CiteULike: neteler's ecology

Wildlife as source of zoonotic infections.

H Kruse — 2008-05-09T20:47:13-00:00

Emerging infectious diseases, Vol. 10, No. 12. (December 2004), pp. 2067-2072.

Zoonoses with a wildlife reservoir represent a major public health problem, affecting all continents. Hundreds of pathogens and many different transmission modes are involved, and many factors influence the epidemiology of the various zoonoses. The importance and recognition of wildlife as a reservoir of zoonoses are increasing. Cost-effective prevention and control of these zoonoses necessitate an interdisciplinary and holistic approach and international cooperation. Surveillance, laboratory capability, research, training and education, and communication are key elements.

Environmental conditions and Puumala virus transmission in Belgium

Catherine Linard — 2007-12-14T10:49:42-00:00

International Journal of Health Geographics, Vol. 6 (14 December 2007), 55.

Climate and land use changes, biodiversity and agri-environmental measures in the Belluno province, Italy

Carlo Giupponi — 2008-01-17T08:55:27-00:00

Environmental Science & Policy, Vol. 9, No. 2. (April 2006), pp. 163-173.

This paper presents a synthesis of the results of the ACCELERATES project (Assessing Climate Change Effects on Land Use and Ecosystems from Regional Analysis to the European Scale), obtained in the case study of the Belluno province (north-east Italy), a context chosen as representative of the Alpine area. Selected results of the analysis of the relationships between future scenarios of change, farming systems, land use and biodiversity are presented. An initial historical analysis of the dynamics of land use with respect to the agricultural, socio-economic and demographic dynamics identified the main drivers of change and the positive and negative factors for conservation of the rural land and of biodiversity. In a subsequent stage the scenarios of future climate and land use changes were used to analyse the future for the species selected as indicators of biodiversity in the studied area. The results obtained provided useful information for the identification of suitable agri-environmental policies at the local scale. Maintenance of the livestock production systems typical of mountain agriculture is shown to be the key factor for contrasting land abandonment and the consequent expansion of woodlands, with negative effects in terms of simplification of landscape and impacts on species of naturalistic interest.

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

JP Scharlemann — 2008-01-16T16:23:08-00:00

PLoS ONE, Vol. 3, No. 1. (2008)

BACKGROUND: Remotely-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 FINDINGS: We 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/SIGNIFICANCE: Global 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.

Habitat Factors Associated with Bank Voles (Clethrionomys glareolus) and Concomitant Hantavirus in Northern Sweden

Gert Olsson — 2008-01-16T14:12:48-00:00

Vector-Borne and Zoonotic Diseases, Vol. 5, No. 4. (2005), pp. 315-323.

Puumala virus (PUUV), genus hantavirus, causes nephropathia epidemica, a mild form of hemorrhagic fever with renal syndrome in humans. In this study, bank voles, the natural reservoir of PUUV, were captured at locations of previous human PUUV exposure and paired controls within a region of high incidence in northern Sweden. The aim of the study was to evaluate the influence of environmental factors on the abundance of bank voles and the occurrence of PUUV. The total number of voles and the number of PUUV-infected voles did not differ between locations of previous human PUUV exposure and paired controls. The number of bank voles expressing antibodies to PUUV infection increased linearly with total bank vole abundance implying density independent transmission. Using principal component and partial correlation analysis, we found that particular environmental characteristics associated with old-growth moist forests (i.e., those dominated by Alectoria spp., Picea abies, fallen wood, and Vaccinium myrtillus) were also associated with increased abundance of bank vole and hence the number of PUUV-infected bank voles, whereas there were no correlations with factors associated with dry environments (i.e., Pinus sylvestris and V. vitis-idea). This suggests that circulation and persistence of PUUV within bank vole populations was influenced by habitat factors. Future modeling of risk of exposure to hantavirus and transmission of PUUV within vole populations should include the influence of these factors. Vector-Borne Zoonotic Dis. 5, 315-323.

Wildlife, environment and (re)-emerging zoonoses, with special reference to sylvatic tick-borne zoonoses in North-Western Italy.

D De Meneghi — 2008-01-11T09:34:43-00:00

Ann Ist Super Sanita, Vol. 42, No. 4. (2006), pp. 405-409.

Over the last century, changes in land-use, modification of agriculture-livestock production systems, disruption of wildlife habitats, increase of human activities, higher frequency of international and intercontinental travels, wider circulation of animals and animal products have contributed to alter the distribution, presence and density of hosts and vectors. As a result, the number of emerging and reemerging diseases, including zoonoses, have greatly increased. Some infectious pathogens, originated in wild animals and/or maintained in sylvatic environments, have become increasingly important worldwide for their impact on wildlife, human health, livestock and agricultural production systems. In this paper, a synthesis of the information available on selected zoonoses of wildlife origin is given, with special reference to sylvatic tick-borne zoonoses in North-western Italy.

LAND USE CHANGE AND BIODIVERSITY CONSERVATION IN THE ALPS

C Chemini — 2007-12-18T09:53:55-00:00

Journal of Mountain Ecology, Vol. 7 (2003), pp. 1-7.

