Ips sexdentatus’un Duyarlılığının Maksimum Entropi (MaxEnt) ile Modellenmesi

Year 2024, Volume: 26 Issue: 2, 16 - 27, 23.04.2024

Gonca Ece Özcan

https://doi.org/10.24011/barofd.1387342

Abstract

İklim değişimi ve buna bağlı faktörlerden en çok etkilenen ormanlardır. İklim değişikliği, konukçu ağaçların ve bunlarla ilişkili olan zararlıların dağılımlarında değişikliğe neden olmaktadır. Ekoloji ve koruma alanındaki planlamacılara yol gösterecek uygulamalar için türlerin coğrafi dağılımlarını belirleyen tahmine dayalı modeller önemlidir. Orman ekosistemlerinde ciddi olumsuzluklara neden olan kabuk böceklerinin her yıl artarak devam eden zararlarının önemli sonuçlar meydana getireceği beklenmektedir. Bu nedenle orman ekosistemlerinde bulunan kabuk böceği türlerinin potansiyel dağılımlarının belirlenmesi sürdürülebilir orman yönetimi açısından oldukça önemlidir. Bu türlerin salgınlarını iklim, topoğrafik ve meşcere parametreleri önemli ölçüde etkilemektedir. Bu çalışmada, Maksimum Entropi (MaxEnt) yaklaşımı kullanılarak 19 farklı biyoiklimsel değişken ile kapalılık, yükselti ve eğim değişkenlerini dikkate alarak Ips sexdentatus’un zararına ilişkin potansiyel duyarlılık haritası oluşturulmuştur. Modelin doğruluğu alıcı çalışma karakteristiği (ROC) analizi ile değerlendirilmiş eğitim verilerinde eğri altında kalan alan (Area Under Curve, (AUC)) 0,846; test verilerinde ise 0,855 olarak hesaplanmıştır. Ips sexdentatus’un duyarlılık haritasında model sonucunu en çok etkileyen parametrenin kapalılık olduğu ve modelin %68.5’ini oluşturduğu belirlenmiştir. Bunun yanında kapalılık, eğim ve en nemli ayın yağış miktarı değişkenlerinin toplu olarak modelin %88.4’ünü oluşturduğu görülmüştür. Ayrıca, çalışma alanının % 51.6’sı Ips sexdentatus istilası açısından riskli kategoride yer almaktadır. Bu çalışmanın sonuçları Ips sexdentatus’un izlenmesi ve mücadele stratejilerinin belirlenmesine katkı sağlayacaktır. Aynı zamanda diğer salgın yapma potansiyeline sahip kabuk böceği türlerinin yönetimi için bir öngörü oluşturacaktır.

Keywords

Kabuk böceği, MaxEnt, duyarlılık haritası, iklim değişimi. ROC analizi, Ips sexdentatus

Modeling the Susceptibility of Ips sexdentatus with Maximum Entropy (MaxEnt)

Abstract

Forests are most affected by climate change and related factors. Climate change causes changes in the distribution of host trees and their associated pests. Predictive models that determine the spatial distributions of species are important for applications that will guide planners in the field of ecology and conservation. It is predicted that the ever-increasing damage of bark beetles, which cause significant negativities in forest ecosystems, will have serious consequences. Therefore, determining the potential distributions of bark beetle species in forest ecosystems is important for sustainable forest management. Climate, topographic and stand parameters significantly affect the epidemics of these species. In this study, a potential susceptibility map for the damage of Ips sexdentatus was created using the Maximum Entropy (MaxEnt) approach, taking into account 19 different bioclimatic, crown closer, elevation, and slope variables. The accuracy of the model was evaluated by receiver operating characteristic (ROC) analysis. AUC was 0.846 in the training data, and it was calculated as 0.855 in the test data. In the susceptibility map of Ips sexdentatus, it was determined that the variable that most affected the model result was crown closure, which constituted 68.5% of the model. In addition, it was observed that the variables of crown closure, slope, and precipitation of the wettest month collectively included 88.4% of the model. In addition, 51.6% of the study area is in the risk category regarding I. sexdentatus invasion. The results of this study will contribute to monitoring Ips sexdentatus and determining control strategies. It will also provide insight for the management of other bark beetle species with epidemic potential.

