Yilong County (Fig 2). Total of 10 and 20 models were generated for Mengla and Yilong County, respectively. After adjusting for seasonality, final best-fitted models indicated that rainfall at lag of 6-month (incidence rate ratio (IRR)= 0.989; 95% confidence interval (CI) 0.985-0.993, p<0.001) and current LST (IRR=0.857, 95%CI:0.729-0.929, p<0.001) significantly associated with leptospirosis in Mengla County (Table 1). While in Yilong, rainfall at 1-month lag, MNDWI (5-months lag) and LST (3-months lag) were associated with an increased incidence of leptospirosis with a risk ratio of 1.013 (95%CI: 1.003-1.023), 7.960 (95%CI: 1.241-47.66) and 1.193 (95%CI:1.095-1.301), respectively.ConclusionsOur study identified lagged effect and relationships of weather and remotely-sensed environmental factors with leptospirosis in two endemic counties in China. Rainfall in combination with satellite derived physical environment factors such as flood/water indicator (MNDWI) and temperature (LST) could help explain the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning system in to support leptospirosis control in China.

Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system

BackgroundEarly indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited.MethodsThe Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system was designed and implemented to integrate disease surveillance with environmental monitoring in support of operational malaria forecasting in the Amhara region of Ethiopia. A co-design workshop was held with computer scientists, epidemiological modelers, and public health partners to develop an initial list of system requirements. Subsequent updates to the system were based on feedback obtained from system evaluation workshops and assessments conducted by a steering committee of users in the public health sector.ResultsThe system integrated epidemiological data uploaded weekly by the Amhara Regional Health Bureau with remotely-sensed environmental data freely available from online archives. Environmental data were acquired and processed automatically by the EASTWeb software program. Additional software was developed to implement a public health interface for data upload and download, harmonize the epidemiological and environmental data into a unified database, automatically update time series forecasting models, and generate formatted reports. Reporting features included district-level control charts and maps summarizing epidemiological indicators of emerging malaria outbreaks, environmental risk factors, and forecasts of future malaria risk.ConclusionsSuccessful implementation and use of EPIDEMIA is an important step forward in the use of epidemiological and environmental informatics systems for malaria surveillance. Developing software to automate the workflow steps while remaining robust to continual changes in the input data streams was a key technical challenge. Continual stakeholder involvement throughout design, implementation, and operation has created a strong enabling environment that will facilitate the ongoing development, application, and testing of the system.

Malacological survey and geospatial distribution of <i>Indoplanor bisexutus</i> (Deshayes, 1934) and <i>Lymnaea natalensis</i> (Krauss 1848) snail vectors of trematode parasites, in Abeokuta, south-western, Nigeria

Vector snails of trematode parasites are important because of their role in the transmission of parasitic diseases. However, information on the diversity and distribution of snail hosts in Abeokuta area of Ogun State is scanty. Snails were collected from 31 water bodies including streams, rivers and dams on a monthly basis for six months. The snails were identified using shell morphology and examined for cercariae shedding. Collection sites were geo-referenced. Environmental data, Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), rainfall and Land cover corresponding to these locations were derived from Moderate resolution Imaging Spectroradiometer (MODIS). Binary logistic regression analysis was used to create spatial maps and predictive model map for snail species distribution. A total of 87 vector snails were collected; Lymnaea natalensis 65 (74.71%) and Indoplanor bisexutus 22 (25.29%).Nine out of 22I. exutus collected shed microcercouscercariae. Infected I. exutus, intermediate snail host of Schistosoma nasale, a cattle schistosome was found in two sites. The logistic regression analysis using remotely-sensed environmental data showed that NDVI was the significant variable influencing I. exutus distribution (B = -8.460, Sig = 0.043). Geospatial distribution maps were created to show the distribution of each snail species and potential transmission sites. This study mapped the distribution of snail hosts, identified possible transmission sites and demonstrates the presence of snail hosts of certain parasites in Abeokuta area (Abeokuta South and Abeokuta North Local Government Areas), the distribution of these snail hosts should be monitored to prevent transmission of these pathogens to humans.Keywords: geospatial, vector snails, trematode, distribution, malacological

Fortuitous encounters between seagliders and adult female northern fur seals (Callorhinus ursinus) off the Washington (USA) coast: upper ocean variability and links to top predator behavior.

