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

Yıl 2024, Cilt: 7 Sayı: 1, 62 - 82, 26.08.2024

Mehmet Akif Erdoğan

https://doi.org/10.51552/peyad.1486493

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

Teşekkür

The Kentish pullover nesting dataset that used in this study is a part of the project that was funded by a Natural Environment Research Council grant to Alasdair Houston, ICC, and John McNamara (GR3/10957), by an Orszagos Tudomanyos Kutatsi Alap grant to T.S. (T031706), and by a grant from the Hungarian Ministry of Education to Z. Barta and T.S. (FKFP-0470/2000). I would like to thank Tamas Szekely from Department of Biology and Biochemistry, University of Bath as the project coordinator and his team for sharing the dataset and providing their expertise for this study.

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

Kaynakça

• Arn, R. P., Lew, D., & Peterson, A. T. (2003). Evaluating predictive models of species’ distributions: Criteria for selecting optimal models. Ecological Modelling, 162(3), 211-232.
• Argáez, J. A., Christen, J. A., Nakamura, M., & Soberón, J. (2005). Prediction of potential areas of species distributions based on presence-only data. Environmental and Ecological Statistics, 12(1), 27-44.
• Austin, M. P. (2002). Spatial prediction of species distribution: An interface between ecological theory and statistical modelling. Ecological Modelling, 157(2-3), 101-118.
• Berberoglu, S. (1994). A research on the impact of afforestation on the coastal dune ecosystem in Eastern Mediterranean region of Turkey (Master's thesis). Institute of Science, University of Cukurova, Adana, Turkey.
• Berberoğlu, S., Şatır, O., & Atkinson, P. M. (2009). Mapping percentage tree cover from Envisat MERIS data using linear and nonlinear techniques. International Journal of Remote Sensing, 30(18), 4747-4766.
• Bock, C. E., & Webb, B. (1984). Birds as grazing indicator species in southeastern Arizona. Journal of Wildlife Management, 48(4), 1045-1049.
• Brotons, L., Thuiller, W., Araújo, M. B., & Hirzel, A. H. (2004). Presence–absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27(4), 437-448.
• Burger, J. (1985). Habitat selection in marsh-nesting birds. In M. Cody (Ed.), Habitat Selection in Birds (pp. 253-281). New York: Academic Press.
• Burger, J., & Gochfeld, M. (1987). Nest-site selection by the Herald Petrel and White-tailed Tropicbird on Round Island. Wilson Bulletin, 103(1), 126-130.
• Cai, T., Huettmann, F., & Guo, Y. (2014). Using stochastic gradient boosting to infer stopover habitat selection and distribution of Hooded Cranes Grus monacha during spring migration in Lindian, Northeast China. PLoS One, 9(2), e89913.
• Cairns, D. (1980). Nesting density, habitat structure and human disturbance as factors in Black Guillemot reproduction. Wilson Bulletin, 92(3), 352-361.
• Collias, N. E., & Collias, E. C. (1984). Nest building and bird behavior. Princeton, NJ: Princeton University Press.
• De’Ath, G., & Fabricius, K. E. (2000). Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology, 81(11), 3178-3192.
• Development Core Team. (2006). R: A language and environment for statistical computing. Royal Foundation for Statistical Computing. Retrieved from http://www.R-project.org (accessed February 10, 2006).
• Dinsmore, S. J., White, G. C., & Knopf, F. L. (2002). Advanced techniques for modeling avian nest survival. Ecology, 83(12), 3476-3488.
• Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38-49.
• Gillett, W. H., Hayward, J. L., & Stout, J. F. (1975). Effects of human activities on egg and chick mortality in a Glaucous-winged Gull colony. Condor, 77(4), 492-495.
• Good, T. P. (2002). Breeding success in the western Gull × Glaucous-winged Gull complex: The influence of habitat and nest-site characteristics. Condor, 104(2), 353-365.
• Guégan, J. F., Lek, S., & Oberdoff, T. (1998). Energy availability and habitat heterogeneity to predict global riverine fish diversity. Nature, 391(6664), 382-384.
• Guisan, A., & Zimmerman, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2-3), 147-186.
• Hagan, M. T., Demuth, H., & Beale, M. (1996). Neural network design. PWS Publishing.
• Helsel, D. R., & Hirsch, R. M. (1992). Statistical methods in water resources. Elsevier Science Publishers . Hirzel, A. H., & Guisan, A. (2002). Which is the optimal sampling strategy for habitat suitability modelling? Ecological Modelling, 157(2-3), 331-341.
