Using MODIS satellite imagery to predict hantavirus risk

Aims Sin Nombre virus (SNV), a strain of hantavirus, causes hantavirus pulmonary syndrome (HPS) in humans, a deadly disease with high mortality rate (> 50%). The primary virus host is the deer mouse, and greater abundance of deer mice has been shown to increase the human risk of HPS. Our aim is to identify and compare vegetation indices and associated time lags for predicting hantavirus risk using remotely sensed imagery. Location Utah, USA. Methods A 5‐year time‐series of moderate‐resolution imaging spectroradiometer (MODIS) satellite imagery and corresponding field data was utilized to compare various vegetation indices that measure productivity with the goal of indirectly estimating mouse abundance and SNV prevalence. Relationships between the vegetation indices and deer mouse density, SNV prevalence and the number of infected deer mice at various time lags were examined to assess which indices and associated time lags might be valuable in predicting SNV outbreaks. Results The results reveal varying levels of positive correlation between the vegetation indices and deer mouse density as well as the number of infected deer mice. Among the vegetation indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) produced the highest correlations with deer mouse density and the number of infected deer mice using a time lag of 1.0 to 1.3 years for May and June imagery. Main conclusions This study demonstrates the potential for using MODIS time‐series satellite imagery in estimating deer mouse abundance and predicting hantavirus risk. The 1‐year time lag provides a great opportunity to apply satellite imagery to predict upcoming SNV outbreaks, allowing preventive strategies to be adopted. Analysis of different predictive indices and lags could also be valuable in identifying the time windows for data collection for practical uses in monitoring rodent abundance and subsequent disease risk to humans.     

Geographic variation in seasonality and its influence on the dynamics of an infectious disease

Seasonal changes in environmental drivers – such as temperature, rainfall, and resource availability – have the potential to shape infection dynamics through their reverberating effects on biological processes including host abundance and susceptibility to infection. However, seasonality varies geographically. We therefore expect marked differences in infection dynamics between regions with different seasonal patterns. By pairing extensive Avian Influenza Virus (AIV) surveillance data – 65 358 individual bird samples from 12 species of dabbling ducks sampled at 174 locations across North America – with quantification of seasonality using remote sensed data indicative for primary productivity (normalised differenced vegetation index, NDVI), we provide evidence that seasonal dynamics influence infection dynamics across a continent. More pronounced epidemics were seen to occur in regions experiencing a higher degree of seasonality, and epidemics of lower amplitude and longer duration occurred in regions with a more protracted and lower seasonal amplitude. These results demonstrate the potential importance of geographic variation in seasonality for explaining geographic variation in the dynamics of infectious diseases in wildlife.     

The Application of Genomics to Emerging Zoonotic Viral Diseases

Interspecies transmission of pathogens may result in the emergence of new infectious diseases in humans as well as in domestic and wild animals. Genomics tools such as high-throughput sequencing, mRNA expression profiling, and microarray-based analysis of single nucleotide polymorphisms are providing unprecedented ways to analyze the diversity of the genomes of emerging pathogens as well as the molecular basis of the host response to them. By comparing and contrasting the outcomes of an emerging infection with those of closely related pathogens in different but related host species, we can further delineate the various host pathways determining the outcome of zoonotic transmission and adaptation to the newly invaded species. The ultimate challenge is to link pathogen and host genomics data with biological outcomes of zoonotic transmission and to translate the integrated data into novel intervention strategies that eventually will allow the effective control of newly emerging infectious diseases.     

Vegetation dynamics and avian seasonal migration: clues from remotely sensed vegetation indices and ecological niche modelling