Human activities are changing the Planet, inducing high rates of extinction and a worldwide depletion of biological diversity at genetic, species, and ecosystem level. Biodiversity not only has an ethical and cultural value, but also plays a role in ecosystem function and, thus, ecosystem services, which are essential to civilization, economic production, and human wellbeing. The functional role of biodiversity is still poorly known; a minimum level of biodiversity is required for sustainable preservation of ecosystem functions, and as an insurance for future environmental changes. A large part of the biodiversity of the Alps is linked to an interaction between the natural environment and traditional human practices. At present, the change in land-use, with both intensification and abandonment, and other environmental and socioeconomic processes at different scales (urbanization, tourism, pol- lution, global change, etc.) are important forces of environmental change. Mowing and livestock grazing are prima- ry factors inhibiting woody plant succession in many areas of the Alps. Abandonment and fragmentation has result- ed in an expansion of ecotones and edge, with increase in tick-hosts and possibly changes in host-parasite interac- tions resulting from species concentration. The abandonment of mountain fields and meadows with a consequent expansion of shrubs and forests has caused a decrease of several grassland species, such as rock partridge Alectoris graeca; some arthropod communities of grassland have also been affected. Many forest species should find new opportunities, but in several cases the forests have become too dense for some species, such as for capercaille Tetrao urogallus. In the low altitude belts, a high species diversity co-occurs with human disturbance. Biodiversity studies require an interdisciplinary approach by the life sciences, and an interface to socioeconomic sciences. Preservation of species and landscape diversity cannot prescind from a dialogue between different actors and interests.

Fundamental processes in the evolutionary ecology of Lyme borreliosis

Klaus Kurtenbach — 2006-08-19T00:40:25-00:00

Nature Reviews Microbiology, Vol. 4, No. 9. (07 August 2006), pp. 660-669.

The ecology of ticks transmitting Lyme borreliosis

JS Gray — 2008-01-02T14:34:44-00:00

Experimental and Applied Acarology, Vol. 22, No. 5. (May 1998), pp. 249-258.

The main vectors of Borrelia burgdorferi sensu lato, the cause of Lyme borreliosis, are ixodid ticks of the Ixodes persulcatus species complex. These ticks, which occur throughout the northern temperate zone, have very similar life cycles and ecological requirements. All are three-host ticks, with the immature stages mainly parasitizing small to medium-sized mammals and birds and the adult females parasitizing large mammals such as deer, cattle, sheep and hares. The host-seeking stages show a distinct seasonality, which is regulated by diapause mechanisms and there appear to be major differences in this respect between the Old World and New World species. Most cases of human borreliosis are transmitted in the summer by the nymphal stages, with the exception of the Eurasian species, I. persulcatus, in which the adult females are mainly responsible. The ticks acquire the spirochaetes from a wide variety of mammals and birds but large mammals do not seem to be infective, so that ticks that feed almost exclusively on large mammals, for example in some agricultural habitats, are rarely infected. The greatest tick infection prevalences occur in deciduous woodland harbouring a diverse mix of host species and the diversity of the different genospecies of B. burgdorferi s.l. is also greatest in such habitats. There is evidence that these genospecies have different host predilections but, apart from the fact that I. persulcatus does not seem to be infected by B. burgdorferi sensu stricto, they do not seem to be adapted to different tick strains or species.

Landscape genetics: combining landscape ecology and population genetics

Stephanie Manel — 2007-10-09T09:54:02-00:00

Trends in Ecology & Evolution, Vol. 18, No. 4. (April 2003), pp. 189-197.

Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population geneticists have investigated how physiognomy and other landscape features have influenced genetic variation within and between populations. They have relied on sampling populations that have been identified beforehand because most population genetics methods have required discrete populations. However, a new approach has emerged for analyzing spatial genetic data without requiring that discrete populations be identified in advance. This approach, landscape genetics, promises to facilitate our understanding of how geographical and environmental features structure genetic variation at both the population and individual levels, and has implications for ecology, evolution and conservation biology. It differs from other genetic approaches, such as phylogeography, in that it tends to focus on processes at finer spatial and temporal scales. Here, we discuss, from a population genetic perspective, the current tools available for conducting studies of landscape genetics.

A Spatial View of the Ecological Inference Problem

Carol Gotway — 2007-10-01T08:57:06-00:00

(2004), pp. 233-244.

Ecological Inference: New Methodological Strategies (Analytical Methods for Social Research)

Gary King — 2007-10-01T08:46:25-00:00

(13 September 2004)

This collection of essays brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half-decade has witnessed an explosion of research in ecological inference--the process of trying to infer individual behavior from aggregate data. Although uncertainties and information lost in aggregation make ecological inference one of the most problematic types of research to rely on, these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, by business in marketing research, and by governments in policy analysis.

Spatial aspects of disease dynamics

G Hess — 2007-01-24T17:34:47-00:00

(2002), pp. 102-118.

Tree-based methods

John Bell — 2006-08-22T15:44:24-00:00

(August 1999), pp. 89-106.

... see Fielding1999_machine_learning ...

Machine Learning Methods for Ecological Applications

Alan Fielding — 2006-08-22T14:32:38-00:00

(31 August 1999), pp. 1-36.

The last 25 years have seen a tremendous growth in the application of statistical and modelling techniques to ecological problems. This expansion has been accelerated by the increasing availability of software, books and computing power. However, the suitability of some of these approaches to data analysis, in a relatively knowledge-poor discipline such as ecology, can be questioned on grounds of appropriateness and robustness. One reason for these concerns is that many ecological problems are at best poorly defined and most lack algorithmic solutions. Machine learning methods offer the potential for a different approach to these difficult problems. One definition of machine learning is that it is concerned with inducing knowledge from data, where the data could be patterns in a game of chess or patterns in the species composition of natural communities. Unfortunately ecologists have little experience of these relatively recent and novel approaches to understanding data. This is a problem that is made more complex because there is no simple taxonomy of machine learning methods and there are relatively few examples in the mainstream ecological literature to encourage exploration. This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem. Examples include the identification of species, optimal mate choice, predicting species distributions and modelling landscape features. A group of experienced machine learning workers, who have become interested in environmental problems, have written a chapter that demonstrates how machine learning methods can be used to discover equations that describe the dynamic behaviour of ecological systems. The final chapter reviews `real learning', offering the potential for greater dialogue between the biological and machine learning communities.

Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula

Marta Garzon — 2006-05-01T09:45:24-00:00

Ecological Modelling, Vol. 197, No. 3-4. (August 2006), pp. 383-393.

