Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

https://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf

Maximum entropy modeling of species geographic distributions

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk

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Bayesian Phylogenetics with BEAUti and the BEAST 1.7

Ecological changes in the phenology and distribution of plants and animals are occurring in all well-studied marine, freshwater, and terrestrial groups These observed changes are heavily biased in the directions predicted from global warming and have been linked to local or regional climate change through correlations between climate and biological variation, field and laboratory experiments, and physiological research Range-restricted species, particularly polar and mountaintop species, show severe range contractions and have been the first groups in which entire species have gone extinct due to recent climate change Tropical coral reefs and amphibians have been most negatively affected Predator-prey and plant-insect interactions have been disrupted when interacting species have responded differently to warming Evolutionary adaptations to warmer conditions have occurred in the interiors of species’ ranges, and resource use and dispersal have evolved rapidly at expanding range margins Observed genetic shifts modulate local effects of climate change, but there is little evidence that they will mitigate negative effects at the species level

https://www.fws.gov/southwest/es/documents/R2ES/LitCited/LPC_2012/Parmesan_2006.pdf

Ecological and Evolutionary Responses to Recent Climate Change

Nipah virus is a highly pathogenic but poorly known paramyxovirus from South and Southeast Asia. In spite of the risks that it poses to human health, the geography and ecology of its occurrence remain little understood—the virus is basically known from Bangladesh and peninsular Malaysia, and little in between. In this contribution, I use documented occurrences of the virus to develop ecological niche-based maps summarizing its likely broader occurrence—although rangewide maps could not be developed that had significant predictive abilities, reflecting minimal sample sizes available, maps within Bangladesh were quite successful in identifying areas in which the virus is predictably present and likely transmitted.

Mapping Risk of Nipah Virus Transmission Across Asia and Across Bangladesh

In his comment, Peterson reiterates the need for improved methods for collecting and presenting spatial epidemiologic data for vector-borne diseases (1). He agrees with us that lack of reliable data on probable pathogen exposure site is an obstacle to the development of predictive spatial risk models (2). In that article we noted, “New methods are urgently needed to determine probable pathogen exposure sites that will yield reliable results while taking into account economic and time constraints of the public health system and attending physicians.” Peterson suggests that the point-radius method is a viable solution to this problem. Unfortunately, its practical implementation for vector-borne diseases is neither reliable nor cost-effective.

With regard to practical implementation of the point-radius method in a public health setting, Peterson states, “If data are to be captured initially on paper, the data recorder would simply record the focal point of the person’s activities (usually a residence) and an approximate description of the person’s movements (e.g., broadly across the state, housebound, within 20 miles)” (1). We find a number of serious problems with this approach to determining probable sites of pathogen exposure, primarily that meaningful use of the point-radius method 1) will require not only recording detailed movements during the perceived window of opportunity for pathogen exposure but also weighting of risk by activity type and, for some vector-borne diseases, time of day; and 2) will require the public health community to allocate resources to in-depth interviews conducted by specially trained personnel.

Our first concern is that Peterson’s scenario does not distinguish between a car trip to the mall at noon and spending an evening on the golf course. In reality, one activity presents minimal risk for exposure to mosquitoes infected with West Nile virus, whereas the other is a potential high-risk activity. Giving equal weight to the movements represented by these activities will assuredly produce an unreliable result for probable pathogen exposure site. Other issues are patient recall and reluctance to provide information on movement patterns and specific activities. Peterson’s suggestion that the data recorder would simply record the focal point of the person’s activities and an approximate description of the person’s movements is therefore a grossly oversimplified solution to a complex public health problem.

With regard to the second concern, the average physician likely lacks the knowledge, time, and training in vector-borne disease epidemiology and ecology needed to accurately assess when and where risk for pathogen exposure occurred. To be of use, the method will require in-depth patient interviews by specially trained personnel from local or state health departments. Even then, we doubt that the quality of data gleaned would justify the cost incurred.

We fail to see that the quality of information gathered by using the point-radius method would be an improvement over our suggestion. In our original article, we suggested using sets of standardized questions that are tailored to a given vector-borne disease. We also indicated that a critical minimal need includes a basic assessment of whether pathogen exposure likely occurred in 1) the peridomestic environment, 2) outside the peridomestic environment but within the county of residence, or 3) outside the county of residence (2).

The challenge of how to most effectively collect and present spatial epidemiologic data is neither conceptual nor technologic; rather, it is logistic and legal. Any new method must 1) weigh the public health utility of the method against the time and cost required for the public health system to implement it and 2) comply with existing patient privacy laws. The point-radius method clearly fails on the first count and also likely will present substantial problems in terms of patient privacy.

