References

Aebischer, A. and Scherler, P. 2021. Der Rotmilan: Ein Greifvogel im Aufwind, Bern, Switzerland, Haupt Verlag.
Blas, J., Sergio, F. and Hiraldo, F. 2009. Age-related improvement in reproductive performance in a long-lived raptor: a cross-sectional and longitudinal study. – Ecography 32: 647-657.
Bowgen, K. M., Dodd, S. G., Lindley, P., Burton, N. H. and Taylor, R. C. 2022. Curves for Curlew: Identifying Curlew breeding status from GPS tracking data. – Ecology and Evolution 12: e9509.
Breiman, L. 2001. Random forests. – Machine Learning 45: 5-32.
Buechley, E. R., Oppel, S., Efrat, R., Phipps, W. L., Carbonell Alanís, I., Álvarez, E., Andreotti, A., Arkumarev, V., Berger-Tal, O., Bermejo Bermejo, A., Bounas, A., Ceccolini, G., Cenerini, A., Dobrev, V., Duriez, O., García, J., García-Ripollés, C., Galán, M., Gil, A., Giraud, L., Hatzofe, O., Iglesias-Lebrija, J. J., Karyakin, I., Kobierzycki, E., Kret, E., Loercher, F., López-López, P., Miller, Y., Mueller, T., Nikolov, S. C., de la Puente, J., Sapir, N., Saravia, V., Şekercioğlu, Ç. H., Sillett, T. S., Tavares, J., Urios, V. and Marra, P. P. 2021. Differential survival throughout the full annual cycle of a migratory bird presents a life-history trade-off. – Journal of Animal Ecology 90: 1228-1238.
Carneiro, A. P. B., Dias, M. P., Oppel, S., Pearmain, E. J., Clark, B. L., Wood, A. G., Clavelle, T. and Phillips, R. A. 2022. Integrating immersion with GPS data improves behavioural classification for wandering albatrosses and shows scavenging behind fishing vessels mirrors natural foraging. – Animal Conservation 25: 627-637.
Catlin, D. H., Gibson, D., Hunt, K. L., Friedrich, M. J., Weithman, C. E., Karpanty, S. M. and Fraser, J. D. 2019. Direct and indirect effects of nesting density on survival and breeding propensity of an endangered shorebird. – Ecosphere 10: e02740.
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J. and Lawler, J. J. 2007. Random Forests for classification in ecology. – Ecology 88: 2783-2792.
Eisaguirre, J. M., Williams, P. J., Brockman, J. C., Lewis, S. B., Barger, C. P., Breed, G. A. and Booms, T. L. 2023. A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data. – Methods in Ecology and Evolution n/a.
Ferrer, M., Otalora, F. and GarcÍa-Ruiz, J. M. 2004. Density-dependent age of first reproduction as a buffer affecting persistence of small populations. – Ecological Applications 14: 616-624.
García-Macía, J., Vidal-Mateo, J., De La Puente, J., Bermejo, A., Raab, R. and Urios, V. 2022a. Seasonal differences in migration strategies of Red Kites (Milvus milvus ) wintering in Spain. – Journal of Ornithology 163: 27-36.
García-Macía, J., Vidal-Mateo, J., de la Puente, J., Bermejo, A. and Urios, V. 2022b. Spatial ecology of the Red Kite (Milvus milvus ) during the breeding period in Spain. – Ornis Fennica 99: 150-162.
Grömping, U. 2009. Variable importance assessment in regression: Linear regression versus Random Forest. – American Statistician 63: 308-319.
Harris, M. P., Heubeck, M., Bogdanova, M. I., Newell, M. A., Wanless, S. and Daunt, F. 2020. The importance of observer effort on the accuracy of breeding success estimates in the Common Guillemot Uria aalge. – Bird Study 67: 93-103.
Heiniger, N., Weibel, R., Grüebler, M. and Scherler, P. 2020. Identifying anthropogenic feeding sites from GPS tracking data: A case study for red kites (Milvus milvus) in Western Switzerland. Master’s Thesis, Department of Geography, University of Zurich, Zurich, Switzerland.
Hinde, A. 1956. The biological significance of the territories of birds. – Ibis 98: 340-369.
Hothorn, T., Müller, J., Schröder, B., Kneib, T. and Brandl, R. 2011. Decomposing environmental, spatial, and spatiotemporal components of species distributions. – Ecological Monographs 81: 329-347.