We present a modelling framework for predicting forest areas. The framework is obtained by integrating a machine learning software suite within the GRASS Geographical Information System (GIS) and by providing additional methods for predictive habitat modelling. Three machine learning techniques (Tree-Based Classification, Neural Networks and Random Forest) are available in parallel for modelling from climatic and topographic variables. Model evaluation and parameter selection are measured by sensitivity-specificity ROC analysis, while the final presence and absence maps are obtained through maximisation of the kappa statistic. The modelling framework is applied at a resolution of 1 km with Iberian subpopulations of Pinus sylvestris L. forests. For this data set, the most accurate algorithm is Breiman's random forest, an ensemble method which provides automatic combination of tree-classifiers trained on bootstrapped subsamples and randomised variable sets. All models show a potential area of P. sylvestris for the Iberian Peninsula which is larger than the present one, a result corroborated by regional pollen analyses.

Indicator bacteria at five swimming beaches-analysis using random forests.

DF Parkhurst — 2006-08-03T16:57:00-00:00

Water Res, Vol. 39, No. 7. (April 2005), pp. 1354-1360.

"Random forests," an extension of tree regression, provide a relatively new technique for exploring relationships of a response variable like the density of indicator bacteria in water to numerous potential explanatory variables. We used this tool to study relationships of indicator density at five beaches to numerous other variables and found that day of the week, indicator density 24h earlier, water depth at the sampling point, cloud cover, and others were related to density at one or more of the beaches. Using data from the first 52 days of measurement allowed predicting indicator densities in the following 10 days to order of magnitude at some of the beaches. Our analyses served to demonstrate the potential usefulness of this analytic tool for large data sets with many variables.

Comparison of statistical methods commonly used in predictive modelling

J Muñoz — 2006-08-02T16:24:50-00:00

Journal of Vegetation Science, Vol. 15, No. 2. (2004), pp. 285-292.

Logistic Multiple Regression, Principal Component Regression and Classification and Regression Tree Analysis (CART), commonly used in ecological modelling using GIS, are compared with a relatively new statistical technique, Multivariate Adaptive Regression Splines (MARS), to test their accuracy, reliability, implementation within GIS and ease of use. All were applied to the same two data sets, covering a wide range of conditions common in predictive modelling, namely geographical range, scale, nature of the predictors and sampling method. We ran two series of analyses to verify if model validation by an independent data set was required or cross-validation on a learning data set sufficed. Results show that validation by independent data sets is needed. Model accuracy was evaluated using the area under Receiver Operating Characteristics curve (AUC). This measure was used because it summarizes performance across all possible thresholds, and is independent of balance between classes. MARS and Regression Tree Analysis achieved the best prediction success, although the CART model was difficult to use for cartographic purposes due to the high model complexity.

Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

Anantha Prasad — 2006-08-02T13:14:50-00:00

Ecosystems, Vol. 9, No. 2. (March 2006), pp. 181-199.

The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built in to select the most appropriate values for input model parameters for each species so that minimal effort is required when these parameters are fine-tuned for individual tree species. We evaluated four statistical models—Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS)—for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model. To test, we applied these techniques to four tree species common in the eastern United States: loblolly pine (Pinus taeda), sugar maple (Acer saccharum), American beech (Fagus grandifolia), and white oak (Quercus alba). When the four techniques were assessed with Kappa and fuzzy Kappa statistics, RF and BT were superior in reproducing current importance value (a measure of basal area in addition to abundance) distributions for the four tree species, as derived from approximately 100,000 USDA Forest Service’s Forest Inventory and Analysis plots. Future estimates of suitable habitat after climate change were visually more reasonable with BT and RF, with slightly better performance by RF as assessed by Kappa statistics, correlation estimates, and spatial distribution of importance values. Although RTA did not perform as well as BT and RF, it provided interpretive models for species whose distributions were captured well by our current set of predictors. MARS was adequate for predicting current distributions but unacceptable for future climate. We consider RTA, BT, and RF modeling approaches, especially when used together to take advantage of their individual strengths, to be robust for predictive mapping and recommend their inclusion in the ecological toolbox.

Remote Sensing and Geographical Information Systems in Epidemiology (Advances in Parasitology)

SI Hay — 2006-07-31T08:02:58-00:00

(15 September 2000)

Global problems require global information, which satellites can now provide. With ever more sophisticated control methods being developed for infectious diseases, our ability to map spatial and temporal variation in risk is more important than ever. Only then may we plan control campaigns and deliver novel interventions and remedies where the need is greatest, and sustainable success is most likely. This book presents a comprehensive guide to using the very latest methods of surveillance from satellites, including analysing spatial data within geographical information systems, interpreting complex biological patterns, and predicting risk both today and as it may change in the future. Of all infectious disease systems, those that involve free-living invertebrate vectors or intermediate hosts are most susceptible to changing environmental conditions, and have hitherto received most attention from the marriage of analytical biology with this new space technology. Accordingly, this volume presents detailed case studies on malaria, African trypanosomiasis (sleeping sickness), tick-borne infections and helminths (worms). For those who are unfamiliar with this science, and unsure how to start, the book ends with a chapter of practical advice on where to seek hands-on instruction. The lessons to be learned from these studies are applicable to many other epidemiological and ecological problems that face us today, most significantly the preservation of the world's biodiversity. Key Features * Only book to provide a synthesis of complex biology, quantitative analysis, space technology and practical applications, focused on solving real epidemiological problems on a global scale * Broad scope, with methods relevant to subjects ranging from biodiversity to public health * Practical advice on relevant courses * 24 pages of colour plates

Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands.

