Linking land cover and species distribution models to project potential ranges of malaria vectors

An example using Anopheles arabiensis in Sudan and Upper Egypt

Douglas Fuller, Michael S. Parenti, Ali N. Hassan, John C Beier

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Background: Anopheles arabiensis is a particularly opportunistic feeder and efficient vector of Plasmodium falciparum in Africa and may invade areas outside its normal range, including areas separated by expanses of barren desert. The purpose of this paper is to demonstrate how spatial models can project future irrigated cropland and potential, new suitable habitat for vectors such as An. arabiensis. Methods. Two different but complementary spatial models were linked to demonstrate their synergy for assessing re-invasion potential of An. arabiensis into Upper Egypt as a function of irrigated cropland expansion by 2050. The first model (The Land Change Modeler) was used to simulate changes in irrigated cropland using a Markov Chain approach, while the second model (MaxEnt) uses species occurrence points, land cover and other environmental layers to project probability of species presence. Two basic change scenarios were analysed, one involving a more conservative business-as-usual (BAU) assumption and second with a high probability of desert-to-cropland transition (Green Nile) to assess a broad range of potential outcomes by 2050. Results: The results reveal a difference of 82,000 sq km in potential An. arabiensis range between the BAU and Green Nile scenarios. The BAU scenario revealed a highly fragmented set of small, potential habitat patches separated by relatively large distances (maximum distance=64.02km, mean=12.72km, SD=9.92), while the Green Nile scenario produced a landscape characterized by large patches separated by relatively shorter gaps (maximum distance=49.38, km, mean=4.51km, SD=7.89) that may be bridged by the vector. Conclusions: This study provides a first demonstration of how land change and species distribution models may be linked to project potential changes in vector habitat distribution and invasion potential. While gaps between potential habitat patches remained large in the Green Nile scenario, the models reveal large areas of future habitat connectivity that may facilitate the re-invasion of An. arabiensis from Sudan into Upper Egypt. The methods used are broadly applicable to other land cover changes as they influence vector distribution, particularly those related to tropical deforestation and urbanization processes.

Original languageEnglish
Article number264
JournalMalaria Journal
Volume11
DOIs
StatePublished - Aug 8 2012

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Sudan
Anopheles
Egypt
Malaria
Ecosystem
Markov Chains
Urbanization
Conservation of Natural Resources
Plasmodium falciparum
Reference Values

Keywords

  • Anopheles arabiensis
  • Irrigation
  • Land change Modeler
  • MaxEnt
  • Species invasion potential
  • Sudan and Upper Egypt

ASJC Scopus subject areas

  • Infectious Diseases
  • Parasitology

Cite this

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title = "Linking land cover and species distribution models to project potential ranges of malaria vectors: An example using Anopheles arabiensis in Sudan and Upper Egypt",
abstract = "Background: Anopheles arabiensis is a particularly opportunistic feeder and efficient vector of Plasmodium falciparum in Africa and may invade areas outside its normal range, including areas separated by expanses of barren desert. The purpose of this paper is to demonstrate how spatial models can project future irrigated cropland and potential, new suitable habitat for vectors such as An. arabiensis. Methods. Two different but complementary spatial models were linked to demonstrate their synergy for assessing re-invasion potential of An. arabiensis into Upper Egypt as a function of irrigated cropland expansion by 2050. The first model (The Land Change Modeler) was used to simulate changes in irrigated cropland using a Markov Chain approach, while the second model (MaxEnt) uses species occurrence points, land cover and other environmental layers to project probability of species presence. Two basic change scenarios were analysed, one involving a more conservative business-as-usual (BAU) assumption and second with a high probability of desert-to-cropland transition (Green Nile) to assess a broad range of potential outcomes by 2050. Results: The results reveal a difference of 82,000 sq km in potential An. arabiensis range between the BAU and Green Nile scenarios. The BAU scenario revealed a highly fragmented set of small, potential habitat patches separated by relatively large distances (maximum distance=64.02km, mean=12.72km, SD=9.92), while the Green Nile scenario produced a landscape characterized by large patches separated by relatively shorter gaps (maximum distance=49.38, km, mean=4.51km, SD=7.89) that may be bridged by the vector. Conclusions: This study provides a first demonstration of how land change and species distribution models may be linked to project potential changes in vector habitat distribution and invasion potential. While gaps between potential habitat patches remained large in the Green Nile scenario, the models reveal large areas of future habitat connectivity that may facilitate the re-invasion of An. arabiensis from Sudan into Upper Egypt. The methods used are broadly applicable to other land cover changes as they influence vector distribution, particularly those related to tropical deforestation and urbanization processes.",
keywords = "Anopheles arabiensis, Irrigation, Land change Modeler, MaxEnt, Species invasion potential, Sudan and Upper Egypt",
author = "Douglas Fuller and Parenti, {Michael S.} and Hassan, {Ali N.} and Beier, {John C}",
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AU - Beier, John C