Keywords

Bark beetle, MaxEnt, susceptibility map, climate change. ROC analysis, Ips sexdentatus

References

• Bentz, B.J., Régnière J., Fettig, C.J., Hansen, E.M., Hayes, J.L., Hicke, J.A., Kelsey, R.G., Negrón, J.F., Seybold, S.J. (2010). Climate change and bark beetles of the western United States and Canada: direct and indirect effects. BioScience, 60 (8), 602–613. https://doi.org/10.1525/bio.2010.60.8.6
• Buotte, P.C., Hicke, J.A., Preisler, H.K., Abatzoglou, j.T., Raffa, K.F., Logan, J.A. (2016). Climate influences on whitebark pine mortality from mountain pine beetle in the Greater Yellowstone Ecosystem. Ecological Applications, 26(8), 2507-2524. https://doi.org/10.1002/eap.1396
• Choi, W.I., Park, Y S. (2019). Monitoring, assessment and management of forest insect pests and diseases. Forests, 10(10), 865. https://doi.org/10.3390/f10100865
• Craig, E., Bland, R., Ndirangu, J., Reilly, J.J. (2014). Use of mid-upper arm circumference for determining overweight and overfatness in children and adolescents. Archives of disease in childhood, 99(8), 763-766. https://doi:10.1136/archdischild-2013-305137
• Dale, V. H., Joyce, L.A., McNulty, S.M., Neilson, R.P., Ayres, M.P., Flannigan, M.D., Hanson, P.J., Irland, L.C., Lugo, A.E., Peterson, C.J., Simberloff, D., Swanson, F.J., Stocks, B.J., Wotton, B.M. 2001. Climate change and forest disturbances: climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides. BioScience, 51(9), 723-734. https://doi.org/10.1641/0006-3568(2001)051[0723:CCAFD]2.0.CO;2
• Elith, J., Kearney, M., Phillips, S. (2010). The art of modelling range‐shifting species.Methods in Ecology and Evolution, 1(4), 330-342. https://doi.org/10.1111/j.2041-210X.2010.00036.x
• Evangelista, P.H., Kumar, S., Stohlgren, T.J., Young, N.E. (2011). Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US. Forest Ecology and Management, 262(3), 307-316. https://doi.org/10.1016/j.foreco.2011.03.036
• Fitzgibbon, A., Pisut, D., Fleisher, D. (2022). Evaluation of Maximum Entropy (Maxent) machine learning model to assess relationships between climate and corn suitability. Land, 11(9), 1382. https://doi.org/10.3390/land11091382
• Gil, L., Pajares, J.A. (1986). Los escolıtidos de las conıferas en la Penınsula Ibérica. Monografıas INIA, (53), 194. González-Hernández, A., Morales-Villafaña, R., Romero-Sánchez, M.E., Islas-Trejo, B., Pérez-Miranda, R. (2020). Modelling potential distribution of a pine bark beetle in Mexican temperate forests using forecast data and spatial analysis tools. Journal of Forestry Research, 31(2), 649-659. https://doi.org/10.1007/s11676-018-0858-4
• Hansen, B.B., Grøtan, V., Herfindal, I., Lee, A.M. (2020). The Moran effect revisited: spatial population synchrony under global warming. Ecography, 43(11), 1591-1602. https://doi.org/10.1111/ecog.04962 Jactel, H., Koricheva, J., Castagneyrol, B. (2019). Responses of forest insect pests to climate change: not so simple. Current Opinion in Insect Science, 35, 103-108. https://doi.org/10.1016/j.cois.2019.07.010
• Jaime, L., Batllori, E., Margalef-Marrase, J., Navarro, M. Á. P., Lloret, F. (2019). Scots pine (Pinus sylvestris L.) mortality is explained by the climatic suitability of both host tree and bark beetle populations. Forest Ecology and Management, 448, 119-129. https://doi.org/10.1016/j.foreco.2019.05.070