E Mushinzimana — 2006-07-28T15:08:06-00:00

Malar J, Vol. 5 (2006)

BACKGROUND: In the past two decades the east African highlands have experienced several major malaria epidemics. Currently there is a renewed interest in exploring the possibility of anopheline larval control through environmental management or larvicide as an additional means of reducing malaria transmission in Africa. This study examined the landscape determinants of anopheline mosquito larval habitats and usefulness of remote sensing in identifying these habitats in western Kenya highlands. METHODS: Panchromatic aerial photos, Ikonos and Landsat Thematic Mapper 7 satellite images were acquired for a study area in Kakamega, western Kenya. Supervised classification of land-use and land-cover and visual identification of aquatic habitats were conducted. Ground survey of all aquatic habitats was conducted in the dry and rainy seasons in 2003. All habitats positive for anopheline larvae were identified. The retrieved data from the remote sensors were compared to the ground results on aquatic habitats and land-use. The probability of finding aquatic habitats and habitats with Anopheles larvae were modelled based on the digital elevation model and land-use types. RESULTS: The misclassification rate of land-cover types was 10.8% based on Ikonos imagery, 22.6% for panchromatic aerial photos and 39.2% for Landsat TM 7 imagery. The Ikonos image identified 40.6% of aquatic habitats, aerial photos identified 10.6%, and Landsate TM 7 image identified 0%. Computer models based on topographic features and land-cover information obtained from the Ikonos image yielded a misclassification rate of 20.3-22.7% for aquatic habitats, and 18.1-25.1% for anopheline-positive larval habitats. CONCLUSION: One-metre spatial resolution Ikonos images combined with computer modelling based on topographic land-cover features are useful tools for identification of anopheline larval habitats, and they can be used to assist to malaria vector control in western Kenya highlands.

Modelling distribution and abundance with presence-only data

E Pearc — 2006-05-13T23:59:05-00:00

Journal of Applied Ecology, Vol. 43, No. 3. (June 2006), pp. 405-412.

A comparison of statistical approaches for modelling fish species distributions

Julian Olden — 2006-07-25T22:55:47-00:00

Freshwater Biology, Vol. 47, No. 10. (2002), pp. 1976-1995.

SUMMARY 1. The prediction of species distributions is of primary importance in ecology and conservation biology. Statistical models play an important role in this regard; however, researchers have little guidance when choosing between competing methodologies because few comparative studies have been conducted. 2. We provide a comprehensive comparison of traditional and alternative techniques for predicting species distributions using logistic regression analysis, linear discriminant analysis, classification trees and artificial neural networks to model: (1) the presence/absence of 27 fish species as a function of habitat conditions in 286 temperate lakes located in south-central Ontario, Canada and (2) simulated data sets exhibiting deterministic, linear and non-linear species response curves. 3. Detailed evaluation of model predictive power showed that approaches produced species models that differed in overall correct classification, specificity (i.e. ability to correctly predict species absence) and sensitivity (i.e. ability to correctly predict speciespresence) and in terms of which of the study lakes they correctly classified. Onaverage, neural networks outperformed the other modelling approaches, although all approaches predicted species presence/absence with moderate to excellent success. 4. Based on simulated non-linear data, classification trees and neural networks greatly outperformed traditional approaches, whereas all approaches exhibited similar correct classification rates when modelling simulated linear data. 5. Detailed evaluation of model explanatory insight showed that the relative importance of the habitat variables in the species models varied among the approaches, where habitat variable importance was similar among approaches for some species and very different for others. 6. In general, differences in predictive power (both correct classification rate and identity of the lakes correctly classified) among the approaches corresponded with differences in habitat variable importance, suggesting that non-linear modelling approaches (i.e. classification trees and neural networks) are better able to capture and model complex, non-linear patterns found in ecological data. The results from the comparisons using simulated data further support this notion. 7. By employing parallel modelling approaches with the same set of data and focusing on comparing multiple metrics of predictive performance, researchers can begin to choose predictive models that not only provide the greatest predictive power, but also best fit the proposed application.

Classification and Regression Trees: A Powerful Yet Simple Technique for Ecological Data Analysis

Glenn De'ath — 2006-07-25T22:48:05-00:00

Ecology, Vol. 81, No. 11. (November 2000), pp. 3178-3192.

Classification and regression trees are ideally suited for the analysis of complex ecological data. For such data, we require flexible and robust analytical methods, which can deal with nonlinear relationships, high-order interactions, and missing values. Despite such difficulties, the methods should be simple to understand and give easily interpretable results. Trees explain variation of a single response variable by repeatedly splitting the data into more homogeneous groups, using combinations of explanatory variables that may be categorical and/or numeric. Each group is characterized by a typical value of the response variable, the number of observations in the group, and the values of the explanatory variables that define it. The tree is represented graphically, and this aids exploration and understanding. Trees can be used for interactive exploration and for description and prediction of patterns and processes. Advantages of trees include: (1) the flexibility to handle a broad range of response types, including numeric, categorical, ratings, and survival data; (2) invariance to monotonic transformations of the explanatory variables; (3) ease and robustness of construction; (4) ease of interpretation; and (5) the ability to handle missing values in both response and explanatory variables. Thus, trees complement or represent an alternative to many traditional statistical techniques, including multiple regression, analysis of variance, logistic regression, log-linear models, linear discriminant analysis, and survival models. We use classification and regression trees to analyze survey data from the Australian central Great Barrier Reef, comprising abundances of soft coral taxa (Cnidaria: Octocorallia) and physical and spatial environmental information. Regression tree analyses showed that dense aggregations, typically formed by three taxa, were restricted to distinct habitat types, each of which was defined by combinations of 3-4 environmental variables. The habitat definitions were consistent with known experimental findings on the nutrition of these taxa. When used separately, physical and spatial variables were similarly strong predictors of abundances and lost little in comparison with their joint use. The spatial variables are thus effective surrogates for the physical variables in this extensive reef complex, where information on the physical environment is often not available. Finally, we compare the use of regression trees and linear models for the analysis of these data and show how linear models fail to find patterns uncovered by the trees.

