Sulak Alan Peyzajları Örneğinde Doğrusal ve Doğrusal Olmayan Teknikler Kullanarak Kuş Habitat Uygunluğunun Haritalanması

Öz

Habitat kalitesi, sulak alanlar gibi hassas peyzajların korunmasını adına geliştirilecek yaban hayatı yönetim süreçleri için kritik öneme sahiptir. CBS'deki gelişmelerle birlikte, habitat modellemesi artık fauna varlığı ve habitat uygunluğunu etkili bir şekilde tahmin edebilecek seviyelere ulaşmıştır. Bu kapsamda bu çalışma ile Tuzla Lagünü'nde yaşayan Akça cılıbıt kuş türünün habitat uygunluğunu birden fazla teknik kullanarak modellemeyi ve haritalamayı amaçlanmıştır. Çalışma kapsamında 293 yuvadan toplanmış ve yumurtlama zamanı, yumurta hacmi ve yuva kaderi gibi üreme başarısı ölçütlerini içeren veri seti analiz edilmiştir. Çoklu doğrusal regresyon, co-kriging, yapay sinir ağları ve karar ağaçları dahil olmak üzere mekansal habitat modelleme teknikleri kullanılmıştır. Yöntem doğruluklarının karşılaştırılması sonucunda yumurtlama zamanı için tüm yöntemler düşük doğrulukta sonuçlar üretmiş olmakla beraber karar ağacı, yumurta hacmi için co-kriging, yumurta başarısı içinse doğrusal regresyon en yüksek doğruluğa ulaşmıştır. Yuva kaderi için ise hem ikili lojistik regresyon hem de yapay sinir ağları yöntemleri %75 doğrulukla en iyi tahmine ulaşmıştır.

Anahtar Kelimeler

• Habitat Uygunluğu
• Habitat Modelleme
• Sulak Alan Peyzajları
• Kuş Habitatı
• Uzaktan Algılama

Mapping Avian Habitat Suitability Using Linear and Non-Linear Techniques in the Case of Wetland Landscapes

Öz

Habitat quality is crucial for wildlife management that impacts the conservation of sensitive landscapes such as wetlands. With advancements in GIS, habitat modelling now effectively predicts species occurrences and habitat suitability. This study aims to model and map habitat suitability for case bird species of Kentish plover in Tuzla Lagoon using multiple techniques. Kentish plover nesting data were collected from 293 nests, and reproductive success measures such as lay date, egg volume, and nest fate were analysed. Spatial habitat modelling techniques, including regression, co-kriging, artificial neural networks, and decision trees, were used with IKONOS imagery and ground data. The overall prediction accuracies were poor for lay date across all techniques, with the decision tree being the most accurate, while egg volume was best predicted by co-kriging, egg success by linear regression, and nest fate by both binomial logistic regression and ANN with 75% accuracy.

Anahtar Kelimeler

• Habitat Suitability
• Habitat Modelling
• Wetland Landscapes
• Avian Habitat
• Remote Sensing

Kaynakça

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