• Jeger, M., Bragard, C., Caffier, D., Candresse, T., Chatzivassiliou, E., Dehnen-Schmutz, K., Gilioli, G., Miret, J.A.J., MacLeod, A., Navarro, M.N., Niere, B., Parnell, S., Potting, R., Rafoss, T., Rossi, V., Urek, G., Van Bruggen, S., Werf, W.V., West, J., Winter, S., Kertész, V., Aukhojee, M., Grégoire, J.C. (2017). Pest categorisation of Ips sexdentatus. EFSA Journal, 15(11), 4999. https://doi.org/10.2903/j.efsa.2017.4999.
• Jenkins, M.J., Hebertson, E.G., Munson, A.S. (2014). Spruce beetle biology, ecology and management in the Rocky Mountains: an addendum to spruce beetle in the Rockies. Forests, 5(1), 21-71. https://doi.org/10.3390/f5010021
• Johnson, D.M., Haynes, K.J. (2023). Spatiotemporal dynamics of forest insect populations under climate change. Current Opinion in Insect Science, 53, 101020. https://doi.org/10.1016/j.cois.2023.101020
• Kamińska, A., Lisiewicz, M., Kraszewski, B., Stereńczak, K. (2021). Mass outbreaks and factors related to the spatial dynamics of spruce bark beetle (Ips typographus) dieback considering diverse management regimes in the Białowieża forest. Forest Ecology and Management, 498, 119530. https://doi.org/10.1016/j.foreco.2021.119530
• Li, Y., Johnson, A. J., Gao, L., Wu, C., Hulcr, J. (2021). Two new invasive Ips bark beetles (Coleoptera: Curculionidae) in mainland China and their potential distribution in Asia. Pest Management Science, 77(9), 4000-4008. https://doi.org/10.1002/ps.6423
• Lissovsky, A.A., Dudov, S.V. (2021). Species-distribution modeling: advantages and limitations of its application. 2. MaxEnt. Biology Bulletin Reviews, 11(3), 265-275.
• Luo, Y., Ogle, K., Tucker, C., Fei, S., Gao, C., LaDeau, S., Clark, J.S., Schimel, D.S. (2011). Ecological forecasting and data assimilation in a data-rich era. Ecological Applications, 21, 1429–1442. https://doi: 10.1890/09-1275.1
• Marini, L., Ayres, M.P., Battisti, A., Faccoli, M. (2012). Climate affects severity and altitudinal distribution of outbreaks in an eruptive bark beetle. Climatic Change, 115, 327-341.
• Méndez-Encina, F.M., Méndez-González, J., Mendieta-Oviedo, R., López-Díaz, J.Ó., Nájera-Luna, J.A. (2021). Ecological niches and suitability areas of three host pine species of bark beetle Dendroctonus mexicanus Hopkins. Forests, 12(4), 385. https://doi.org/10.3390/f12040385
• Moat, J., Williams, J., Baena, S., Wilkinson, T., Gole, T.W., Challa, Z.K., Demissew, S., Davis, A.P. (2017). Resilience potential of the Ethiopian coffee sector under climate change. Nature Plants, 3, 17081
• Muttaqin, L. A., Murti, S. H., Susilo, B. (2019, November). MaxEnt (Maximum Entropy) model for predicting prehistoric cave sites in Karst area of Gunung Sewu, Gunung Kidul, Yogyakarta. In Sixth Geoinformation Science Symposium (Vol. 11311, pp. 87-95). SPIE.
• Nardi, D., Jactel, H., Pagot, E., Samalens, J.C., Marini, L. (2023). Drought and stand susceptibility to attacks by the European spruce bark beetle: A remote sensing approach. Agricultural and Forest Entomology, 25(1), 119-129. https://doi.org/10.1111/afe.12536
• Negrete, L., Lenguas Francavilla, M., Damborenea, C., Brusa, F. (2020). Trying to take over the world: potential distribution of Obama nungara (Platyhelminthes: Geoplanidae), the Neotropical land planarian that has reached Europe. Global Change Biology, 26, 4907–4918. https://doi.org/10.1111/gcb.15208
• Økland, B., Flø, D., Schroeder, M., Zach, P., Cocos, D., Martikainen, P., Siitonen, J., Mandelshtam, M.Y., . Musolin, D.L., Neuvonen, S., Vakula, J., Nikolov, C., . Lindelöw, Å., Voolma, K. (2019). Range expansion of the small spruce bark beetle Ips amitinus: a newcomer in northern Europe. Agricultural and Forest Entomology, 21(3), 286-298. https://doi.org/10.1111/afe.12331