Classification Tree Methods for Analysis of Mesoscale Distribution of Ixodes ricinus (Acari: Ixodidae) in Trentino, Italian Alps

S Merler — 2006-07-25T22:42:28-00:00

Journal of Medical Entomology, Vol. 33, No. 6. (June 1996), pp. 888-893.

Cases of Lyme disease and tick borne encephalitis were recently recognized in the province of Trento, Italian Alps. Assessment of areas of potential risk for these tick-borne diseases is carried out by a model based on CART (Classification and Regression Trees), using both discrete and continuous variables. Data on Ixodes ricinus (L.) occurrence resulted from samplings carried out by standard methods in 99 sites over an area of 2,700 km2 in the Province of Trento. A series of environmental parameters were recorded from each site and population densities of roe deer, Capreolus capreolus (L.), were considered. The CART model discriminates two variables which appear to have the greatest effect on the mesoscale occurrence of ticks: altitude and geological substratum with drastic decrease of tick frequency above 1,100 m a.s.l. or on volcanic substrata. The model is effective in identifying the mesoscale areas at greater potential risk, with a relatively low sampling effort.

Comparing models for predicting species' potential distributions: a case study using correlative and mechanistic predictive modelling techniques

MP Robertson — 2006-07-25T16:51:13-00:00

Ecological Modelling, Vol. 164, No. 2. (June 2003), pp. 153-167.

Models used to predict species' potential distributions have been described as either correlative or mechanistic. We attempted to determine whether correlative models could perform as well as mechanistic models for predicting species potential distributions, using a case study. We compared potential distribution predictions made for a coastal dune plant (Scaevola plumieri) along the coast of South Africa, using a mechanistic model based on summer water balance (SWB), and two correlative models (a profile and a group discrimination technique). The profile technique was based on principal components analysis (PCA) and the group-discrimination technique was based on multiple logistic regression (LR). Kappa () statistics were used to objectively assess model performance and model agreement. Model performance was calculated by measuring the levels of agreement (using ) between a set of testing localities (distribution records not used for model building) and each of the model predictions. Using published interpretive guidelines for the kappa statistic, model performance was ”excellent” for the SWB model (=0.852), perfect for the LR model (=1.000), and ”very good” for the PCA model (=0.721). Model agreement was calculated by measuring the level of agreement between the mechanistic model and the two correlative models. There was ”good” model agreement between the SWB and PCA models (=0.679) and ”very good” agreement between the SWB and LR models (=0.786). The results suggest that correlative models can perform as well as or better than simple mechanistic models. The predictions generated from these three modelling designs are likely to generate different insights into the potential distribution and biology of the target organism and may be appropriate in different situations. The choice of model is likely to be influenced by the aims of the study, the biology of the target organism, the level of knowledge the target organism's biology, and data quality.

Modelling ecological niches with support vector machines

E Drak — 2006-05-13T23:59:05-00:00

Journal of Applied Ecology, Vol. 43, No. 3. (June 2006), pp. 424-432.

Longitudinal surveillance of the tick Ixodes ricinus for borreliae.

Z Hubálek — 2006-07-23T15:43:51-00:00

Med Vet Entomol, Vol. 17, No. 1. (March 2003), pp. 46-51.

Host-seeking Ixodes ricinus (L.) (Acari: Ixodidae) were monitored for borreliae (Borrelia burgdorferi s.l.) using dark-field microscopy in South Moravia (Czech Republic) each May from 1991 to 2001 (150 nymphs, 100 females and 100 males each year). This survey revealed a mean annual percentage of infected ticks of 16.8% (range, 11.7-24.2) in nymphs, 24.9% (range, 16.5-33.6) in females and 26.1% (range, 17.1-37.3) in males. Annual incidence of Lyme borreliosis in humans of the area in the same period (range, 8.7-41.7 per 100,000) correlated significantly with the frequency (number of ticks per flag per hour) of nymphs infected with >50 borreliae or all nymphal ticks, but not with the frequency of females, infected females or the infection rate (% of ticks infected) of either nymphal or female ticks. A prediction of the annual incidence of Lyme borreliosis, based on the frequency of heavily infected or all nymphal I. ricinus ticks, is feasible. The infection rate in I. ricinus correlated significantly with the North Atlantic Oscillation winter index of the last year (in nymphs) or of the year before last (in adults).

Populations of Ixodes scapularis (Acari: Ixodidae) are modulated by drought at a Lyme disease focus in Illinois.

CJ Jones — 2006-07-23T09:30:54-00:00

J Med Entomol, Vol. 37, No. 3. (May 2000), pp. 408-415.

From 1990 through 1997, Ixodes scapularis Say larvae and nymphs were sampled between May and October along a 400-m segment of a nature trail in a Lyme disease endemic site in northern Illinois. Ticks were removed from Peromyscus leucopus mice and collected via tick drags at approximately 3-wk intervals. Mouse population estimates along the trail varied from 2, in the spring of 1996 following a year of drought, to > 200 in 1993, the wettest year on record. During the 8-yr period, there were major droughts during the summers of 1991 and 1995. Cumulative degree-days were positively correlated with the number of ticks collected on drags in the same year and negatively correlated with larval tick populations for the following year (P < 0.05). Cumulative rainfall was positively correlated with larval tick abundance for the following year. This was most readily apparent by examination of the larval density on captured mice. In the year following each of two drought years, larval densities were significantly depressed compared with the 8-yr average at the site.

Satellite-based mapping of the growing season and bioclimatic zones in Fennoscandia

Stein Karlsen — 2006-07-03T15:23:13-00:00

Global Ecology & Biogeography, Vol. 15, No. 4. (July 2006), pp. 416-430.