Aim Regional movements of tropical birds are among the least understood patterns of migration, generally assumed to be related to seasonality of vegetation and food resources. We used readily available remotely sensed data to analyse this relationship in the three‐wattled bellbird (Procnias tricarunculatus), an IUCN Vulnerable canopy frugivorous bird that undergoes localized altitudinal and latitudinal movements between several non‐breeding and breeding ranges. Location Central America. Methods We generated ecological niche models (GARP and Maxent) based on remotely sensed vegetation indices (enhanced vegetation index, EVI; red index, RI; and normalized difference water index, NDWI) that were used as proxies for canopy characteristics and phenological changes between seasons. The variation in EVI was also analysed in relation to known bellbird seasonal migration patterns. Results Remotely sensed summaries of intra‐annual vegetation variation could not explain known movement patterns of bellbirds breeding in Monteverde Cloud Forest Reserve, Costa Rica. The ecological niche modelling (ENM) framework used for exploring potential movement patterns of another population of bellbirds, observed in March 2004 in Corcovado National Park, Costa Rica, showed that the Atlantic and Pacific lowlands are suitable for the bellbirds year‐round, while middle elevations, where breeding may occur, have limited suitability. Main conclusions Our findings suggest that: (1) the Corcovado population tracks similar environmental conditions during the non‐breeding season; (2) migration to middle elevation breeding sites might be related to factors other than vegetation seasonality or breeding might not be restricted to these elevation ranges; and (3) the lowlands comprising most of the suitable areas for bellbirds are where anthropogenic pressure is highest, so conservation status should consider this threat, and conservation planning should emphasize these areas.     

日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪

植被物候期(春季返青和秋季衰老)是表征生物响应和陆地碳循环的基础信息。由于常绿针叶林冠层绿度的季节变动较弱,遥感提取常绿针叶林的物候信息存在着较大的不确定性,是目前区域物候监测中的难点。利用MODIS植被指数(归一化植被指数NDVI和增强型植被指数EVI)、GOME-2日光诱导叶绿素荧光(SIF)和通量数据(总初级生产力GPP)估算2007—2011年亚热带常绿针叶林物候期,用来比较三类遥感指数估算常绿针叶林物候的差异。结果表明:基于表征光合作用物候的通量GPP数据估算得到5年内亚热带常绿针叶林生长季开始时间(SOS_(GPP))为第63天,生长季结束时间(EOS_(GPP))为第324天,生长季长度为272天;基于反映植被光合作用特征的SIF曲线获得物候信息要滞后GPP物候期,其中生长季开始时间滞后19天,生长季结束时间滞后2天;基于传统植被指数NDVI和EVI的物候期滞后GPP物候期的时间要大于SIF滞后期,其中植被指数SOS滞后SOS_(GPP)31天,植被指数EOS滞后EOS_(GPP)10—17天。虽然基于3种遥感指数估算的春季和秋季物候都滞后于通量GPP的物候期,但是卫星SIF的物候信息能够更好地捕捉常绿针叶林的生长阶段。同时,春季温度是影响森林生长季开始时间的最重要因素;秋季水分和辐射是影响生长季结束时间的关键因素。由此可见,SIF估算的亚热带常绿针叶林的春季和秋季物候的滞后时间要短于传统植被指数,能更好地追踪常绿林光合作用的季节性,为深入研究陆地生态系统碳循环及其对气候变化的响应提供重要的基础。     

Integrating vegetation field surveys with remotely sensed data

Summary   This paper explores data compatibility issues arising from the assessment of remnant native vegetation condition using satellite remote sensing and field-based data. Space-borne passive remote sensing is increasingly used as a way of providing a total sample and synoptic overview of the spectral and spatial characteristics of native vegetation canopies at a regional scale. However, integrating field-collected data often not designed for integration with remotely sensed data can lead to data compatibility issues. Subsequent problems associated with the integration of unsuited datasets can contribute to data uncertainty and result in inconclusive findings. It is these types of problems (and potential solutions) that form the basis of this paper. In other words, how can field surveys be designed to support and improve compatibility with remotely sensed total surveys? Key criteria were identified for consideration when designing field-based surveys of native vegetation condition (and other similar applications) with the intent to incorporate remotely sensed data. The criteria include recommendations for the siting of plots, the need for reference location plots, the number of sample sites and plot size and distribution, within a study area. The difficulties associated with successfully integrating these data are illustrated using real examples taken from a study of the vegetation in the Little River Catchment, New South Wales, Australia.     