We agree that presenting data for case counts and disease incidence by ZIP code or census tract falls short of the desired level of spatial precision. However, this realistic compromise 1) is a marked improvement over the current practice to display only county-based spatial patterns for case counts or incidence; 2) incurs only minimal added time and cost for the public health community; and 3) can be implemented, especially for census tracts, with minimal concerns regarding patient privacy.

Improving Methods for Reporting Spatial Epidemiologic Data

This is the publisher's version, also available electronically from http://www.bioone.org/doi/abs/10.1642/0004-8038%282006%29123%5B885%3ACOTTAR%5D2.0.CO%3B2.

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Consistency of Taxonomic Treatments: A Response to Remsen (2005)

Despite their fame, Ebola and Marburg viruses (family Filoviridae) remain mysterious. Filovirus outbreaks are restricted to tropical Africa, but their likely geographic extent has been outlined only recently, and their natural reservoir host(s) remains unidentified. If environmental conditions associated with outbreaks in space and time can be identified precisely, much could be learned about the ecology, evolution and transmission of these viruses. We examined five filovirus outbreaks for which time series of remotely sensed data (NDVI values) are available and for which the reservoir-to-human index case transmission timing and location are known. A wavelet analysis was used to detect anomalous behaviour in the NDVI signal across multiple time scales for each outbreak. Scale-wise anomalies were manifested at a 20-day scale, and were found in four of the five sites, one to three weeks preceding outbreaks, suggesting that filovirus-caused disease outbreaks may be associated with either behavioural shifts i...

Spatiotemporal environmental triggers of Ebola and Marburg virus transmission

To the Editor: In their article, “Geographic distribution of wild rabies risk and evaluation of the factors associated with its incidence in Colombia, 1982–2010,” Brito and colleagues (1) analyzed the spatial distribution of batborne rabies in Colombia and showed an association of occurrence of rabies outbreaks with climatic tendencies. The authors also identified risk clusters for rabies based on modeled potential distributions. While we appreciate this work, which fills knowledge gaps regarding the epidemiology of vampire-borne rabies in Latin America, we are also concerned because some of the data presented merits re-evaluation and correction. The distribution of rabies in Colombia was modeled using an ecological niche modeling approach. The authors analyzed its distribution with respect to 15 climatic variables, using all available occurrences in model calibration. Model evaluation, a critical issue if modeling results are to be incorporated in public health decision making, was thus limited in scope and strength. Our goal herein is to evaluate the quality, quantity, and distribution of rabies occurrences data and effectiveness of the environmental data used by Brito and colleagues. We based our analyses on the data used in and provided by the article. First, Brito and colleagues used all available occurrence points in model calibration. Generally, however, clusters of occurrences can lead to overfitting models to specific environments in specific areas (2). To avoid biases in model calibration, we therefore used single occurrences per cell—indeed, in some cases, multiple occurrences grouped within single cells in the environmental layers used by the authors. This step left 2 008 of the original 2 330 occurrences. This minor correction reduces overfitting, and increased area identified as suitable by > 4%. Another major concern centers on use of climatic variables as environmental information in analyses across such restricted extents. Using principal components analysis, we found the climatic layers to be highly intercorrelated: indeed, just three principal components summarized > 99% of variance among the 15 climatic layers analyzed by Brito and colleagues. We compared models calibrated with respect to that same climatic information against those based on remotely sensed data (monthly Normalized Difference Vegetation Index [NDVI] data sets for October 1992 – September 1993 0.01° resolution), which provide finer spatial resolution and less need for interpolation. Indeed climate data showed environmental homogeneity across broad areas, with spatial lags of > 350 km, whereas remotely-sensed data offered a richer and more detailed environmental characterization: 11 principal components were required to summarize > 99% of overall variance, and spatial lags were only < 250 km (Figure 1). The contrast in detail available from these two data sources is evident in Figure 2. Brito and colleagues subdivided available data at random for calibration versus evaluation of their model, and used approaches that weight presence and absence data equally (2). We evaluated model predictions more rigorously, with spatial subdivision of occurrences into calibration and evaluation areas, and using evaluation tools that emphasize presence information (2, 3). Models calibrated based on presence

/pdf/spatial-epidemiology-of-bat-borne-rabies-in-colombia-9bqt16zu8r.pdf

A. Townsend Peterson

Papers

Mapping Risk of Nipah Virus Transmission Across Asia and Across Bangladesh

Improving Methods for Reporting Spatial Epidemiologic Data

Consistency of Taxonomic Treatments: A Response to Remsen (2005)

Spatiotemporal environmental triggers of Ebola and Marburg virus transmission

Spatial epidemiology of bat-borne rabies in Colombia