Aim To test whether satellite-derived NDVI values obtained during the growing season as delimited by the onset of phenological phases can be used to map bioclimatically a large region such as Fennoscandia. Location Fennoscandia north of about 58° N and neighbouring parts of NW Russia. Methods Phenology data on birch from 15 research stations and the half-monthly GIMMS-NDVI data set with 8 × 8 km2 resolution from the period 1982–2002 were used to characterize the growing season. To link surface phenology with NDVI data, new algorithms on a pixel-by-pixel basis that show high correlation with phenophases on birch were developed. Then, time-integrated values (TI NDVI) during the phenologically defined growing season were computed to produce a bioclimatological map of Fennoscandia, which was tested and correlated with growing degree days (GDD) obtained from 20 meteorological stations. The map was also compared vs. traditional bioclimatic maps, and analysed for error factors distorting NDVI values. Results The correlation between GDD and TI NDVI data during the phenologically defined growing season was very high. Therefore, the TI NDVI map could be presented as a bioclimatic map reflecting GDD. However, several major areas have interfering factors distorting NDVI values, such as the pixel heterogeneity caused by the altitudinal mosaic in western Norway, the mosaic of lakes in southeastern Finland, and the agriculture-dominated areas in southern Fennoscandia. Main conclusions TI NDVI data from the phenologically defined growing season during 1982–2002 in Fennoscandia can be processed as a bioclimatic map reflecting GDD, except for the areas distorting NDVI values by their strong ground-cover heterogeneity.

Biology of Ticks: Volume 2

Daniel Sonenshine — 2006-07-21T10:04:26-00:00

(01 October 1993)

This is the second of a two-volume work on the biology, morphology, ecology, disease relationships, and control of ticks. Volume 2 explores survival strategies of non-nidicolous ticks (those dispersed throughout the open landscape and attacking passing hosts) versus nidicolous ticks (those surviving in caves, burrows, nests, or man-made shelters). It also examines immunological responses to tick parasitism, the role of ticks in disease transmission, and the control of ticks through acaricides and recent innovative approaches using knowledge of tick and host ecology, tick pheromones, hormones, and modelling. An appendix is also included, with details on methods for collecting ticks in the natural environment, preparing ticks for study, and laboratory rearing. This book is a worthy complement to the first volume's outstanding achievement, and will be of interest to entomologists, physicians, veterinarians, and public health officers.

Climate, Deer, Rodents, and Acorns as Determinants of Variation in Lyme-Disease Risk

Richard Ostfeld — 2006-07-20T17:58:32-00:00

PLoS Biology, Vol. 4, No. 6. (1 June 2006)

Risk of human exposure to vector-borne zoonotic pathogens is a function of the abundance and infection prevalence of vectors. We assessed the determinants of Lyme-disease risk (density and Borrelia burgdorferi-infection prevalence of nymphal Ixodes scapularis ticks) over 13 y on several field plots within eastern deciduous forests in the epicenter of US Lyme disease (Dutchess County, New York). We used a model comparison approach to simultaneously test the importance of ambient growing-season temperature, precipitation, two indices of deer (Odocoileus virginianus) abundance, and densities of white-footed mice (Peromyscus leucopus), eastern chipmunks (Tamias striatus), and acorns (Quercus spp.), in both simple and multiple regression models, in predicting entomological risk. Indices of deer abundance had no predictive power, and precipitation in the current year and temperature in the prior year had only weak effects on entomological risk. The strongest predictors of a current year's risk were the prior year's abundance of mice and chipmunks and abundance of acorns 2 y previously. In no case did inclusion of deer or climate variables improve the predictive power of models based on rodents, acorns, or both. We conclude that interannual variation in entomological risk of exposure to Lyme disease is correlated positively with prior abundance of key hosts for the immature stages of the tick vector and with critical food resources for those hosts.

Climatic factors controlling reproduction and growth of Norway spruce in southern Norway

V Selas — 2006-07-20T08:36:20-00:00

Canadian Journal of Forest Research, Vol. 32, No. 2. (February 2002), pp. 217-225.

Time series of seed production and tree-ring width of Norway spruce (Picea abies (L.) Karst.) in southern Norway were analysed for their relationship to various climatic factors occurring during "key" stages, which a priori might be expected to show a strong climate response. The following factors combined in a multiple linear regression model were found to predict seed production (based on withheld data points) with considerable accuracy, at high levels of statistical significance: June–July mean temperature and August lowest temperature in the previous year, late spring frost and June–July precipitation of the last 2 years, and January–February lowest temperature in the current year. Tree ring width was negatively correlated with the seed production index of the current year and the lowest July temperature in the previous year and positively correlated with June-July precipitation in the current year. It is suggested that habitat constraints for seedling establishment should also be considered in a more general life-history cost theory to explain masting behaviour in forest trees.

The evolutionary ecology of masting: does the environmental prediction hypothesis also have a role in mesic temperate forests?

G Piovesan — 2006-07-20T08:19:10-00:00

Ecological Research, Vol. 20 (November 2005), pp. 739-743.

The evolutionary advantages of mast seeding in mesic temperate forests are reviewed with reference to the whole plantrsquos lifecycle. The aim of this article is to give attention to the environmental prediction hypothesis as an evolutionary aspect of closed-forest dynamics that need to be tested in field studies and modelling. It is suggested that the year after a period of water stress (or other suboptimal conditions for growth) trees respond with high seed production. Due to an understory environment favorable for prolific establishment of seedlings (i.e., more light at the forest floor) this may give rise to a pulse of regeneration. Thus, understanding masting may require a multi-faceted approach including the study of the ecology of the trees themselves beyond pollination and seed predation, and including gap ecology and patch dynamics with special attention to patterns of forest regeneration.

Mast seeding in perennial plants: Why, How, Where?

Dave Kelly — 2006-07-18T12:40:48-00:00

Annual Review of Ecology and Systematics, Vol. 33, No. 1. (January 2002), pp. 427-447.

