Assessing the Potential Distribution of Insect Pests: Case Studies on Large Pine Weevil (Hylobius Abietis L) and Horse-Chestnut Leaf Miner (Cameraria Ohridella) under Present and Future Climate Conditions in European Forests. Barredo, J. I., Strona, G., de Rigo , D., Caudullo, G., Stancanelli, G., & San-Miguel-Ayanz, J. EPPO Bulletin, 45(2):273–281, August, 2015.
doi  abstract   bibtex   
Forest insect pests represent a serious threat to European forests and their negative effects could be exacerbated by climate change. This paper illustrates how species distribution modelling integrated with host tree species distribution data can be used to assess forest vulnerability to this threat. Two case studies are used: large pine weevil (Hylobius abietis L) and horse-chestnut leaf miner (Cameraria ohridella Deschka & Dimič) both at pan-European level. The proposed approach integrates information from different sources. Occurrence data of insect pests were collected from the Global Biodiversity Information Facility (GBIF), climatic variables for present climate and future scenarios were sourced, respectively, from WorldClim and from the Research Program on Climate Change, Agriculture and Food Security (CCAFS), and distributional data of host tree species were obtained from the European Forest Data Centre (EFDAC), within the Forest Information System for Europe (FISE). The potential habitat of the target pests was calculated using the machine learning algorithm of Maxent model. On the one hand, the results highlight the potential of species distribution modelling as a valuable tool for decision makers. On the other hand, they stress how this approach can be limited by poor pest data availability, emphasizing the need to establish a harmonised open European database of geo-referenced insect pest distribution data. [Excerpt: Discussion and conclusions] This study presented an approach to assess forest vulnerability to insect pests. The results illustrate potential effects of a changing climate in the distribution of two forest insect pests in Europe. Furthermore, they show how climate change can influence forest pests in different ways, for instance by expanding or contracting the habitat range of insects, as suggested by the projected range increment of C. ohridella, and the projected range reduction of H. abietis. This demonstrates that some areas that are not vulnerable under the present climate may become vulnerable under the future climate and vice versa. This aspect is illustrated in the vulnerability assessment implemented for H. abietis where shifts in the distribution of its suitable habitat towards the end of the 21st century suggests marked changes in the distribution of vulnerable areas, assuming no redistribution of host tree species. [\n] Temperature-related variables exhibited the highest relative contribution to the models for both insect species. Variables describing warmest limits and temperature ranges were the most important contributors to Maxent models. This result is not surprising if heat sensitivity of the target insects is considered (Denlinger & Yocum, 1998). The models computed for both insects using current climate have shown strong predictability of suitable habitat. Nevertheless, caution is needed in assessing impacts of future climate due the degree of uncertainty as discussed below. [\n] The results of this paper are in line with a number of recent studies suggesting shifts in the distribution of insects as consequence of climate change. For example, Evangelista et al. (2011) observed similar patterns in the interior West of the US, while Bebber et al. (2013) studied the phenomenon at the global scale. Netherer & Schopf (2010) provide a review on the potential effects of climate change on the distribution of forest insect pests in Europe, indicating that climate change has had impacts, and will continue to have a major influence on the spatio-temporal dynamics of insect herbivores in European forest. [\n] The present study proposes a framework to assess forest vulnerability to insect herbivores. However, the results are subject to a number of constraints. Suitable habitat involves the probability of presence under a set of environmental conditions, therefore it should be considered as an estimate of potential distribution and not as a distribution per se. In addition, other factors, not considered in this study, may affect the presence of insects, such as increasing concentrations of CO2, insect-plant interactions, levels of UVB, irradiation levels, and variations in nutrient availability. Other sources of uncertainty in the modelling approach derive from Maxent model fitting, from the limited number of climate simulations used, two GCMs in this case, the projection of only one scenario (A1B), and the many gaps of available geo-referenced insect pest data. This issue is evident in the sampling bias correction that reduced raw observation data to 24\,% and 13\,% for H. abietis and C. ohridella respectively. Actually, it was found that availability