Behavioral responses by top marine predators to oceanographic features such as eddies, river plumes, storms, and coastal topography suggest that biophysical interactions in these zones affect predators' prey, foraging behaviors, and potentially fitness. However, examining these pathways is challenged by the obstacles inherent in obtaining simultaneous observations of surface and subsurface environmental fields and predator behavior. In this study, migratory movements and, in some cases, diving behavior of 40 adult female northern fur seals (NFS; Callorhinus ursinus) were quantified across their range and compared to remotely-sensed environmental data in the Gulf of Alaska and California Current ecosystems, with a particular focus off the coast of Washington State (USA) – a known foraging ground for adult female NFS and where autonomous glider sampling allowed opportunistic comparison of seal behavior to subsurface biophysical measurements. The results show that in these ecosystems, adult female habitat utilization was concentrated near prominent coastal topographic, riverine, or inlet features and within 200 km of the continental shelf break. Seal dive depths, in most ecosystems, were moderated by surface light level (solar or lunar), mirroring known behaviors of diel vertically-migrating prey. However, seal dives differed in the California Current ecosystem due to a shift to more daytime diving concentrated at or below the surface mixed layer base. Seal movement models indicate behavioral responses to season, ecosystem, and surface wind speeds; individuals also responded to mesoscale eddies, jets, and the Columbia River plume. Foraging within small scale surface features is consistent with utilization of the inner coastal transition zone and habitats near coastal capes, which are known eddy and filament generation sites. These results contribute to our knowledge of NFS migratory patterns by demonstrating surface and subsurface behavioral responses to a spatially and temporally dynamic ocean environment, thus reflecting its influence on associated NFS prey species.

Environmental determinant of malaria cases among travellers

BackgroundApproximately 125 million travellers visit malaria-endemic countries annually and about 10,000 cases of malaria are reported after returning home. Due to the fact that malaria is insect vector transmitted, the environment is a key determinant of the spread of infection. Geo-climatic factors (such as temperature, moisture, water quality) determine the presence of Anopheles breeding sites, vector densities, adult mosquito survival rate, longevity and vector capacity. Several studies have shown the association between environmental factors and malaria incidence in autochthonous population. The association between the incidence of clinical malaria cases among non-immune travellers and environmental factors is yet to be evaluated. The objective of the present study was to identify, at a country scale (Ivory Coast), the environmental factors that are associated with clinical malaria among non-immune travellers, opening the way for a remote sensing-based counselling for malaria risk prevention among travellers.MethodsThe study sample consisted in 87 cohorts, including 4,531 French soldiers who travelled to Ivory Coast, during approximately four months, between September 2002 and December 2006. Their daily locations were recorded during the entire trip. The association between the incidence of clinical malaria and other factors (including individual, collective and environmental factors evaluated by remote sensing methods) was analysed in a random effect mixed Poisson regression model to take into account the sampling design.ResultsOne hundred and forty clinical malaria cases were recorded during 572,363 person-days of survey, corresponding to an incidence density of 7.4 clinical malaria episodes per 1,000 person-months under survey. The risk of clinical malaria was significantly associated with the cumulative time spent in areas with NDVI > 0.35 (RR = 2,42), a mean temperature higher than 27°C (RR = 2,4), a longer period of dryness during the preceding month (RR = 0,275) and the cumulative time spent in urban areas (RR = 0,52).ConclusionsThe present results suggest that remotely-sensed environmental data could be used as good predictors of the risk of clinical malaria among vulnerable individuals travelling through African endemic areas.