For many years biologists have debated whether mast seeding (the synchronous intermittent production of large seed crops in perennial plants) results from weather conditions or is an evolved plant reproductive strategy. In this review, we analyze the evidence for the underlying causes of masting. In the absence of selection for higher or lower variability, plants will vary in tandem with the environment (resource matching). Two selective factors often favor the evolution of masting: increased pollination efficiency in wind-pollinated species, and satiation of seed predators. Other factors select against masting, including animal pollination and frugivore dispersal. A survey of 570 masting datasets shows that wind-pollinated species had higher seed production coefficients of variation (CVs) than biotically pollinated ones. Frugivore-dispersed species had low CVs whereas predator-dispersed plants had high CVs, consistent with gaining benefits from predator satiation rather than dispersal. The global pattern of masting shows highest seed crop variability at mid latitudes and in the Southern Hemisphere, which are similar to the patterns in variability of rainfall. We conclude that masting is often an adaptive reproductive trait overlaid on the direct influence of weather.

The ecology of tick-borne infections in wildlife reservoirs

SE Randolph — 2006-04-02T21:57:25-00:00

(2002), pp. 119-138.

Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis.

U Kitron — 2006-03-28T13:57:08-00:00

J Med Entomol, Vol. 35, No. 4. (July 1998), pp. 435-445.

Geographic information systems (GIS), global positioning systems (GPS), remote sensing, and spatial statistics are tools to analyze and integrate the spatial component in epidemiology of vector-borne disease into research, surveillance, and control programs based on a landscape ecology approach. Landscape ecology, which deals with the mosaic structure of landscapes and ecosystems, considers the spatial heterogeneity of biotic and abiotic components as the underlying mechanism which determines the structure of ecosystems. The methodologies of GIS, GPS, satellite imagery, and spatial statistics, and the landscape ecology--epidemiology approach are described, and applications of these methodologies to vector-borne diseases are reviewed. Collaborative studies by the author and colleagues on malaria in Israel and tsetse flies in Kenya, and Lyme disease, LaCrosse encephalitis, and eastern equine encephalitis in the north-central United States are presented as examples for application of these tools to research and disease surveillance. Relevance of spatial tools and landscape ecology to emerging infectious diseases and to studies of global change effects on vector-borne diseases are discussed.

The Ecology of Lyme-Disease Risk: Complex interactions between seemingly unconnected phenomena determine risk of exposure to this expanding disease

Richard Ostfeld — 2006-03-28T13:49:10-00:00

American Scientist, Vol. 85 (July 1997)

Masting behaviour in beech: linking reproduction and climatic variation

Gianluca Piovesan — 2006-03-25T21:26:24-00:00

Canadian Journal of Botany, Vol. 79, No. 9. (September 2001), pp. 1039-1047.

The question of what triggers masting in beech (Fagus) has been a source of uncertainty and curiosity. Analysing seed production series from Europe (Fagus sylvatica L.), eastern North America (Fagus grandifolia Ehrh.), and Japan (Fagus crenata Blume), for various periods (lasting between 6 and 34 years) over the last 150 years, we find a close relationship between masting (mast year) and preceding growing season climate events (mast year–1 and mast year–2) in eastern North America and Europe, with tentative indications of this pattern in Japan. A drought in the early summer preceding masting (mast year–1) is a very strong predictor in Europe and eastern North America, but drought events were not found for the Japan series. The predictive power is increased in all three regions if there has been an unusually moist, cool summer the year before the drought (mast year–2). We suggest that, in this initial moist summer (mast year–2), carbohydrate buildup within the trees "primes" them for floral induction the following year (year–1). In the European and eastern North American series, a drought event in the early part of the following summer (mast year–1) acts as a proximal trigger for the release of those reserves into flower initiation and then seed production. Key words: masting, Fagus spp., floral induction, drought, climatic variation, evolutionary ecology.

Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)

Rick Lawrence — 2006-01-29T15:56:06-00:00

Remote Sensing of Environment, Vol. 100, No. 3. (15 February 2006), pp. 356-362.

Invasive nonindigenous plants are threatening the biological integrity of North American rangelands, as well as the economies that are supported by those ecosystems. Spatial information is critical to fulfilling invasive plant management strategies. Traditional invasive plant mapping has utilized ground-based hand or GPS mapping. The shortfalls of ground-based methods include the limited spatial extent covered and the associated time and cost. Mapping vegetation with remote sensing covers large spatial areas and maps can be updated at an interval determined by management needs. The objective of the study was to map leafy spurge (Euphorbia esula L.) and spotted knapweed (Centaurea maculosa Lam.) using 128-band hyperspectral (5-m and 3-m resolution) imagery and assess the accuracy of the resulting maps. Beiman Cutler classifications (BCC) were used to classify the imagery using the randomForest package in the R statistical program. BCC builds multiple classification trees by repeatedly taking random subsets of the observational data and using random subsets of the spectral bands to determine each split in the classification trees. The resulting classification trees vote on the correct classification. Overall accuracy was 84% for the spotted knapweed classification, with class accuracies ranging from 60% to 93%; overall accuracy was 86% for the leafy spurge classification, with class accuracies ranging from 66% to 93%. Our results indicate that (1) BCC can achieve substantial improvements in accuracy over single classification trees with these data and (2) it might be unnecessary to have separate accuracy assessment data when using BCC, as the algorithm provides a reliable internal estimate of accuracy.

Applications of machine learning to ecological modelling

F Recknagel — 2006-01-25T18:44:12-00:00

Ecological Modelling, Vol. 146 (2001), pp. 303-310.

The paper provides a summary of paper presentations at the 2nd International Conference on Applications of Machine Learning to Ecological Modelling and a preview of forthcoming developments in this area. Artificial neural networks were demonstrated to be very useful for nonlinear ordination and visualization of ecological data by Kohonen networks, and ecological time-series modelling by recurrent networks. Genetic algorithms proved to be very innovative for hybridizing deductive models, and evolving predictive rules, process equations and parameters. Newly emerging adaptive agents provide a novel framework for the discovery and forecasting of emergent ecosystem structures and behaviours in response to environmental changes.