• Olivera, L., Minghetti, E., Montemayor, S.I. (2020). Ecological niche modeling (ENM) of Leptoglossus clypealis a new potential global invader: Following in the footsteps of Leptoglossus occidentalis? Bulletin Entomological Research, 111, 289–300
• Oymen, T. (1992). The forest scolytidae of Turkey. Journal of Faculty of Forestry. Istanbul U. A, 42, I, 77–91.
• Özcan, G.E., Eroğlu, M., Alkan-Akıncı, H. (2011). Use of pheromone-baited traps for monitoring Ips sexdentatus (Boerner) (Coleoptera: Curculionidae) in oriental spruce stands. African Journal of Biotechnology, 10, (72), 16351-16360. https://doi.org/10.5897/AJB11.1709
• Özcan, G.E., Sivrikaya, F., Sakici, O.E., Enez, K. (2022). Determination of some factors leading to the infestation of Ips sexdentatus in crimean pine stands. Forest Ecology and Management, 519, 120316. https://doi.org/10.1016/j.foreco.2022.120316
• Peterson, A. T., Papeş, M., Soberón, J. (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological modelling, 213(1), 63-72. https://doi:10.1016/j.ecolmodel.2007.11.008
• Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E., Blair, M.E. (2017). Opening the black box: an open-source release of Maxent. Ecography, 40: 887–893. https://doi.org/10.1111/ecog.03049
• Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
• Phillips, S.J., Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
• Polo, T.C.F., Miot, H.A. (2020). Use of ROC curves in clinical and experimental studies. Jornal Vascular Brasileiro, 19. https://doi:10.1590/1677-5449.200186
• Romon, P., Zhou, X., Iturrondobeitia, J.C., Wingfield, M.J., Goldarazena, A. (2007). Ophiostoma species (Ascomycetes: Ophiostomatales) associated with bark beetles (Coleoptera: Scolytinae) colonizing Pinus radiata in northern Spain. Canadian Journal of Microbiology, 53(6), 756-767. https://doi.org/10.1139/W07-001
• Rossi, J.P., Samalens, J.C., Guyon, D., van Halder, I., Jactel, H., Menassieu, P., Piou, D. (2009). Multiscale spatial variation of the bark beetle Ips sexdentatus damage in a pine plantation forest (Landes de Gascogne, Southwestern France). Forest Ecology and Management, 257, 1551–1557.https://doi.org/10.1016/j.foreco.2008.12.012
• Salinas-Moreno, Y., Mendoza, M.G., Barrios, M.A., Cisneros, R., Macias-Samano, J., Zuniga, G. (2004). Aerography of the genus Dendroctonus (Coleoptera: Curculionidae: Scolytinae) in Mexico. Journal of Biogeography, 31, 1163-1177. https://doi.org/10.1111/j.1365-2699.2004.01110.x
• Sarikaya, O., Karaceylan, I.B., Sen, I. (2018). Maximum entropy modeling (maxent) of current and future distributions of Ips mannsfeldi (Wachtl, 1879) (Curculionidae: Scolytinae) in Turkey. Applied Ecology and Environmental Research, 16(3), 2527-2535. http://dx.doi.org/10.15666/aeer/1603_25272535
• Schelhaas, M., Nabuurs, G., Schuck, A. (2003). Natural dis-turbances in the European forests in the 19th and 20th centu-ries. Global Change Biology, 9:1620–1633. http://doi: 10.1046/j.1529-8817.2003.00684.x
• Seidl, R., Rammer, W., Jeager, D., Lexer, M.J. (2008). Impact of bark beetle (Ips typographus L.) disturbance on timber production and carbon sequestration in different management strategies under climate change. Forest Ecology and Management, 256(3), 209–20. https://doi.org/10.1016/j.foreco.2008.04.002
• Sivrikaya, F., & Özcan, G. E. (2023). Modeling spatial distribution of bark beetle susceptibility using the maximum entropy approach. Intercontinental Geoinformation Days, 6, 105-109.
