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Data paper

Four Rodent and Vole Biodiversity Models for Europe

William Wint1, 
David Morley2, 
Neil S. Alexander3
  1. Senior Research Associate, Environmental Research Group Oxford (ERGO), Department of Zoology, Oxford, United Kingdom
  2. Research Assistant, Environmental Research Group Oxford (ERGO), Department of Zoology, Oxford, United Kingdom
  3. Research Assistant, Environmental Research Group Oxford (ERGO), Department of Zoology, Oxford, United Kingdom

Abstract

The mechanisms underlying associations between rodent diversity and the spread of rodent borne diseases are still unclear. The number of potential host species may influence a disease by either spreading the pathogen (or vectors) more rapidly or conversely by reducing contact with other hosts through the effects of dilution. In either case the number of potential hosts may impact on the distribution of a disease or its vectors. Four spatially modelled indices of rodent species richness have been generated to support distribution modelling of rodent borne diseases specifically initially focussing on Hantaviruses and tick borne diseases.

Keywords: rodent, vole, biodiversity, tick-borne, hantavirus, linear regression, random forest, generalised linear modelling.

Funding Statement

EU grant GOCE-2003-010284 EDENext and is catalogued by the EDENext Steering Committee as EDENextD002 (http://www.edenext.eu). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the article.

(1) Overview

Context

Spatial coverage

Description: This dataset is clipped to the EDENext [1] extent that covers the continent of Europe and parts of North Africa down to 34 degrees latitude. The projection is WGS84 (ESPG:4326).

Northern boundary: 72.3

Southern boundary: 34.0

Eastern boundary: -12.0

Western boundary: 47.6

Temporal coverage

01/01/2012 (Current)

Species

Striped Field Mouse, Apodemus agrarius; Yellow-necked Mouse, Apodemus flavicollis;Broad-toothed Field Mouse, Apodemus mystacinus; Wood Mouse, Apodemus sylvatica; Bank vole, Clethrionomys glariolus; Common vole, Microtus arvalis; European pine vole, Microtus subterraneus;Norway rat, Rattus norvegicus; Black Rat, Rattus rattus; Common Shrew, Sorex araneus; Eurasian Pygmy Shrew, Sorex minutus

(2) Methods

Steps

Input Datasets

The rodent distribution layers that were modelled models were complied from several sources as follows:

  • The EMMA Database [2]: Mapping Europe's mammals using data from the Atlas of European Mammals.
  • IUCN Red List Dataset [3]
  • The National Biodiversity Network [4] UK 10k Data- Making all biological records freely and easily available to everyone
  • Globcover 2009 Land cover map [5]

Four biodiversity indices - the number of species recorded at a single location - were defined according to different species lists suggested by various experts identified in the Acknowledgements section. The species for each index are identified in Table 1.

Species\Indexsp04nosp05n0Hanta10ters4
Apodemus agrariusXXX
Apodemus flavicollisXXXX
Apodemus mystacinusX
Apodemus sylvaticaXXX
Clethrionomys glariolusXXX
Microtus arvalisXXXX
Microtus subterraneusX
Rattus norvegicusX
Rattus rattusX
Sorex araneusXX
Sorex minutusX

Table 1

Species included in each index are marked with an "X". For more details please see the supporting document (edenextvolelayers.docx) within the data download.

The spatial modelling also requires a comprehensive predictor variable suite which included a wide range of remotely sensed variables as follows:

  1. Remotely sensed climatic indicators derived by Temporal Fourier Analysis (TFA) of MODIS satellite imagery of several temperature parameters, and vegetation indices for the period 2001-2008 [6]
  2. Digital Elevation from the Shuttle Radar Topography Mission, together with derived aspect and ruggedness [7]
  3. Fourier Processed Precipitation, and allied Bioclimatic Indicator (Bioclim) precipitation variables from the WORLDCLIM datasets [8]
  4. Length of Growing Period from United Nations Food and Agriculture Organisation [9]
  5. Travel Time to major towns from the Joint Research Centre at Ispra [10]
  6. Human population density derived from the Global Rural Urban Mapping project at CEISIN [11]
  7. A weighted human population proximity layer [12]

The four different rodent biodiversity index layers were modelled using a range of techniques:

  1. zoned random forest [14] method calculated via the VECMAP [15] modelling system using a point input dataset
  2. zoned bootstrapped General Linear Model (GLM) [16] method calculated via VECMAP [15] modelling system using a point input dataset
  3. zoned regression calculated using the FARMS [17] modelling suite using an polygon input dataset

An ensemble mean was taken of the three model outputs for each Biodiversity variable.

Output Datasets

Each of the four Biodiversity indices has been provided as a quick look map in JPEG format to view from any image viewer. The data itself is distributed as GIS Raster data in two formats. GeoTIFFs which is a standard proprietary GIS raster format. GeoJP2 (JPEG 2000 format) which is a non-proprietary format. Please note that as the GeoJP2 format is an integer-only format, the values found in this format have been multiplied by 100 to minimise the loss of data.

To access and analyse the Raster data directly GeoTIFFs and GeoJPGs can be read by most GIS software and some other software packages These formats are compatible with proprietary (ESRI ArcGIS) and open source (Quantum GIS (QGIS) or R-project raster package)

Folder Structure

  • quicklooks - JPEG maps for viewing only
  • tiff - GeoTIFF data 0.008333 degree (~1km) 32bit floating point
  • geoJPG2k - GeoJPG 2000, 0.008333 degree (~1km) 16bit unsigned Integer data. ***This data has been multiplied by 100 to save as an integer value with minimum loss of data.***

Sampling Strategy

Sample points were extracted for input into the three different models from a 20km matrix defining the number of key rodent species for each index (as defined in table 1). Depending on the model 1000-2000 sample points were used in each of 25 bootstraps.