• Holeńa, M., & Baerns, M. (2003). Experimental design for combinatorial and high throughput materials development. In J. N. Cawse (Ed.), Wiley, New York (p. 163).
• Huang, C., Yang, L., Homer, C., Coan, M., Rykhus, R., Zhang, Z., Wylie, B., Hegge, K., Zhu, Z., Lister, A., Hoppus, M., Tymcio, R., DeBlander, L., Cooke, W., McRoberts, R., Wendt, D., & Weyermann, D. (2001). Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping. Thirty-Fifth Annual Midwest Forest Mensurationists Meeting and the Third Annual Forest Inventory and Analysis Symposium, October 17-19, 2001, Traverse City, MI.
• Isaaks, E. H., & Srivastava, R. M. (1988). An introduction to applied geostatistics. Oxford University Press.
• Jiguet, F., Julliard, R., Couvet, D., & Petiau, A. (2005). Modeling spatial trends in estimated species richness using breeding bird survey data: A valuable tool in biodiversity assessment. Biodiversity and Conservation, 14(14), 3305-3324.
• Johnson, J. B., & Omland, K. S. (2004). Model selection in ecology and evolution. Trends in Ecology & Evolution, 19(2), 101-108.
• Kantrud, H. A., & Kologiski, R. L. (1982). Effects of soils and grazing on breeding birds of uncultivated upland grasslands on the northern Great Plains. US Fish and Wildlife Service, Wildlife Resource Report 15.
• Kerns, B. K., & Ohmann, J. L. (2004). Evaluation and prediction of shrub cover in coastal Oregon forests (USA). Ecological Indicators, 4(2), 83-98.
• Kleijnen, J. P. C., & van Beers, W. C. M. (2005). Robustness of kriging when interpolating in random simulation with heterogeneous variances: Some experiments. European Journal of Operational Research, 165(3), 826-834.
• Lack, D. (1968). Ecological adaptations for breeding in birds. Methuen.
• Lark, R. M. (2003). Two robust estimators of the cross variogram for multivariate geostatistical analysis of soil properties. European Journal of Soil Science, 54(1), 187-202.
• Lendvai, Á. Z., Kis, J., Székely, T., & Cuthill, I. C. (2004). An investigation of mate choice based on manipulation of multiple ornaments in the Kentish plover. Animal Behaviour, 67(4), 703-709.
• Li, P., & Martin, T. E. (1991). Nest-site selection and nesting success of cavity-nesting birds in high-elevation forest drainages. Auk, 108(2), 405-418.
• Li, Z., Zhang, Y., Schilling, K., & Skopec, M. (2006). Co-kriging estimation of daily suspended sediment loads. Journal of Hydrology, 327(3-4), 389-398.
• Lippmann, R. P. (1987). An introduction to computing with neural nets. Institute of Electrical and Electronic Engineers ASSP Magazine, 2(4), 4-22.
• Magnin, G., & Yarar, M. (1997). Important bird areas in Turkey. DHKD.
• Manel, D., Dias, J. M., Buckton, S. T., & Ormerod, S. J. (1999). Alternative methods for predicting species distribution: An illustration with Himalayan river birds. Journal of Applied Ecology, 36(5), 734-747.
• Manel, S., Williams, H. C., & Ormerod, S. J. (2001). Evaluating presence-absence models in ecology: The need to account for prevalence. Journal of Applied Ecology, 38(5), 921-931.
• Martin, T. E. (1987). Food as a limit on breeding birds: A life-history perspective. Annual Review of Ecology and Systematics, 18(1), 453-487.
• Martin, T. L. (1988). Processes organizing open-nesting bird assemblages: Competition or nest predation? Evolutionary Ecology, 2(1), 37-50.
• Marzluff, J. M., & Neatherlin, E. (2006). Corvid response to human settlements and campgrounds: causes, consequences, and challenges for conservation. Biological conservation, 130(2), 301-314.
• Mastrorillo, S., Lek, S., Dauba, F., & Belaud, A. (1997). The use of artificial neural networks to predict the presence of small-bodied fish in a river. Freshwater Biology, 38(2), 237-246.
• Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (Eds.). (1994). Machine learning, neural and statistical classification. Ellis Horwood.
• Miller, J., & Franklin, J. (2002). Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modelling, 157(2-3), 227-247.
• Moore, I., Grayson, R., & Ladson, A. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3-30.
• Mönkkönen, M., Husby, M., Tornberg, R., Helle, P., & Thomson, R. L. (2007). Predation as a landscape effect: the trading off by prey species between predation risks and protection benefits. Journal of animal ecology, 76(3), 619-629.
• Myers, D. E. (1982). Matrix formulation of co-kriging. Mathematical Geology, 14(3), 249-267.
• Murray, B. G. (2000). Measuring annual reproductive success in birds. Condor, 102(2), 470-473.