• Sivrikaya, F., Özcan, G. E., Enez, K. (2023). Predicting the susceptibility to Pityokteines curvidens using GIS with analytical hierarchy process and, maximum entropy models in fir forests. In Analytic Hierarchy Process-Models, Methods, Concepts, and Applications. IntechOpen. https://doi.org/10.5772/intechopen.1001074
• Sproull, G.J., Bukowski, M., McNutt, N., Zwijacz-Kozica, T., Szwagrzyk, J. (2017). Landscape-level spruce mortality patterns and topographic forecasters of bark beetle outbreaks in managed and unmanaged forests of the Tatra Mountains. Polish Journal of Ecology, 65, 24–37. https://doi.org/10.3161/15052249PJE2017.65.1.003
• Steven J. Phillips, Miroslav Dudík, Robert E. Schapire. [Internet] Maxent software for modeling species niches and distributions (Version 3.4.1). Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2023-10-26.
• U.S. Geological Survey. https://earthexplorer.usgs.gov/, 2021. (accessed 3 March 2023).
• Volney, W.J.A., Fleming, R.A. (2000). Climate change and impacts of boreal forest insects. Agriculture, Ecosystems &. Environment, 82 (1-3), 283–294. https://doi.org/10.1016/S0167-8809(00)00232-2
• West, A.M., Kumar, S., Brown, C.S., Stohlgren, T.J., Bromberg, J. (2016). Field validation of an invasive species Maxent model. Ecological Informatics, 36, 126-134. https://doi.org/10.1016/j.ecoinf.2016.11.001
• Williams, K.K., McMillin, J.D., DeGomez, T.E., Clancy, K.M., Miller, A. (2014). Influence of elevation on bark beetle (Coleoptera: Curculionidae, Scolytinae) community structure and flight periodicity in ponderosa pine forests of Arizona. Environmental Entomology, 37 (1), 94-109. https://doi.org/10.1603/0046-225X(2008)37[94:IOEOBB]2.0.CO;2
• Winter, M.B., Baier, R., Ammer, C. (2015). Regeneration dynamics and resilience of unmanaged mountain forests in the Northern Limestone Alps following bark beetle induced spruce dieback. European Journal of Forest Research, 134, 949–968. https://doi.org/10.1007/s10342-015-0901-3
• Worldclim 2023. Global Climate Data, Version 2 (Free climate data for ecological modeling and GIS). http://worldclim.org/version2.
• Wu, Z., Gao, T., Luo, Y., Shi, J. (2022). Prediction of the global potential geographical distribution of Hylurgus ligniperda using a maximum entropy model. Forest Ecosystems, 9:100042. https://doi.org/10.1016/j.fecs.2022.100042
• Yates, K.L., Bouchet, P.J., Caley, M.J., Mengersen, K., Randin, C. F., Parnell, S., Fielding, A.H., Bamford, A.J., Ban, S., Barbosa, A.M., Dormann, C.F., Elith, J., Embling, C.B., Ervin, G.N., Fisher, R., Gould, S., Graf, R.F., Gregr, E.J., Halpin, P.N., Heikkinen, R.K., Heinänen, S., Jones, A.R., Krishnakumar, P.K., Lauria, V., Lozano-Montes, H., Mannocci, L., Mellin, C., Mesgaran, M.B., Moreno-Amat, E., Mormede, S., Novaczek, E., Oppel, S., Crespo, G.O., Peterson, A.T., Rapacciuolo, G., Roberts, J.J., Ross, R.E., Scales, K.L., Schoeman, D., Snelgrove, P., Sundblad, G., Thuiller, W., Torres, L.G., Verbruggen, H., Wang, L., Wenger, S., Whittingham, M.J., Zharikov, Y., Zurell, D., Sequeira, A.M.M. (2018). Outstanding challenges in the transferability of ecological models. Trends in Ecology & Evolution,33(10), 790-802.
• Yusup, S., Sulayman, M., Ilghar, W., Zhang, Z. X. (2018). Prediction of potential distribution of Didymodon (Bryophyta, Pottiaceae) in Xinjiang based on the MaxEnt model. Plant Science Journal, 36(4), 541-553.
• Yüksel, B., Akbulut, S. (2005). Doğu Ladini ormanlarında Ips sexdentatus (Boern.)'un doğal düşmanlarının belirlenmesi. Journal of Faculty of Forestry, Istanbul University. 55, (2), 59-70.