Westward ho: chasing rural China across time and space

My entree to fieldwork in rural China began with a seven month stay in Taiwan in 1985. I accompanied researchers from the Agricultural Bureau surveying households who were recently taught to grow a variety of crops such as strawberries, vegetables, and a crop I had never seen before called lingjiao ([TEXT NOT REPRODUCIBLE IN ASCII], water caltrop) in an effort to reduce the majestic piles of surplus rice that filled every cupboard and cranny of the island at that time. Since I could not even identify the edible parts of the plant I was supposed to economically evaluate, it is quite safe to say that on that thrilling virgin voyage and many of those that followed, I knew a lot less about local conditions than I should have prior to going into the field. However, with the great kindness and patience typically shown to strangers anywhere in China coupled with a willingness to repeatedly ask stupid questions, eventually I learned enough to get by. Since 1986 when I conducted my dissertation research in nine counties in Jiangsu Province during an 18-month stay, I have conducted rural survey-based projects in twelve additional provinces and have visited six more. To summarize my research in a sentence, I would say I study agricultural systems, investigating the roles that agriculture plays across China's rural economic landscapes and how these agricultural activities, in turn, impact rural people and their environments. I have conducted most of my research in China's poorer (and drier) rural areas (pinkun dichu), with my projects shifting westward over time as millions of farm families have been lifted from poverty in the eastern provinces on the back of rural industrialization and specialized commodity production. I have never worked alone; rather my projects are always conducted in conjunction with Chinese scholars and extension officials who identify current problems and issues that could or should be analyzed. Typically, I meet with a team a year before a project starts to discuss possible topics, approaches, and potential sources of funding. This is both good and bad: good in the sense that I feel our projects generate useful information and draw attention to contemporary issues challenging rural China because the topics come from local agents and agencies, but bad because my projects are almost exclusively driven by issues, seldom by theory--a sort of intellectual ambulance chasing. Theories do emerge from the work, but they do so during the research process, not in anticipation of it. This is NOT exactly the way it is supposed to be done--at least according to the textbooks, but I do not think this is uncommon, especially in applied work. Apologies aside, these projects have allowed me to visit family farms in countless villages throughout China and conduct surveys and/or in-depth interviews for a quarter of a century (Figure 1). Learning new methods from friends and articles, I try to join these household-scale data with published statistical data and remotely-sensed environmental data derived from a variety of sources to generate synthetic multiscale ecological economic studies. Still, when all is said and done, I most like being with the farm families, killing time in villages talking to farmers about agriculture and rural issues but often digressing into stimulating topics such as how much I paid for my shoes or current crop prices in the US. Since 1985, I have gone to China almost every year with stays ranging from a typical summer season of 1 to 2 months to several opportunities of a year or more. For a foreigner bumbling about in rural China, it has been a pretty good run--enjoyable, sometimes exciting, often funny, and always interesting. Last year during July in Gansu Province, accompanied by four young Chinese graduate students and my friend and colleague Jay Emerson, I crossed the Yellow River during flood stage on a six-foot long raft woven of bamboo and buoyed by six inflated sheep skin bladders (Yangpi fa). …

Elephant seasonal vegetation preferences across dry and wet savannas

As African savanna elephants ( Loxodonta africana) become increasingly confined to smaller fragmented landscapes, concern over their potential detrimental impacts on vegetation and biodiversity has increased. Understanding elephant vegetation preferences across relevant spatial and temporal scales is a critical step towards managing protected areas for the persistence of both elephants and biodiversity. To better understand elephant vegetation selection, we fitted 68 elephants with GPS collars across a strong rainfall gradient spanning seven southern African countries over a period of 6 years. We compared elephant locations with remotely-sensed environmental data that measure bi-monthly vegetation greenness across the study area. Elephants consistently seek out greener than expected vegetation throughout the year. Interestingly, they do so by utilizing vegetation with different phenologies and by selecting landscapes when they are greener than their surroundings. We found no differences between dry and wet savannas. These patterns persist even when elephants are constrained by seasonally available water. In the wet season, elephants select seasonally variable landscapes such as open woodlands, shrublands, and grasslands. These landscapes have a lower average annual greenness but become very green for a few months in the wet season. In the dry season, elephants prefer less variable landscapes that are more consistently green year-round such as well-wooded areas and closed woodlands. Because elephants prefer different vegetation types at different times of the year, small homogeneous protected areas may be unsuitable for elephants. Since elephants prefer woody vegetation during the dry season when they are constrained by water, human actions that increase dry season water availability may contribute to detrimental elephant impacts on vegetation and biodiversity.

