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Probability of Habitat Suitability for Stem Borer Pests and Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)

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Persistent identifier
doi:10.60507/FK2/BBP6GG
Published version
1.0
Publication date
2023-09-18
License
CC BY-SA 4.0

Description

The research within the scope of which the presented data was generated was part of the funding initiative ‘Knowledge for Tomorrow-Cooperative Research Project in sub-Saharan Africa on Resource, their Dynamics, and Sustainability’ funded by the Volkswagen Foundation. The overall study aimed at investigating the uncertainties in the effectiveness of biological control of stem borers under different climate change scenarios in Kenya and Tanzania. Using the species distribution modelling approach MaxEnt, the research predicts the current and future distribution of three important lepidopteran stem borer pests of maize in eastern Africa, i.e., Busseola fusca (Fuller, 1901), Chilo partellus (Swinhoe, 1885) and Sesamia calamistis (Hampson, 1910), and two of their parasitoids used for biological control, i.e., Cotesia flavipes (Cameron, 1891) and Cotesia sesamiae (Cameron, 1906). Based on these potential distributions and data collected during household surveys with local farmers in Kenya and Tanzania, future maize yield losses are predicted considering three different Global Circulation Models (GCMs) for four different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP 3-7.0, SSP5-8.5) and two time periods, i.e., 2041-2060 and 2081-2100. A raster in which probability of habitat suitability is separately specified for each grid cell is the immediate output from species distribution modelling with MaxEnt. Probability of habitat suitability for the respective species hereby is expressed as probability value ranging between 0 (unsuitable habitat) to 1 (perfectly suitable habitat). Probability of habitat suitability was modelled for five species for current climatic conditions, as well as for four SSPs and two time periods. Quality/Lineage: The generated data is based on a collection of presence points from different sources and environmental data from WorldClim. Species Distribution Models (SDMs) have been built using the 'kuenm' package in RStudio. The data on probability of habitat suitability are an immediate output from SDM in R. Habitat suitability is here given as multi-model average of predictions for three Global Circulation Models (GCMs) from CMIP6: CanESM5, CNRM-CM6-1 and MIROC6

Creators

Keywords

natural enemy, stem borer pest, parasitoid, integrated pest management, species habitat suitability, biota, farming

Files

Before downloading: Use and redistribution are governed by CC BY-SA 4.0. Review and comply with the license before using the data.
FileTypeBytesChecksum
ZEF_bio_stem-borer-habitat-suitability-model_tanzan-kenya-ras.zipapplication/zip591316MD5 bcd7cdee395b575e05944ff5467a4077
metadata.xmltext/xml24586MD5 451bd43b0333564b90896fe94192a845
Map-new.PNGimage/png19955MD5 971f2bf7bd4cbd38c80b8c44c17fe614

Citation

Ines Jendritzki, 2023-09-18, Probability of Habitat Suitability for Stem Borer Pests and Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100), doi:10.60507/FK2/BBP6GG, V1.0

Additional Dataverse fields

Id61
Dataset Typedataset
Internal Version Number7
Latest Version Publishing StateRELEASED
Deaccession LinkNot supplied
Release Time2023-09-18T10:29:50Z
Create Time2023-09-18T08:53:27Z
Citation Date2023-09-18
File Access RequestTrue