Species Richness and Abundance of Cerambycidae (Coleoptera) in Huatulco,Oaxaca, Mexico; Relationships with Phenological Changes in the Tropical Dry Forest

Cerambycidae have an important ecological role in initiating the degradation process of dead wood, but few studies have evaluated Cerambycidae community attributes in relation to ecosystem phenology. We surveyed the cerambicid fauna of the tropical dry forest in Huatulco, Oaxaca, Mexico, and explored the relationship of Cerambycidae species richness and abundance with phenological changes in vegetation. We applied three collecting methods of light traps, direct collection, and Malaise traps to survey Cerambycidae throughout 2005. To determine seasonal variations, we collected samples in the dry season month of February in the rainy season of May–July and August–September, and in the transition months of October and November through. We collected and identified 145 species, 88 genera, 37 tribes, and four subfamilies. The subfamily with the highest number of species was Cerambycinae (100 species), and the tribe with the highest number of genera and species was Elaphidiini with 13 genera and 33 species. The ICE non-parametric estimator determined an overall expected richness of 373 species, while the overall Shannon Diversity Index was 4.1. Both species richness and abundance varied seasonally, with the highest values recorded in the rainy season and the lowest in the dry season. Overall species abundance was not significantly correlated to monthly rainfall or EVI neither, only for “direct collecting” the EVI vs Richness and EVI vs Shannon Diversity Index were significantly correlated. We propose that the seemingly contradictory relationships between seasonal richness patterns of Cerambycidae and the greening/senescence of vegetation (EVI) may be explained by the seasonal availability of dead organic matter, flowers, or leafy vegetation that may be synchronized with the behavior of different cerambycid species.     

Vegetation browning: global drivers,impacts, and feedbacks

As global climate conditions continue to change, disturbance regimes and environmental drivers will continue to shift, impacting global vegetation dynamics. Following a period of vegetation greening, there has been a progressive increase in remotely sensed vegetation browning globally. Given the many societal benefits that forests provide, it is critical that we understand vegetation dynamic alterations. Here, we review associative drivers, impacts, and feedbacks, revealing the complexity of browning. Concomitant increases in browning include the weakening of ecosystem services and functions and alterations to vegetation structure and species composition, as well as the development of potential positive climate change feedbacks. Also discussed are the current challenges in browning detection and understanding associated impacts and feedbacks. Finally, we outline recommended strategies.     

Altitudinal patterns of tick and host abundance: a potential role for climate change in regulating tick-borne diseases?

The impact of climate change on vector-borne infectious diseases is currently controversial. In Europe the primary arthropod vectors of zoonotic diseases are ticks, which transmit Borrelia burgdorferi sensu lato (the agent of Lyme disease), tick-borne encephalitis virus and louping ill virus between humans, livestock and wildlife. Ixodes ricinus ticks and reported tick-borne disease cases are currently increasing in the UK. Theories for this include climate change and increasing host abundance. This study aimed to test how I. ricinus tick abundance might be influenced by climate change in Scotland by using altitudinal gradients as a proxy, while also taking into account the effects of hosts, vegetation and weather effects. It was predicted that tick abundance would be higher at lower altitudes (i.e. warmer climates) and increase with host abundance. Surveys were conducted on nine hills in Scotland, all of open moorland habitat. Tick abundance was positively associated with deer abundance, but even after taking this into account, there was a strong negative association of ticks with altitude. This was probably a real climate effect, with temperature (and humidity, i.e. saturation deficit) most likely playing an important role. It could be inferred that ticks may become more abundant at higher altitudes in response to climate warming. This has potential implications for pathogen prevalence such as louping ill virus if tick numbers increase at elevations where competent transmission hosts (red grouse Lagopus lagopus scoticus and mountain hares Lepus timidus) occur in higher numbers.     

Differing Sensitivities to Fire Disturbance Result in Large Differences Among Remotely Sensed Products of Vegetation Disturbance

Ecosystems - Recent advances in high-performance computing (HPC) have promoted the creation of standardized remotely sensed products that map annual vegetation disturbance through two primary...     