Spatiotemporal habitat use by breeding sooty shearwaters Puffinus griseus

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 391:209-220 (2009) - DOI: https://doi.org/10.3354/meps07932 Spatiotemporal habitat use by breeding sooty shearwaters Puffinus griseus Scott A. Shaffer1,*, Henri Weimerskirch2, Darren Scott3, David Pinaud2, David R. Thompson4, Paul M. Sagar5, Henrik Moller3, Graeme A. Taylor6, David G. Foley7,8, Yann Tremblay1,9, Daniel P. Costa1 1Ecology and Evolutionary Biology and Institute of Marine Sciences, University of California, 100 Shaffer Road, Santa Cruz, California 95060-5730, USA 2Centre d’Etudes Biologiques de Chizé, CNRS, 79360 Villiers en Bois, France 3Department of Zoology, University of Otago, 340 Great King Street, Dunedin, New Zealand 4National Institute of Water and Atmospheric Research, 301 Evans Bay Parade, Wellington, New Zealand 5National Institute of Water and Atmospheric Research, 10 Kyle Street, Riccarton, Christchurch, New Zealand 6Department of Conservation, PO Box 108, Kilbirnie, Wellington, New Zealand 7Joint Institute for Marine and Atmospheric Research, University of Hawaii, 1000 Pope Road, Honolulu, Hawaii 96822, USA 8National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center, 1352 Lighthouse Avenue, Pacific Grove, California 93950, USA 9Centre de Recherche Halieutique Méditerranéenne et Tropicale, IRD, 34203 Sète cedex, France *Email: shaffer@biology.ucsc.edu ABSTRACT: Breeding sooty shearwaters Puffinus griseus cycle between long (11 to 14 d) and short (1 to 2 d) foraging bouts at sea, but no information exists on bird behavior during these trips. We tested the hypothesis that shearwaters use these long trips to travel to distant Antarctic waters compared to remaining in local waters. Patterns of habitat use of 28 breeding sooty shearwaters were studied using 6 g archival data loggers that recorded location, environmental temperature, and diving behavior. Dive activity was compared to remotely-sensed environmental data to characterize the habitats visited by shearwaters on long and short trips. Sooty shearwaters traveled predominantly (70% of all long trips) to cold oceanic waters along the Polar Front (mean ± SD, 1970 ± 930 km from colony) on long trips or remained within warmer neritic waters of the New Zealand shelf (515 ± 248 km from colony) on short trips. Diving depths (mean depth 15.9 ± 10.8 m, max depth 69.9 m, n = 2007 dives) were not significantly different between excursion types. Activity patterns suggest that shearwaters commuted between distant foraging grounds (e.g. Polar Front) and the breeding colony and that more than 95% of diving activity occurred during daylight hours. Although shearwaters traveled primarily to Antarctic waters on long trips, occasional trips around New Zealand waters were observed; all but 2 birds were from the northern-most study colony. Oceanic habitats in Antarctic waters were substantially different from neritic habitats around New Zealand, indicating that shearwaters experience dramatically different environmental conditions associated with each excursion type. The ability of sooty shearwaters to use 2 vastly different habitats provides greater flexibility for maximizing resource acquisition during breeding and reduces competition near the colony. KEY WORDS: Activity · Archival data logger · Diving · Geolocation · Puffinus griseus · Tracking ·Sea surface temperature · Sooty shearwater Full text in pdf format PreviousNextCite this article as: Shaffer SA, Weimerskirch H, Scott D, Pinaud D and others (2009) Spatiotemporal habitat use by breeding sooty shearwaters Puffinus griseus. Mar Ecol Prog Ser 391:209-220. https://doi.org/10.3354/meps07932 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 391. Online publication date: September 28, 2009 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2009 Inter-Research.

Fences and artificial water affect African savannah elephant movement patterns

The IUCN Redlist considers the African savannah elephant ( Loxodonta africana) to be “vulnerable” despite it numbering in the 100,000s and having a large geographical range. This seeming paradox stems from how quickly human persecution can eliminate elephants across large areas and how quickly elephant numbers can increase when protected. Much elephant research concentrates on the extent and consequences of elephant persecution. Where elephants thrive, two other human interventions, the provision of artificial water and the construction of fences may have large, and perhaps unintended, impacts on elephant behavior. In general, successful management requires that we understand elephant movements and land-use choices across large areas and long periods. Here, we ask specifically how artificial water and fences might affect these movements. To do this, we first characterize how elephants move in different seasons and landscape types, in different years, and how these patterns change over a region that varies considerably in annual rainfall. We fitted 73 elephants with GPS collars across a large rainfall gradient spanning seven southern African countries over a period of 6 years. We analyzed remotely-sensed environmental data from four satellite borne sensors that measure daily rainfall, weekly temperature, bi-monthly greenness, and summarise human infrastructure. Elephants move approximately 6 km/day in dry landscapes, down to approximately 3 km/day in the wettest ones. Strong seasonal differences modulate geographic differences. Elephants move less, cover less area, and are more faithful to landscapes across years in the dry season than the wet. Water availability drives these seasonal patterns. Seasonal differences in the area covered are less pronounced in wet landscapes where permanent water is more dispersed. Within-day movements reveal that elephants are consistently crepuscular but more active at night than midday when temperatures are high. Direction-changes are centered at midnight when elephants are close to water indicating regular nighttime treks to water. By design, our analyses seek to find general patterns of elephant movements — something that one can achieve only across a large range of locations and ecological conditions — in order to understand the impact of human interventions. We show that both interventions reduce seasonal differences in elephant ranging patterns and increase local impacts of elephants on the vegetation. Artificial water sources allow more extensive dry season ranging, allowing elephants to use — and potentially overexploit — vegetation in areas that would have be otherwise inaccessible to them except in the wet season. Fences cause elephants to “bunch-up” against them during the wet season, again locally increasing the pressure elephants put on their resources.

