Pythium, Globisporangium ve Phytopythium İzolatlarındaki Farklılaşan Termal Nişlerin, Sıcaklığa Bağlı Misel Gelişiminin Doğrusal Olmayan Modelleme ile Belirlenmesi

Öz

Amaç: Bu çalışmanın amacı, ekonomik öneme sahip oomycete'lerin (Pythium dissotocum, Globisporangium heterothallicum, G. intermedium, G. sylvaticum ve Phytopythium vexans) termal biyolojisini karakterize etmek için en uygun matematiksel modeli belirlemek ve bu modelleri kullanarak patojenlerin sıcaklık nişlerini ortaya çıkarmaktır. Bu sayede, hastalık epidemiyolojisi ve iklime dayalı yönetim stratejileri için temel bir anlayış oluşturulması hedeflenmektedir. Materyal ve Yöntem: Çalışmada, Pythium dissotocum, Globisporangium heterothallicum, G. intermedium, G. sylvaticum ve Phytopythium vexans'a ait 18 izolat materyal olarak kullanılmıştır. Bu izolatların misel gelişimlerine ilişkin deneysel veriler toplanmıştır. Toplanan veriler, on dört farklı doğrusal olmayan model kullanılarak analiz edilmiştir. Modellerin performansı, bilgi-teorik bir yaklaşımla karşılaştırılarak çok kriterli bir değerlendirmeye tabi tutulmuştur. Araştırma Bulguları: Yapılan karşılaştırmalar sonucunda, asimetrik fonksiyonların, özellikle de Briere2 modelinin, oomycete'lerin doğrusal olmayan termal tepkilerini tanımlamada üstün olduğu kanıtlanmıştır. Briere2 modeli, incelenen 18 izolattan 12'si için en uygun model olarak belirlenmiştir. Bu modelleme ile patojenlerin termal nişlerinde belirgin bir ayrım gözlemlenmiştir: P. vexans (Topt: 26.46–26.98°C) ve G. sylvaticum (Topt: 25.58–27.16°C) izolatları daha sıcak iklimlere adaptasyon gösterirken, G. heterothallicum (Topt: 20.65–22.06°C) daha serin termal optimumlara sahip olduğu saptanmıştır. Sonuç: Bu çalışma, oomycete'lerin termal biyolojisini modellemek için kesin bir metodolojik örnek oluşturmuş ve bu patojenler için kritik ampirik parametreler sağlamıştır. Elde edilen bulgular, tarımsal ortamlarda tahmine dayalı hastalık izleme sistemlerini geliştirmek ve iklim değişikliğine uyumlu, sağlam yönetim stratejileri formüle etmek için temel bir zemin sunmaktadır.

Anahtar Kelimeler

• Pythium benzeri funguslar
• Miselyal büyüme
• Kardinal sıcaklıklar
• Doğrusal olmayan regresyon modelleri
• Termal adaptasyon
• Model seçimi
• Oomycete ekoloji

Nonlinear Modeling of Temperature-Driven Mycelial Growth Reveals Divergent Thermal Niches in Pythium, Globisporangium, and Phytopythium Isolates

Abstract

Objective: The purpose of this research is to identify the most suitable mathematical model for characterizing the thermal biology of economically important oomycetes (Pythium dissotocum, Globisporangium heterothallicum, G. intermedium, G. sylvaticum, and Phytopythium vexans) and to use these models to define the thermal niches of these pathogens. The goal is to establish a fundamental understanding for disease epidemiology and climate-driven management strategies. Materials and Methods: In this study, 18 isolates belonging to Pythium dissotocum, Globisporangium heterothallicum, G. intermedium, G. sylvaticum, and Phytopythium vexans were used as material. Empirical data on the mycelial growth of these isolates were collected. The collected data were analyzed using fourteen different nonlinear models. The performance of the models was subjected to a multi-criteria evaluation and compared using an information-theoretic approach. Results: The comparisons unequivocally demonstrated the superiority of asymmetric functions, most notably the Briere2 model, in describing the nonlinear thermal responses of the oomycetes. The Briere2 model was identified as the optimal model for 12 of the 18 isolates studied. This modeling revealed a distinct partitioning of thermal niches: P. vexans (Topt: 26.46–26.98°C) and G. sylvaticum (Topt: 25.58–27.16°C) isolates showed clear adaptations to warmer climates, in contrast to the cooler thermal optima of G. heterothallicum (Topt: 20.65–22.06°C). Conclusion: This work establishes a definitive methodological precedent for modeling the thermal biology of oomycetes and provides critical empirical parameterization for these pathogens. The findings offer an essential foundation for advancing predictive disease forecasting and for formulating robust, climate-driven management strategies in agricultural environments.

Keywords

• Pythium-like fungi
• Mycelial growth
• Cardinal temperatures
• Nonlinear regression models
• Thermal adaptation
• Model selection
• Oomycete ecology

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