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.