Remote sensing variables as predictors of habitat suitability of the viscacha rat (<Emphasis Type="Italic">Octomys mimax</Emphasis>), a rock-dwelling mammal living in a desert environment

Identifying high-quality habitats across large areas is a central goal in biodiversity conservation. Remotely sensed data provide the opportunity to study different habitat characteristics (e.g., landscape topography, soil, vegetation cover, climatic factors) that are difficult to identify at high spatial and temporal resolution on the basis of field studies. Our goal was to evaluate the applicability of remotely sensed information as a potential tool for modeling habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling species that lives in a desert ecosystem. We fitted models considering raw indices (i.e., green indices, Brightness Index (BI) and temperature) and their derived texture measures on locations used by and available for the viscacha rat. The habitat preferences identified in our models are consistent with results of field studies of landscape use by the viscacha rat. Rocky habitats were well differentiated by the second-order contrast of BI, instead of BI only, making an important contribution to the global model by capturing the heterogeneity of the substratum. Furthermore, rocky habitats are able to maintain more vegetation than much of the surrounding desert; hence, their availability might be estimated using SATVI (Soil Adjusted Total Vegetation Index) and its derived texture measures: second-order contrast and entropy. This is the first study that evaluates the usefulness of remotely sensed data for predicting and mapping habitat suitability for a small-bodied rock dwelling species in a desert environment. Our results may contribute to conservation efforts focused on these habitat specialist species by using good predictors of habitat quality.     

What limits the evolutionary emergence of pathogens?

The ability of a pathogen to cause an epidemic when introduced in a new host population often relies on its ability to adapt to this new environment. Here, we give a brief overview of recent theoretical and empirical studies of such evolutionary emergence of pathogens. We discuss the effects of several ecological and genetic factors that may affect the likelihood of emergence: migration, life history of the infectious agent, host heterogeneity, and the rate and effects of mutations. We contrast different modelling approaches and indicate how details in the way we model each step of a life cycle can have important consequences on the predicted probability of evolutionary emergence. These different theoretical perspectives yield important insights into optimal surveillance and intervention strategies, which should aim for a reduction in the emergence (and re-emergence) of infectious diseases.     

A Quantitative Prioritisation of Human and Domestic Animal Pathogens in Europe

Disease or pathogen risk prioritisations aid understanding of infectious agent impact within surveillance or mitigation and biosecurity work, but take significant development. Previous work has shown the H-(Hirsch-)index as an alternative proxy. We present a weighted risk analysis describing infectious pathogen impact for human health (human pathogens) and well-being (domestic animal pathogens) using an objective, evidence-based, repeatable approach; the H-index. This study established the highest H-index European pathogens. Commonalities amongst pathogens not included in previous surveillance or risk analyses were examined. Differences between host types (humans/animals/zoonotic) in pathogen H-indices were explored as a One Health impact indicator. Finally, the acceptability of the H-index proxy for animal pathogen impact was examined by comparison with other measures. 57 pathogens appeared solely in the top 100 highest H-indices (1) human or (2) animal pathogens list, and 43 occurred in both. Of human pathogens, 66 were zoonotic and 67 were emerging, compared to 67 and 57 for animals. There were statistically significant differences between H-indices for host types (humans, animal, zoonotic), and there was limited evidence that H-indices are a reasonable proxy for animal pathogen impact. This work addresses measures outlined by the European Commission to strengthen climate change resilience and biosecurity for infectious diseases. The results include a quantitative evaluation of infectious pathogen impact, and suggest greater impacts of human-only compared to zoonotic pathogens or scientific under-representation of zoonoses. The outputs separate high and low impact pathogens, and should be combined with other risk assessment methods relying on expert opinion or qualitative data for priority setting, or could be used to prioritise diseases for which formal risk assessments are not possible because of data gaps.     