Habitat suitability modelling for red deer (Cervus elaphus L.) in South-central Slovenia with classification trees

Marko Debeljak — 2006-01-26T23:19:27-00:00

Ecological Modelling, Vol. 138, No. 1-3. (15 March 2001), pp. 321-330.

We study and assess the potential habitats of a population of red deer in South-central Slovenia. Using existing data on the deer population spatial distribution and size, as well as data on the landscape and ecological properties (GIS) of the area inhabited by this population, we develop a habitat suitability model by automated data analysis using machine learning of classification trees. We assume that the recorded observations of deer approximate the actual spatial distribution of the deer population reasonably well. The habitat suitability models for individual animals have the form of classification trees. The induced trees are interpreted by domain experts and a generic model is proposed. The generic habitat suitability models can help determine potential unoccupied habitats for the red deer population and develop guidelines for managing the development of the red deer population and its influence on the environment.

Epidemiology of Lyme Borreliosis

DT Dennis — 2005-11-07T16:41:19-00:00

(2002)

Ecology of Borrelia burgdorferi sensu lato in Europe

L Gern — 2005-11-07T16:33:43-00:00

(2002)

Ecological research on Borrelia burgdorferi sensu lato: Terminology and some methodological pitfalls

O Kahl — 2005-11-07T15:24:42-00:00

(2002)

Lyme Borreliosis: Biology, Epidemiology and Control

JS Gray — 2005-11-07T11:07:43-00:00

(04 October 2002)

Lyme borreliosis is now acknowledged as the most highly prevalent arthropod-borne human disease in northern temperate regions of the world. The majority of the publications it has generated concern clinical aspects, and a book dealing with the complex biology and ecology of the causative organisms in a worldwide context is overdue. This book describes the basic characteristics of the disease, the biology of the pathogens in their vectors (ticks) and vertebrate hosts, their ecology in different regions of the world and the global epidemiology of the diesease. The final chapters address the prevention and control measures that have resulted from this knowledge.

Using the satellite-derived NDVI to assess ecological responses to environmental change

Nathalie Pettorelli — 2005-09-06T15:02:28-00:00

Trends in Ecology & Evolution, Vol. 20, No. 9. (September 2005), pp. 503-510.

Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.

Ecology of Lyme disease: Habitat associations of ticks (Ixodes scapularis) in a rural landscape

Rs Ostfeld — 2005-08-05T13:50:13-00:00

Ecological Applications, Vol. 5, No. 2. (1995), pp. 353-361.

Human activities often result in the creation of patchy landscapes, which may influence distribution and abundance of some wildlife species and their ectoparasites. Risk of exposure to Lyme disease is a function of the abundance of ticks (Ixodes scapularis, formerly I. dammini), which in turn may be determined by the distribution of key vertebrate hosts within landscapes. We used transect drag sampling and small-mammal trapping to estimate, respectively, the abundance of host-seeking and attached ticks in a rural landscape (southeastern New York) consisting of a mosaic of several discrete habitat types. Forested habitat types supported higher densities of host-seeking ticks than herbaceous or shrub-dominated habitats. However, in patches of little bluestem grass and gray dogwood shrubs, small mammals had high tick burdens despite low densities of host-seeking ticks. There was an outbreak of larval ticks limited to oak-dominated habitats in summer, 1992, which we postulate was related to unusually heavy acorn (mast) production attracting white-tailed deer and attached adult deer ticks, in autumn 1991. This hypothesis was supported by low densities of larval ticks in oak patches in summer, 1993, following poor mast production the previous autumn. Instead, the 1993 larval peak shifted to maple-dominated habitats, which may result from intensive use of these patches by deer in nonmast years. The abundance of host-seeking nymphs was strongly correlated with the abundance of white-footed mice the prior summer. Both the high tick burdens in little bluestem and dogwood patches, and shifting locations of larval outbreaks, appear to be functions of landscape configuration, especially patch size and juxtaposition.

Chain reactions linking acorns to gypsy moth outbreaks and Lyme disease risk.

CG Jones — 2005-08-05T13:19:17-00:00

Science, Vol. 279, No. 5353. (13 February 1998), pp. 1023-1026.

In eastern U.S. oak forests, defoliation by gypsy moths and the risk of Lyme disease are determined by interactions among acorns, white-footed mice, moths, deer, and ticks. Experimental removal of mice, which eat moth pupae, demonstrated that moth outbreaks are caused by reductions in mouse density that occur when there are no acorns. Experimental acorn addition increased mouse density. Acorn addition also increased densities of black-legged ticks, evidently by attracting deer, which are key tick hosts. Mice are primarily responsible for infecting ticks with the Lyme disease agent. The results have important implications for predicting and managing forest health and human health.

Dynamic environmental modelling in GIS: 2. Modelling error propagation

D Karssenberg — 2005-07-04T12:47:13-00:00

International Journal of Geographical Information Science, Vol. 19, No. 6. (July 2005), pp. 623-637.

Environmental modelling languages provide the possibility to construct models in two or three spatial dimensions. These models can be static models, without a time component, or dynamic models. Dynamic models are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a period or time step preceding t . Since inputs and parameters of environmental models are associated with errors, environmental modelling languages need to provide techniques to calculate how these errors propagate to the output(s) of the model. Since these techniques are not yet available, the paper describes concepts for extending an environmental-modelling language with functionality for error-propagation modelling. The approach models errors in inputs and parameters as stochastic variables, while the error in the model outputs is approximated with a Monte Carlo simulation. A modelling language is proposed which combines standard functions in a structured script (program) for building environmental models, and calculation of error propagation in these models. A prototype implementation of the language is used in three example models to illustrate the concepts.