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Complete Dataverse metadata

Citation Metadata

Title title
Probability of Habitat Suitability for Stem Borer Pests and Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)
Other Id otherId
  1. Other Id Agency
    ZEF Data Portal
    Other Id Value
    36105b57-ae76-4433-b0ea-8b224af13ac4
Author author
  1. Author Name
    Ines Jendritzki
    Author Affiliation
    Center for Development Research, Department Ecology and Natural Resources Management (ZEF C), University of Bonn
Dataset Contact datasetContact
  1. Dataset Contact Name
    Ines Jendritzki
    Dataset Contact Affiliation
    Center for Development Research, Department Ecology and Natural Resources Management (ZEF C), University of Bonn
    Dataset Contact Email
    I.Jendritzki@gmx.de
Ds Description dsDescription
  1. Ds Description Value
    The research within the scope of which the presented data was generated was part of the funding initiative ‘Knowledge for Tomorrow-Cooperative Research Project in sub-Saharan Africa on Resource, their Dynamics, and Sustainability’ funded by the Volkswagen Foundation. The overall study aimed at investigating the uncertainties in the effectiveness of biological control of stem borers under different climate change scenarios in Kenya and Tanzania. Using the species distribution modelling approach MaxEnt, the research predicts the current and future distribution of three important lepidopteran stem borer pests of maize in eastern Africa, i.e., Busseola fusca (Fuller, 1901), Chilo partellus (Swinhoe, 1885) and Sesamia calamistis (Hampson, 1910), and two of their parasitoids used for biological control, i.e., Cotesia flavipes (Cameron, 1891) and Cotesia sesamiae (Cameron, 1906). Based on these potential distributions and data collected during household surveys with local farmers in Kenya and Tanzania, future maize yield losses are predicted considering three different Global Circulation Models (GCMs) for four different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP 3-7.0, SSP5-8.5) and two time periods, i.e., 2041-2060 and 2081-2100. A raster in which probability of habitat suitability is separately specified for each grid cell is the immediate output from species distribution modelling with MaxEnt. Probability of habitat suitability for the respective species hereby is expressed as probability value ranging between 0 (unsuitable habitat) to 1 (perfectly suitable habitat). Probability of habitat suitability was modelled for five species for current climatic conditions, as well as for four SSPs and two time periods. Quality/Lineage: The generated data is based on a collection of presence points from different sources and environmental data from WorldClim. Species Distribution Models (SDMs) have been built using the 'kuenm' package in RStudio. The data on probability of habitat suitability are an immediate output from SDM in R. Habitat suitability is here given as multi-model average of predictions for three Global Circulation Models (GCMs) from CMIP6: CanESM5, CNRM-CM6-1 and MIROC6
Subject subject
  1. Other
Subject Refinement subjectRefinement
  1. Subject Refinement Value
    biota
  2. Subject Refinement Value
    farming
Free Keyword freeKeyword
  1. Free Keyword Value
    natural enemy
  2. Free Keyword Value
    stem borer pest
  3. Free Keyword Value
    parasitoid
  4. Free Keyword Value
    integrated pest management
  5. Free Keyword Value
    species habitat suitability
  6. Free Keyword Value
    biota
  7. Free Keyword Value
    farming
Topic Classification topicClassification
  1. Topic Class Value
    agricultural ecology
    Topic Class Vocab
    GEMET - Concepts, version 2.4
  2. Topic Class Value
    climatic change
    Topic Class Vocab
    GEMET - Concepts, version 2.4
  3. Topic Class Value
    biological pest control
    Topic Class Vocab
    GEMET - Concepts, version 2.4
  4. Topic Class Value
    agricultural pest
    Topic Class Vocab
    GEMET - Concepts, version 2.4
  5. Topic Class Value
    integrated pest control
    Topic Class Vocab
    GEMET - Concepts, version 2.4
Notes Text notesText
This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={36105b57-ae76-4433-b0ea-8b224af13ac4}.<br/> The naming of files follows the syntax: first letter species name_SSP_time period_mme average (= multi-model ensemble average)
Language language
  1. English
Time Period Covered timePeriodCovered
  1. Time Period Covered Start
    1970-01-01
    Time Period Covered End
    2000-12-31
Date Of Collection dateOfCollection
  1. Date Of Collection Start
    2021-05-01
    Date Of Collection End
    2021-05-01
Software software
  1. Software Name
    Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595
Other References otherReferences
  1. International Centre for Insect Physiology and Ecology (icipe)

Geospatial Metadata

Geographic Coverage geographicCoverage
  1. Country
    Kenya
  2. Other Geographic Coverage
    Tanzania
Geographic Bounding Box geographicBoundingBox
  1. West Longitude
    29.416667
    East Longitude
    41.916667
    North Latitude
    4.625
    South Latitude
    -11.708333
Coordinate Reference System CoordinateReferenceSystem
Coordinate Reference System.code Space
EPSG
Coordinate Reference System.code
4326
Coordinate Reference System.version
6.14(3.0.1)

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