Prediction of Peromyscus maniculatus (deer mouse) population dynamics in Montana, USA, using satellite-driven vegetation productivity and weather data

Deer mice (Peromyscus maniculatus) are the main reservoir host for Sin Nombre virus, the primary etiologic agent of hantavirus pulmonary syndrome in North America. Sequential changes in weather and plant productivity (trophic cascades) have been noted as likely catalysts of deer mouse population irruptions, and monitoring and modeling of these phenomena may allow for development of early-warning systems for disease risk. Relationships among weather variables, satellite-derived vegetation productivity, and deer mouse populations were examined for a grassland site east of the Continental Divide and a sage-steppe site west of the Continental Divide in Montana, USA. We acquired monthly deer mouse population data for mid-1994 through 2007 from long-term study sites maintained for monitoring changes in hantavirus reservoir populations, and we compared these with monthly bioclimatology data from the same period and gross primary productivity data from the Moderate Resolution Imaging Spectroradiometer sensor for 2000-06. We used the Random Forests statistical learning technique to fit a series of predictive models based on temperature, precipitation, and vegetation productivity variables. Although we attempted several iterations of models, including incorporating lag effects and classifying rodent density by seasonal thresholds, our results showed no ability to predict rodent populations using vegetation productivity or weather data. We concluded that trophic cascade connections to rodent population levels may be weaker than originally supposed, may be specific to only certain climatic regions, or may not be detectable using remotely sensed vegetation productivity measures, although weather patterns and vegetation dynamics were positively correlated.     

Spatial analysis of growing season length control over net ecosystem exchange

Using data from 28 flux measurement sites, we performed an analysis of the relationship between annual net ecosystem exchange (NEE) and the length of the carbon uptake period (CUP) (the number of days when the ecosystem is a net carbon sink). The observations suggest a linear correlation between the two quantities. The change in annual carbon exchange per day of the CUP differs significantly between deciduous and evergreen vegetation types. The sites containing vegetation with short‐lived foliage (less than 1 year) have higher carbon uptake and respiration rates than evergreen vegetation. The ratio between mean daily carbon exchange rates during carbon uptake and release periods is relatively invariant (2.73±1.08) across different vegetation types. This implies that a balance between carbon release and uptake periods exists despite different photosynthetic pathways, life forms, and leaf habits. The mean daily carbon sequestration rate for these ecosystems never exceeds the carbon emission rate by more than a factor of 3. Growing season lengths for the study sites were derived from the normalized difference vegetation index (NDVI) of advanced very‐high‐resolution radiometer and from the enhanced vegetation index (EVI) of VEGETATION SPOT‐4. NDVI and EVI were found to be closely related to the CUP, and consequently they also can be used to approximate annual carbon exchange of the ecosystems. This approach has potential for allowing extrapolation of NEE over large areas from remotely sensed data, given a certain amount of ancillary information. This method could complement the currently existing techniques for extrapolation, which rely upon modeling of the individual gross fluxes.     

Evolution of virulence in heterogeneous host communities under multiple trade-offs

Many pathogens and parasites are transmitted through hosts that differ in species, sex, genotype, or immune status. In addition, virulence (here defined as disease-induced mortality) and transmission can vary during the infectious period within hosts of different state. Most models of virulence evolution assume that transmission and virulence are constant over the infectious period and that the host population is homogenous. Here, we examine a multispecies susceptible-infected-recovered (SIR) model where transmission occurs within and between species, and transmission and virulence varied during the infectious period. This allows us to understand virulence evolution in a broader range of situations that characterize many emerging diseases. Because emerging pathogens are by definition new to their host populations, they should be expected to rapidly adapt after emergence. We illustrate these evolutionary effects using the framework of adaptive dynamics to examine how virulence evolves after emergence in response to the relative strength of selection on pathogen fitness and mutational variance for virulence. We illustrate the role of evolution by simulating adaptive walks to an evolutionarily stable virulence. We found that the magnitude of between-species transmission and the relative timing of transmission and mortality across species were of primary importance for determining the evolutionarily stable virulence.     