Hötker, H., Mammen, K., Mammen, U. and Rasran, L. 2017. Red kites and wind farms—telemetry data from the core breeding range. – In: Köppel, J. e. W. E. a. W. I. (ed.) Wind Energy and Wildlife Interactions.Springer, Cham, pp. 3-15.
Janitza, S., Strobl, C. and Boulesteix, A.-L. 2013. An AUC-based permutation variable importance measure for random forests. – BMC bioinformatics 14: 119.
Katzenberger, J., Gottschalk, E., Balkenhol, N. and Waltert, M. 2019. Long-term decline of juvenile survival in German Red Kites. – Journal of Ornithology 160: 337-349.
Katzenberger, J., Gottschalk, E., Balkenhol, N. and Waltert, M. 2021. Density-dependent age of first reproduction as a key factor for population dynamics: stable breeding populations mask strong floater declines in a long-lived raptor. – Animal Conservation 24: 862-875.
Kaufmann, J. H. 1983. On the definitions and functions of dominance and territoriality. – Biological Reviews 58: 1-20.
Kays, R., Crofoot, M. C., Jetz, W. and Wikelski, M. 2015. Terrestrial animal tracking as an eye on life and planet. – Science 348: aaa2478.
Kidd, L. R., Sheldon, B. C., Simmonds, E. G. and Cole, E. F. 2015. Who escapes detection? Quantifying the causes and consequences of sampling biases in a long-term field study. – Journal of Animal Ecology 84: 1520-1529.
Klaassen, R. H. G., Hake, M., Strandberg, R., Koks, B. J., Trierweiler, C., Exo, K.-M., Bairlein, F. and Alerstam, T. 2014. When and where does mortality occur in migratory birds? Direct evidence from long-term satellite tracking of raptors. – Journal of Animal Ecology 83: 176-184.
Literák, I., Raab, R., Škrábal, J., Vyhnal, S., Dostál, M., Matušík, H., Makoň, K., Maderič, B. and Spakovszky, P. 2022. Dispersal and philopatry in Central European Red Kites Milvus milvus . – Journal of Ornithology 163: 469-479.
Longarini, A., Duriez, O., Shepard, E., Safi, K., Wikelski, M. and Scacco, M. 2023. Effect of harness design for tag attachment on the flight performance of five soaring species. – Movement Ecology 11: 39.
López-López, P., Perona, A. M., Egea-Casas, O., Morant, J. and Urios, V. 2021. Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors. – Current Zoology 68: 57-67.
López‐Sepulcre, A. and Kokko, H. 2005. Territorial defense, territory size, and population regulation. – American Naturalist 166: 317-325.
Maciorowski, G., Kosicki, J., Polakowski, M., Urbańska, M., Zduniak, P. and Tryjanowski, P. 2019. Autumn migration of immature Red KitesMilvus milvus from a Central European population. – Acta Ornithologica 54: 45-50, 6.
Mammen, K., Mammen, U. and Resetaritz, A. 2017. Red Kite. – Birds of Prey and Wind Farms: Analysis of Problems and Possible Solutions: 13-95.
Margalida, A., Jiménez, J., Martínez, J. M., Sesé, J. A., García-Ferré, D., Llamas, A., Razin, M., Colomer, M. À. and Arroyo, B. 2020. An assessment of population size and demographic drivers of the Bearded Vulture using integrated population models. – Ecological Monographs 90: e01414.
Mateo-Tomás, P., Olea, P. P., Mínguez, E., Mateo, R. and Viñuela, J. 2020. Direct evidence of poison-driven widespread population decline in a wild vertebrate. – Proceedings of the National Academy of Sciences 117: 16418-16423.
Mattsson, B. J., Mateo-Tomás, P., Aebischer, A., Rösner, S., Kunz, F., Schöll, E. M., Åkesson, S., De Rosa, D., Orr-Ewing, D., Bodega, D. d. l., Ferrer, M., Gelpke, C., Katzenberger, J., Maciorowski, G., Mammen, U., Kolbe, M., Millon, A., Mionnet, A., Puente, J. d. l., Raab, R., Vyhnal, S., Ceccolini, G., Godino, A., Crespo-Luengo, G., Sanchez-Agudo, J. A., Martínez, J., Iglesias-Lebrija, J. J., Ginés, E., Cortés, M., Deán, J. I., Calmaestra, R. G., Dostál, M., Steinborn, E. and Viñuela, J. 2022. Enhancing monitoring and transboundary collaboration for conserving migratory species under global change: The priority case of the red kite. – Journal of Environmental Management 317: 115345.