• Nisbet, I. C. T., & Cohen, M. E. (1975). Asynchronous hatching in Common and Roseate terns: Sterna hirundo and S. dougallii. Ibis, 117(1), 374-379.
• Nisbet, I. C. T. (1978). Dependence of fledging success on egg-size, egg composition and parental performance in Common and Roseate terns, Sterna hirundo and S. dougallii. Ibis, 120(2), 207-215.
• Nisbet, I. C. T., Wilson, K. J., & Broad, W. A. (1978). Common terns raise young after death of their mates. Condor, 80(1), 106-109.
• Noszály, G., & Székely, T. (1993). Clutch and egg-size variation in the Kentish Plover (Charadrius alexandrinus) during the breeding season. Aquila, 100, 161-179.
• Özesmi, S. L., & Özesmi, U. (1999). An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling, 116(1), 15-31.
• Paola, J. D., & Schowengerdt, R. A. (1997). The effect of neural network structure on a multispectral land use/land cover classification. Photogrammetric Engineering and Remote Sensing, 63(5), 535-544.
• Partridge, L. (1978). Habitat selection. In J. R. Krebs & N. B. Davies (Eds.), Behavioural ecology: An evolutionary approach (pp. 351-376). Blackwell Scientific Publications.
• Pearce, J., & Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133(3), 225-245.
• Ricklefs, R. E. (1969). An analysis of nesting mortality in birds. Smithsonian Contributions to Zoology, 9, 1-48.
• Riley, T. Z., Davis, C. A., Ortiz, M., & Wisdom, M. J. (1992). Vegetative characteristics of successful and unsuccessful nests of Lesser Prairie-Chicken. Journal of Wildlife Management, 56(2), 383-387.
• Rulequest Research. (2008). An overview of Cubist. Rulequest Research. Retrieved from http://www.rulequest.com/cubist-unix.html
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations (pp. 318-362). MIT Press.
• Segurado, P., & Araújo, M. B. (2004). An evaluation of methods for modelling species distributions. Journal of Biogeography, 31(10), 1555-1568.
• Showler, D. A., Stewart, G. B., Sutherland, W. J., & Pullin, A. S. (2010). What is the impact of public access on the breeding success of ground-nesting and cliff-nesting birds. Systematic Review, 16.
• Shrubb, M. (1990). Effects of agricultural change on nesting lapwings Vanellus vanellus in England and Wales. Bird Study, 37(2), 115-127.
• Skutch, A. R. (1985). Clutch size, nesting success, and predation on nests of Neotropical birds, reviewed. In R. A. Buckley, M. S. Foster, E. S. Morton, R. S. Ridgely, & R. G. Buckley (Eds.), Neotropical ornithology (pp. 575-594). Ornithological Monographs No. 36.
• Stephens, S. E., Koons, D. N., Rotella, J. J., & Willey, D. W. (2004). Effects of habitat fragmentation on avian nesting success: a review of the evidence at multiple spatial scales. Biological conservation, 115(1), 101-110.
• Székely, T. (1999). Report on ecology and behaviour of birds at Tuzla Lake in 1999. Centre for Behavioural Biology, School of Biological Sciences, University of Bristol.
• Szentirmai, I., & Székely, T. (2004). Diurnal variation in nest material use by the Kentish Plover Charadrius alexandrinus. Ibis, 146(3), 535-537.
• Thuiller, W. (2003). BIOMOD – Optimising predictions of species distributions and projecting potential future shifts under global change. Global Change Biology, 9(8), 1353-1362.
• Vander Haegen, W. M. (2007). Fragmention by agriculture influences reproductive success of birds in a shrubsteppe landscape. Ecological Applications, 17(3), 934-947.
• Yılmaz, K. T., Alphan, H., Kosztolányi, A., Ünlükaplan, Y., & Derse, M. A. (2020). Coastal wetland monitoring and mapping along the Turkish Mediterranean: determining the impact of habitat inundation on breeding bird species. Journal of Coastal Research, 36(5), 961-972.
• Walley, W. J., & Fontama, V. N. (1998). Neural network predictors of average score per taxon and number of families at unpolluted sites in Great Britain. Water Resources, 32(3), 613-622.
• Webb, S. L., Olson, C. V., Dzialak, M. R., Harju, S. M., Winstead, J. B., & Lockman, D. (2012). Landscape features and weather influence nest survival of a ground-nesting bird of conservation concern, the greater sage-grouse, in human-altered environments. Ecological Processes, 1, 1-15.
• Wu, C., & Murray, A. T. (2005). A co-kriging method for estimating population density in urban areas. Computers, Environment and Urban Systems, 29(5), 558-579.
• Zaniewski, A. E., Lehmann, A., & Overton, J. M. (2002). Predicting species spatial distributions using presence-only data: A case study of native New Zealand ferns. Ecological Modelling, 157(2-3), 261-280.