基于MODIS-EVI数据和Symlet11小波识别东北地区水稻主要物候期

作物物候信号能够反映温度和降水等变化对植被生长的影响,是进行农作物动态分析和田间管理的重要依据。基于2008年EOS-MODIS多时相卫星遥感数据,研究了我国东北地区水稻的主要物候期的识别方法。首先提取研究区24个农业气象观测站所在位置的MODIS-EVI(Enhanced Vegetation Index,增强型植被指数)指数的时间序列;同时利用小波滤波消除时间序列上的噪音,小波滤波选用函数包含Daubechies(7-20),Coiflet(3-5)和Symlet(7-15)共26种类型。然后根据水稻移栽期、抽穗期和成熟期在EVI时间序列上的表现特征来识别水稻主要物候期。最后与东北地区24个站点水稻物候观测资料对比并分析误差。结果表明,Symlet11小波滤波的效果最好,其移栽期识别结果的误差绝大部分在±16 d,抽穗期和成熟期识别结果的误差在±8 d。表明通过此方法可以较好地识别东北水稻主要物候期,并可进一步应用到整个东北地区水稻的物候空间分布和时间变化特征研究上。     

NDVI is not reliable as a surrogate of forage abundance for a large herbivore in tropical forest habitat

Remotely sensed vegetation indices are increasingly being used in wildlife studies but field‐based support for their utility as a measure of forage availability comes largely from open‐canopy habitats. We assessed whether normalized difference vegetation index (NDVI) represents forage availability for Asian elephants in a southern Indian tropical forest. We found that the number of food species was a small percentage of all plant species. NDVI was not a good measure of food abundance in any vegetation category partly because of (a) small to moderate proportional abundances of food species relative to the total abundance of all species in that category (herbs and shrubs), (b) abundant overstory vegetation resulting in low correlations between NDVI and food abundance, despite a high proportional abundance of food species and a concordance between total abundance and food species abundance (graminoids), and (c) the relevant variables measured and important as food at the ground level (count and GBH) not being related to primary productivity (trees and recruits). NDVI had a negative relationship with the total abundance of graminoids, which represent a bulk of elephant and other herbivore diet, because of negative interaction with other vegetation and canopy cover that positively explained NDVI. Spatially interpolated total graminoid abundance modeled from field data outperformed NDVI in predicting total graminoid abundance, although interpolation models of food graminoid abundance were not satisfactory. Our results reject the utility of NDVI in mapping elephant forage abundance in tropical forests, a finding that has implications for studies of other herbivores also. Abstract in Kannada is available with online material.     

Divergence in Forest-Type Response to Climate and Weather: Evidence for Regional Links Between Forest-Type Evenness and Net Primary Productivity

Climate change is altering long-term climatic conditions and increasing the magnitude of weather fluctuations. Assessing the consequences of these changes for terrestrial ecosystems requires understanding how different vegetation types respond to climate and weather. This study examined 20 years of regional-scale remotely sensed net primary productivity (NPP) in forests of the northern Lake States to identify how the relationship between NPP and climate or weather differ among forest types, and if NPP patterns are influenced by landscape-scale evenness of forest-type abundance. These results underscore the positive relationship between temperature and NPP. Importantly, these results indicate significant differences among broadly defined forest types in response to both climate and weather. Essentially all weather variables that were strongly related to annual NPP displayed significant differences among forest types, suggesting complementarity in response to environmental fluctuations. In addition, this study found that forest-type evenness (within 8 × 8 km² areas) is positively related to long-term NPP mean and negatively related to NPP variability, suggesting that NPP in pixels with greater forest-type evenness is both higher and more stable through time. This is landscape- to subcontinental-scale evidence of a relationship between primary productivity and one measure of biological diversity. These results imply that anthropogenic or natural processes that influence the proportional abundance of forest types within landscapes may influence long-term productivity patterns.     

基于数字相机、ASTER和MODIS影像综合测量植被盖度

选择我国北方温带典型草原作为研究对象,基于Bottom-up方法,采用地表实测和多尺度遥感综合测量的方法,建立基于地表实测与多尺度遥感数据综合测量的两阶段植被盖度经验模型。此外,还将该模型与常用的亚像元分解模型相比较,结果表明:1)两阶段经验模型可以较好地实现将地面数据扩展到中尺度空间范围,从而完成数据空间尺度的转换,提高大区域草地植被盖度的测量精度;2)MODIS遥感影像数据,结合地面数据和ASTER遥感影像数据可以较好地在区域范围内对北方典型草原的植被盖度进行估测;3)目前常用的亚像元分解模型,应用于中空间分辨率的MODIS影像,估测北方温度典型草原植被盖度的精度不够理想。