Quality Control

These models are a first attempt at quantifying the rodent biodiversity at this scale and there has been no ground truth validation of these maps so far. The model outputs all, however, satisfy standard accuracy metrics (AIC, R squared and Kappa statistic) assuring statistical reliability. They have also been informally reviewed by project Rodent-Borne disease experts.

Constraints

There were no constraints involved in data production.

Privacy

N/A

(3) Dataset Description

Object Name

volebiodivv2.zip

Data Type

Primary data, Processed data, Interpretation of data.

Format Names and Versions

JPG, TIF, TFW, XML

Creation Dates

15/08/2012 - 15/08/2012

Dataset Creators

William Wint, David Morley, Neil S. Alexander.

Publication Date

08/07/2013

language

English

License

CC0

(4) Reuse potential

The subject of the effect of rodent biodiversity upon the spread of vector-borne disease is a fairly new topic. These layers are a first attempt to provide a description of rodent biodiversity at a continental scale. They have been developed in the hope they will aid epidemiologists test hypotheses relating to the role of rodent biodiversity’ in the spread of infectious disease.

Areas of future development on the dataset itself might be to: assess the accuracy of the maps through ground-truthing; a comparison of the three different models used in this analysis and an assessment of which model provides the most accurate outputs; the application of this approach to different groups of species thought to be related to the spread of different diseases.

Acknowledgements

Thank you to the following experts in the fields of Rodent-Borne Disease that helped us identify the key species to include in this analysis: Katrien Tersago, Evolutionary Ecology Group, University of Antwerp, Belgium; Liina Voutilainen, METLA and University of Helsinki, Finland; Gert Olsson, Swedish Institute for Infectious Disease Control, Stockholm, Sweden.

References

  1. EDENext Data Management Team 2012 EDENext Data Portal, Available at: http://www.edenextdata.com [Last accessed 27 April2012].
  2. University of Paris 7 Denis Diderot on behalf of UMS2414 RIATE, France. ESPON2013 database, Available at: http://www.espon.eu/main/Menu_Projects/Menu_ScientificPlatform/espondatabase2013.html [Last accessed 27 April 2012] .
  3. UN Dept of Economic and social affairs, Population Division. 2010 World Population Prospects, the 2010 Revision, Available at: http://esa.un.org/unpd/wpp/Excel-Data/population.htm [Last accessed 27 April 2011] .
  4. National Biodiveristy Network 2001- NBN Gateway, Available from: http://data.nbn.org.uk [Last accessed 7 July 2008]. The data providers and NBN Trust bear no responsibility for the further analysis or interpretation of this material, data and/or information.
  5. Arino, O and et al. 2009 Global Land Cover Map for 2009. Pangea, DOI: http://dx.doi.org/10.1594/PANGAEA.787668
  6. Scharlemann, J P W and et al. 2008 Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLOS ONE, DOI: http://dx.doi.org/10.1371/journal.pone.0001408
  7. The Shuttle Radar Topography Mission 2012 The Shuttle Radar Topography Mission (SRTM) Home Page, Available at: http://www2.jpl.nasa.gov/srtm/.
  8. Hijmans, R J and et al. 2005 Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25(15): 1965–1978, DOI: http://dx.doi.org/10.1002/joc.1276
  9. UN Dept of Economic and social affairs, Population Division. Length of growing period (LGP) zones of the world (FGGD), Available at: http://www.fao.org/geonetwork/srv/en/metadata.show?id=14057.
  10. UN Dept of Economic and social affairs, Population Division. 2010 World Population Prospects, the 2010 Revision, Available at: http://www.fao.org/geonetwork/srv/en/metadata.show?id=14057.
  11. Center for International Earth Science Information Network (CIESIN)/Columbia University, International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT). Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Count Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), Available at: http://sedac.ciesin.columbia.edu/data/set/grump-v1-population-count [Last accessed 27 April 2011].
  12. Alexander, N S and Wint, W 2013 Projected Population Proximity Indices (30km) for 2005, 2030 and 2050. Journal of Open Public Health Data 1(1)DOI: http://dx.doi.org/http://dx.doi.org/10.5334/jophd.ab
  13. R Core Team 2012 R: A language and environment for statistical computing. 2nd ed. Vienna: R Foundation for Statistical Computing.
  14. randomForest: Breiman and Cutler's random forests for classification and regression. R-Project randomForest package, Available at: http://cran.r-project.org/web/packages/randomForest/index.html.
  15. Avia-GIS Zoersel, Belgium; ERGO Ltd Oxford, UK; and MEDES, Toulouse, France, for the European Space Agency. Modelling Module in development for the VECMAP system, Available at: http://www.medes.fr/home_en/telemedicine/tele_epidemiology/vecmap.html.
  16. R-Project glm. Fitting Generalized Linear Models, AVailable at: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/glm.html.
  17. Robinson, T and et al.  FARMS: Mapping the Global Distribution of Livestock.  (In preparation).

How to cite: Wint, W, Morley, D and Alexander, N.S. 2013. Four Rodent and Vole Biodiversity Models for Europe. Open Health Data 1(1):e3, DOI: http://dx.doi.org/10.5334/jophd.ac

This is an Open Access article distributed under the terms of the Creative Commons Attribution License
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This article has been peer reviewed (journal peer review policy).

Published on 11 July 2013.

ISSN: 2054-7102 | Published by Ubiquity Press | Creative Commons License This work is licensed under a Creative Commons License.