of geo-referenced data is a problem common to most European tree pests. It is noteworthy that for demonstrative purposes, the study focused on two of the species for which most occurrence data were available. [\n] The surprising lack or limited geo-referenced insect pest data availability at pan-European level is a major issue requiring coordinated further efforts. In particular, the results highlight how lack of data can strongly limit vulnerability assessments, making it difficult to communicate current and future forest threats to decision makers. Alleviating these limits would require a coordinated action of European organisations and stakeholders with the scope of setting an open Internet database of geo-referenced data useful for forest vulnerability assessment. In the next paragraphs the authors list the main features that the database should contain: [::] Scientific and common name of the observed insect pest; [::] Scientific and common name of the host tree species on which the insect was observed; [::] Systematic geo-referencing (Latitude, Longitude). A minimum spatial accuracy, 30 arc-s (˜1 km), would be desirable for integrating this information with high resolution environmental data such as WorldClim that is disseminated at a spatial resolution of up to 30 arc-s; [::] Geo-referencing using latitude and longitude coordinates is the preferable option for modelling purposes, however the database should be able to accommodate other options in case geographic coordinates are not available, i.e. when insect species are aggregated at grid, region, or administrative or analytical unit. In consequence the database should be able to host observations represented in several formats, such as points, grid cells at different scales or polygons of specifically defined areas; [::] Another fundamental piece of information to be collected is the date of the observation. This will offer the possibility of selecting specific time ranges of occurrence facilitating multi-temporal assessments, and giving the possibility to model specific outbreaks defined both spatially and temporarily; [::] The landscape where the observation is taken, in terms of different land cover categories such as natural forest, forest plantation, agroforestry, green urban areas, etc. This information is useful for delineating critical pest areas and assessment of potential spread on the basis of land cover categories; [::] A few generic items could be also easily recorded, for example, country of observation and observation method: direct, systematic survey, remote sensing, etc.; [::] Information on the organisation responsible for the observation; [::] Finally, the implementation of the database should take into consideration interoperability aspects defined by the INSPIRE Data Specification on Species Distribution - Technical Guidelines report (European Commission, 2014). This will facilitate dissemination and accessibility of datasets in the forest pest data users' community.
@article{barredoAssessingPotentialDistribution2015,
  title = {Assessing the Potential Distribution of Insect Pests: Case Studies on Large Pine Weevil ({{Hylobius}} Abietis {{L}}) and Horse-Chestnut Leaf Miner ({{Cameraria}} Ohridella) under Present and Future Climate Conditions in {{European}} Forests},
  author = {Barredo, Jos{\'e} I. and Strona, Giovanni and {de Rigo}, Daniele and Caudullo, Giovanni and Stancanelli, Giuseppe and {San-Miguel-Ayanz}, Jes{\'u}s},
  year = {2015},
  month = aug,
  volume = {45},
  pages = {273--281},
  issn = {1365-2338},
  doi = {10.1111/epp.12208},
  abstract = {Forest insect pests represent a serious threat to European forests and their negative effects could be exacerbated by climate change. This paper illustrates how species distribution modelling integrated with host tree species distribution data can be used to assess forest vulnerability to this threat. Two case studies are used: large pine weevil (Hylobius abietis L) and horse-chestnut leaf miner (Cameraria ohridella Deschka \& Dimi\v{c}) both at pan-European level. The proposed approach integrates information from different sources. Occurrence data of insect pests were collected from the Global Biodiversity Information Facility (GBIF), climatic variables for present climate and future scenarios were sourced, respectively, from WorldClim and from the Research Program on Climate Change, Agriculture and Food Security (CCAFS), and distributional data of host tree species were obtained from the European Forest Data Centre (EFDAC), within the Forest Information System for Europe (FISE). The potential habitat of the target pests was calculated using the machine learning algorithm of Maxent model. On the one hand, the results highlight the potential of species distribution modelling as a valuable tool for decision makers. On the other hand, they stress how this approach can be limited by poor pest data availability, emphasizing the need to establish a harmonised open European database of geo-referenced insect pest distribution data.