Murgatroyd, M., Tate, G. and Amar, A. 2023. Using GPS tracking to monitor the breeding performance of a low-density raptor improves accuracy, and reduces long-term financial and carbon costs. – Royal Society Open Science 10: 221447.
Nägeli, M., Scherler, P., Witczak, S., Catitti, B., Aebischer, A., van Bergen, V., Kormann, U. and Grüebler, M. U. 2022. Weather and food availability additively affect reproductive output in an expanding raptor population. – Oecologia 198: 125-138.
Nicolai, B., Mammen, U. and Kolbe, M. 2017. Long-term changes in population and habitat selection of Red Kite Milvus milvus in the region with the highest population density. – Vogelwelt 137: 194-197.
Orgeret, F., Grüebler, M. U., Scherler, P., Bergen, V. S. v. and Kormann, U. G. 2023. Shift in habitat selection during natal dispersal in a long-lived raptor species. – Ecography n/a: e06729.
Overton, C., Casazza, M., Bretz, J., McDuie, F., Matchett, E., Mackell, D., Lorenz, A., Mott, A., Herzog, M. and Ackerman, J. 2022. Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl. – Movement Ecology 10: 23.
Ozsanlav-Harris, L., Griffin, L. R., Weegman, M. D., Cao, L., Hilton, G. M. and Bearhop, S. 2022. Wearable reproductive trackers: quantifying a key life history event remotely. – Animal Biotelemetry 10: 24.
Ozsanlav-Harris, L., Hilton, G. M., Griffin, L. R., Walsh, A. J., Cao, L., Weegman, M. D. and Bearhop, S. 2023. Differing drivers of decline within a migratory metapopulation has implications for future conservation. – Ecology and Evolution 13: e10281.
Panuccio, M., Mellone, U. and Agostini, N. (eds.). 2021. Migration Strategies of Birds of Prey in Western Palearctic, CRC Press, Boca Raton, FL.
Peniche, G., Vaughan‐Higgins, R., Carter, I., Pocknell, A., Simpson, D. and Sainsbury, A. 2011. Long‐term health effects of harness‐mounted radio transmitters in red kites (Milvus milvus ) in England. – Veterinary Record 169: 311-311.
Péron, G., Walker, J., Rotella, J., Hines, J. E. and Nichols, J. D. 2014. Estimating nest abundance while accounting for time-to-event processes and imperfect detection. – Ecology 95: 2548-2557.
Pfeiffer, T. and Meyburg, B.-U. 2015. GPS tracking of Red Kites (Milvus milvus ) reveals fledgling number is negatively correlated with home range size. – Journal of Ornithology 156: 963-975.
Pfeiffer, T. and Meyburg, B.-U. 2022. Flight altitudes and flight activities of adult Red Kites (Milvus milvus) in the breeding area as determined by GPS telemetry. – Journal of Ornithology 163: 867-879.
Pfeiffer, T. and Schaub, M. 2023. Productivity drives the dynamics of a red kite source population that depends on immigration. – Journal of Avian Biology 2023: e02984.
Picardi, S., Smith, B. J., Boone, M. E., Frederick, P. C., Cecere, J. G., Rubolini, D., Serra, L., Pirrello, S., Borkhataria, R. R. and Basille, M. 2020. Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers. – Movement Ecology 8: 24.
Pichler, M. and Hartig, F. 2023. Machine learning and deep learning—A review for ecologists. – Methods in Ecology and Evolution 14: 994-1016.
Prasad, A. M., Iverson, L. R. and Liaw, A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. – Ecosystems 9: 181-199.
Saether, B. E. and Bakke, O. 2000. Avian life history variation and contribution of demographic traits to the population growth rate. – Ecology 81: 642-653.
Scherler, P., Bergen, V. v., Catitti, B., Kormann, U., Witczak, S., Andereggen, M., Herzog, J. S., Aebischer, A., Roth, N. and Grüebler, M. U. 2023a. Brutbiologie des Rotmilans Milvus milvus in den Westschweizer Voralpen. – Ornithologischer Beobachter 120.