Modelling of malaria temporal variations in Iran

To model the temporal variations in malaria episodes in a hypo-endemic area of Iran and to assess the feasibility of an epidemic early warning system. Malaria episode data for Kahnooj District, south-east Iran, were collected from the local health system for the period 1994-2002. Plasmodium species-specific models were generated using Poisson regression. Starting with a simple model which included only temporal effects, we iteratively added more explanatory variables to maximize goodness of fit. Of 18,268 recorded malaria episodes, more than 67% were due to P. vivax. In addition to seasonality and secular trend, we found that incorporating a 1-month time lag between key meteorological variables and the predicted number of cases maximized goodness of fit. Maximum temperature, mean relative humidity and previous numbers of malaria cases were the most important predictors. These were included in the model with lags of no less than three dekads, i.e. three 10-day periods or effectively 1 month. Simple models based on climatic factors and information on past case numbers may be useful in improving the quality of the malaria control programme in Iran, particularly in terms of assuring accurate targeting of interventions in time and space. The models developed in this study are based on explanatory data that incorporate a lag of 1 month (i.e. data that were recorded 21-50 days previously). In practice, this translates into an operational 'window' of 1 month. Provided appropriate modes of data exchange exist between key stakeholders and appropriate systems for operational response are in place, this type of early warning information has the potential to lead to significant reductions in malaria morbidity in Iran.

Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data

BackgroundRemotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics.Methodology/Principal FindingsWe present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005.Conclusions/SignificanceGlobal temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

Spatial risk profiling of Schistosoma japonicum in Eryuan county, Yunnan province, China

Bayesian spatial risk profiling holds promise to enhance our understanding of the epidemiology of parasitic diseases, and to target interventions in a cost-effective manner. Here, we present findings from a study using Bayesian variogram models to map and predict the seroprevalence of Schistosoma japonicum in Eryuan county, Yunnan province, China, including risk factor analysis. Questionnaire and serological data were obtained through a cross-sectional survey carried out in 35 randomly selected villages with 3,220 people enrolled. Remotely-sensed environmental data were derived from publicly available databases. Bivariate and non-spatial Bayesian multiple logistic regression models were used to identify associations between the local seroprevalence and demographic (i.e. age and sex), environmental (i.e. location of village, altitude, slope, land surface temperature and normalized difference vegetation index) and socio-economic factors. In the spatially-explicit Bayesian model, S. japonicum seroprevalence was significantly associated with sex, age and the location of the village. Males, those aged below 10 years and inhabitants of villages situated on steep slopes (inclination =20 degrees ) or on less precipitous slopes of >5 degrees above 2,150 m were at lower risk of seroconversion than their respective counterparts. Our final prediction model revealed an elevated risk for seroconversion in the plains of the eastern parts of Eryuan county. In conclusion, the prediction map can be utilized for spatial targeting of schistosomiasis control interventions in Eryuan county. Moreover, S. japonicum seroprevalence studies might offer a convenient means to assess the infection pressure experienced by local communities, and to improve risk profiling in areas where the prevalence and infection intensities have come down following repeated rounds of praziquantel administration.

Guest Editorial: Advances in the Modelling and Interpretation of Remotely Sensed Environmental Data

Guest Editorial: Advances in the Modelling and Interpretation of Remotely Sensed Environmental Data