[Excerpt: Discussion and conclusions]

This study presented an approach to assess forest vulnerability to insect pests. The results illustrate potential effects of a changing climate in the distribution of two forest insect pests in Europe. Furthermore, they show how climate change can influence forest pests in different ways, for instance by expanding or contracting the habitat range of insects, as suggested by the projected range increment of C. ohridella, and the projected range reduction of H. abietis. This demonstrates that some areas that are not vulnerable under the present climate may become vulnerable under the future climate and vice versa. This aspect is illustrated in the vulnerability assessment implemented for H. abietis where shifts in the distribution of its suitable habitat towards the end of the 21st century suggests marked changes in the distribution of vulnerable areas, assuming no redistribution of host tree species.

[\textbackslash n] Temperature-related variables exhibited the highest relative contribution to the models for both insect species. Variables describing warmest limits and temperature ranges were the most important contributors to Maxent models. This result is not surprising if heat sensitivity of the target insects is considered (Denlinger \& Yocum, 1998). The models computed for both insects using current climate have shown strong predictability of suitable habitat. Nevertheless, caution is needed in assessing impacts of future climate due the degree of uncertainty as discussed below.

[\textbackslash n] The results of this paper are in line with a number of recent studies suggesting shifts in the distribution of insects as consequence of climate change. For example, Evangelista et al. (2011) observed similar patterns in the interior West of the US, while Bebber et al. (2013) studied the phenomenon at the global scale. Netherer \& Schopf (2010) provide a review on the potential effects of climate change on the distribution of forest insect pests in Europe, indicating that climate change has had impacts, and will continue to have a major influence on the spatio-temporal dynamics of insect herbivores in European forest.

[\textbackslash n] The present study proposes a framework to assess forest vulnerability to insect herbivores. However, the results are subject to a number of constraints. Suitable habitat involves the probability of presence under a set of environmental conditions, therefore it should be considered as an estimate of potential distribution and not as a distribution per se. In addition, other factors, not considered in this study, may affect the presence of insects, such as increasing concentrations of CO2, insect-plant interactions, levels of UVB, irradiation levels, and variations in nutrient availability. Other sources of uncertainty in the modelling approach derive from Maxent model fitting, from the limited number of climate simulations used, two GCMs in this case, the projection of only one scenario (A1B), and the many gaps of available geo-referenced insect pest data. This issue is evident in the sampling bias correction that reduced raw observation data to 24\,\% and 13\,\% for H. abietis and C. ohridella respectively. Actually, it was found that availability of geo-referenced data is a problem common to most European tree pests. It is noteworthy that for demonstrative purposes, the study focused on two of the species for which most occurrence data were available.

[\textbackslash n] The surprising lack or limited geo-referenced insect pest data availability at pan-European level is a major issue requiring coordinated further efforts. In particular, the results highlight how lack of data can strongly limit vulnerability assessments, making it difficult to communicate current and future forest threats to decision makers. Alleviating these limits would require a coordinated action of European organisations and stakeholders with the scope of setting an open Internet database of geo-referenced data useful for forest vulnerability assessment. In the next paragraphs the authors list the main features that the database should contain:

[::] Scientific and common name of the observed insect pest; [::] Scientific and common name of the host tree species on which the insect was observed; [::] Systematic geo-referencing (Latitude, Longitude). A minimum spatial accuracy, 30 arc-s (\texttildelow 1 km), would be desirable for integrating this information with high resolution environmental data such as WorldClim that is disseminated at a spatial resolution of up to 30 arc-s; [::] Geo-referencing using latitude and longitude coordinates is the preferable option for modelling purposes, however the database should be able to accommodate other options in case geographic coordinates are not available, i.e. when insect species are aggregated at grid, region, or administrative or analytical unit. In consequence the database should be able to host observations represented in several formats, such as points, grid cells at different scales or polygons of specifically defined areas; [::] Another fundamental piece of information to be collected is the date of the observation. This will offer the possibility of selecting specific time ranges of occurrence facilitating multi-temporal assessments, and giving the possibility to model specific outbreaks defined both spatially and temporarily; [::] The landscape where the observation is taken, in terms of different land cover categories such as natural forest, forest plantation, agroforestry, green urban areas, etc. This information is useful for delineating critical pest areas and assessment of potential spread on the basis of land cover categories; [::] A few generic items could be also easily recorded, for example, country of observation and observation method: direct, systematic survey, remote sensing, etc.; [::] Information on the organisation responsible for the observation; [::] Finally, the implementation of the database should take into consideration interoperability aspects defined by the INSPIRE Data Specification on Species Distribution - Technical Guidelines report (European Commission, 2014). This will facilitate dissemination and accessibility of datasets in the forest pest data users' community.},
  journal = {EPPO Bulletin},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13686532,~to-add-doi-URL,cameraria-ohridella,climate-change,data-integration,efdac,eppo,europe,featured-publication,fise,forest-pests,forest-resources,gbif,geospatial,geospatial-semantic-array-programming,gnu-octave,habitat-suitability,hylobius-abietis,integrated-modelling,integration-techniques,mastrave-modelling-library,maxent,niche-modelling,plant-pests,relative-distance-similarity,semantic-array-programming,semap,worldclim},
  lccn = {INRMM-MiD:c-13686532},
  number = {2}
}
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