Scherler, P., Witczak, S., Aebischer, A., van Bergen, V., Catitti, B. and Grüebler, M. U. 2023b. Determinants of departure to natal dispersal across an elevational gradient in a long-lived raptor species. – Ecology and Evolution 13: e9603.
Schreven, K. H., Stolz, C., Madsen, J. and Nolet, B. A. 2021. Nesting attempts and success of Arctic-breeding geese can be derived with high precision from accelerometry and GPS-tracking. – Animal Biotelemetry 9: 1-13.
Sergio, F., Tanferna, A., Blas, J., Blanco, G. and Hiraldo, F. 2019. Reliable methods for identifying animal deaths in GPS- and satellite-tracking data: Review, testing, and calibration. – Journal of Applied Ecology 56: 562-572.
Sergio, F., Tavecchia, G., Blas, J., López, L., Tanferna, A. and Hiraldo, F. 2011. Variation in age-structured vital rates of a long-lived raptor: Implications for population growth. – Basic and Applied Ecology 12: 107-115.
Sergio, F., Tavecchia, G., Blas, J., Tanferna, A. and Hiraldo, F. 2021. Demographic modeling to fine-tune conservation targets: importance of pre-adults for the decline of an endangered raptor. – Ecological Applications 31: e2266.
Sergio, F., Tavecchia, G., Tanferna, A., López Jiménez, L., Blas, J., De Stephanis, R., Marchant, T. A., Kumar, N. and Hiraldo, F. 2015. No effect of satellite tagging on survival, recruitment, longevity, productivity and social dominance of a raptor, and the provisioning and condition of its offspring. – Journal of Applied Ecology 52: 1665-1675.
Shamoun-Baranes, J., Bom, R., van Loon, E. E., Ens, B. J., Oosterbeek, K. and Bouten, W. 2012. From sensor data to animal behaviour: an oystercatcher example. – PLOS ONE 7: e37997.
Spatz, T., Katzenberger, J., Friess, N., Gelpke, C., Gottschalk, E., Hormann, M., Koschkar, S., Pfeiffer, T., Stübing, S., Sudfeldt, C., Rösner, S., Schabo, D. G. and Farwig, N. 2022. Sex, landscape diversity and primary productivity shape the seasonal space use of a migratory European raptor. – Journal of Avian Biology 2022: e02925.
Strobl, C., Boulesteix, A.-L., Zeileis, A. and Hothorn, T. 2007. Bias in random forest variable importance measures: Illustrations, sources and a solution. – BMC Bioinformatics 8: 25-45.
Swift, R. J., Rodewald, A. D., Johnson, J. A., Andres, B. A. and Senner, N. R. 2020. Seasonal survival and reversible state effects in a long-distance migratory shorebird. – Journal of Animal Ecology 89: 2043-2055.
Thiebault, A., Dubroca, L., Mullers Ralf, H. E., Tremblay, Y. and Pistorius Pierre, A. 2018. “m2b” package in r: Deriving multiple variables from movement data to predict behavioural states with random forests. – Methods in Ecology and Evolution 9: 1548-1555.
van der Wal, R., Zeng, C., Heptinstall, D., Ponnamperuma, K., Mellish, C., Ben, S. and Siddharthan, A. 2015. Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data: A case study of reintroduced red kites. – Ambio 44: 612-623.
Weimerskirch, H. 2018. Linking demographic processes and foraging ecology in wandering albatross—Conservation implications. – Journal of Animal Ecology 87: 945-955.
Williams, H. J., Taylor, L. A., Benhamou, S., Bijleveld, A. I., Clay, T. A., de Grissac, S., Demšar, U., English, H. M., Franconi, N., Gómez-Laich, A., Griffiths, R. C., Kay, W. P., Morales, J. M., Potts, J. R., Rogerson, K. F., Rutz, C., Spelt, A., Trevail, A. M., Wilson, R. P. and Börger, L. 2020. Optimizing the use of biologgers for movement ecology research. – Journal of Animal Ecology 89: 186-206.
Wright, M. N. and Ziegler, A. 2017. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. – Journal of Statistical Software 77: 1 - 17.
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., Ortuño Crespo, G., 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. and Sequeira, A. M. M. 2018. Outstanding challenges in the transferability of ecological models. – Trends in Ecology & Evolution 33: 790-802.