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

Yıl 2024, Cilt: 26 Sayı: 2, 16 - 27, 23.04.2024

Gonca Ece Özcan

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

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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