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N2 - Background: Anopheles arabiensis is a particularly opportunistic feeder and efficient vector of Plasmodium falciparum in Africa and may invade areas outside its normal range, including areas separated by expanses of barren desert. The purpose of this paper is to demonstrate how spatial models can project future irrigated cropland and potential, new suitable habitat for vectors such as An. arabiensis. Methods. Two different but complementary spatial models were linked to demonstrate their synergy for assessing re-invasion potential of An. arabiensis into Upper Egypt as a function of irrigated cropland expansion by 2050. The first model (The Land Change Modeler) was used to simulate changes in irrigated cropland using a Markov Chain approach, while the second model (MaxEnt) uses species occurrence points, land cover and other environmental layers to project probability of species presence. Two basic change scenarios were analysed, one involving a more conservative business-as-usual (BAU) assumption and second with a high probability of desert-to-cropland transition (Green Nile) to assess a broad range of potential outcomes by 2050. Results: The results reveal a difference of 82,000 sq km in potential An. arabiensis range between the BAU and Green Nile scenarios. The BAU scenario revealed a highly fragmented set of small, potential habitat patches separated by relatively large distances (maximum distance=64.02km, mean=12.72km, SD=9.92), while the Green Nile scenario produced a landscape characterized by large patches separated by relatively shorter gaps (maximum distance=49.38, km, mean=4.51km, SD=7.89) that may be bridged by the vector. Conclusions: This study provides a first demonstration of how land change and species distribution models may be linked to project potential changes in vector habitat distribution and invasion potential. While gaps between potential habitat patches remained large in the Green Nile scenario, the models reveal large areas of future habitat connectivity that may facilitate the re-invasion of An. arabiensis from Sudan into Upper Egypt. The methods used are broadly applicable to other land cover changes as they influence vector distribution, particularly those related to tropical deforestation and urbanization processes.

AB - Background: Anopheles arabiensis is a particularly opportunistic feeder and efficient vector of Plasmodium falciparum in Africa and may invade areas outside its normal range, including areas separated by expanses of barren desert. The purpose of this paper is to demonstrate how spatial models can project future irrigated cropland and potential, new suitable habitat for vectors such as An. arabiensis. Methods. Two different but complementary spatial models were linked to demonstrate their synergy for assessing re-invasion potential of An. arabiensis into Upper Egypt as a function of irrigated cropland expansion by 2050. The first model (The Land Change Modeler) was used to simulate changes in irrigated cropland using a Markov Chain approach, while the second model (MaxEnt) uses species occurrence points, land cover and other environmental layers to project probability of species presence. Two basic change scenarios were analysed, one involving a more conservative business-as-usual (BAU) assumption and second with a high probability of desert-to-cropland transition (Green Nile) to assess a broad range of potential outcomes by 2050. Results: The results reveal a difference of 82,000 sq km in potential An. arabiensis range between the BAU and Green Nile scenarios. The BAU scenario revealed a highly fragmented set of small, potential habitat patches separated by relatively large distances (maximum distance=64.02km, mean=12.72km, SD=9.92), while the Green Nile scenario produced a landscape characterized by large patches separated by relatively shorter gaps (maximum distance=49.38, km, mean=4.51km, SD=7.89) that may be bridged by the vector. Conclusions: This study provides a first demonstration of how land change and species distribution models may be linked to project potential changes in vector habitat distribution and invasion potential. While gaps between potential habitat patches remained large in the Green Nile scenario, the models reveal large areas of future habitat connectivity that may facilitate the re-invasion of An. arabiensis from Sudan into Upper Egypt. The methods used are broadly applicable to other land cover changes as they influence vector distribution, particularly those related to tropical deforestation and urbanization processes.

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