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8 - Modelling Large-Scale Patterns in Mountain Bird Diversity and Distributions

Published online by Cambridge University Press:  30 June 2023

By
Mattia Brambilla ,
Matthew G. Betts ,
Ute Bradter ,
Hankyu Kim ,
Paola Laiolo  and
Thomas Sattler
Edited by
Dan Chamberlain ,
Aleksi Lehikoinen  and
Kathy Martin
Dan Chamberlain
Affiliation:
University of Turin
Aleksi Lehikoinen
Affiliation:
Finnish Museum of Natural History, University of Helsinki
Kathy Martin
Affiliation:
University of British Columbia, Vancouver
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Summary

Modelling distributions of species and communities is a key task for modern ecological research and conservation planning. Modelling mountain birds has specific challenges: mountain environments are characterized by steep gradients, where conditions in terms of climate, topography and habitat change markedly over relatively small scales. Moreover, mountain bird species are often less comprehensively monitored than lowland species, resulting in a general paucity of information for many species. We review the approaches to deal with these challenges in order to increase model accuracy to enhance ecological research and to improve conservation planning in mountain environments. We discuss how consistency between species occurrence and climate is tested, and what approaches help to assess distribution dynamics. We assess the current strategies to model microclimate and microhabitat, and how they could be incorporated in distribution modelling over increasingly larger extents. We discuss the pros and cons of (and the potential options for) modelling multiple species vs. community traits to get broad scale multi-species projections which are useful to evaluate the general persistence and resilience of mountain bird communities. Finally, the opportunities presented by Citizen Science data to contribute to monitoring and modelling mountain bird populations are assessed.

Keywords

climate changeclimate envelope modelsconservationmicroclimatemodel evaluationremote sensingspatial autocorrelationspecies distribution modelsspecies interactions
Type
Chapter
Information
Ecology and Conservation of Mountain Birds , pp. 296 - 335
Publisher: Cambridge University Press
Print publication year: 2023

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References

Alessandrini, C., Scridel, D., Boitani, L., Pedrini, P. & Brambilla, M. (2022) Remotely sensed variables explain microhabitat selection and reveal buffering behaviours against warming in a climate-sensitive bird species. Remote Sensing in Ecology and Conservation, 8, 615–628.Google Scholar
Altamirano, T.A., de Zwaan, D.R., Ibarra, J.T., Wilson, S. & Martin, K. (2020) Treeline ecotones shape the distribution of avian species richness and functional diversity in south temperate mountains. Scientific Reports, 10, 1–13.Google Scholar
Amano, T., Lamming, J.D.L. & Sutherland, W.J. (2016) Spatial gaps in global biodiversity information and the role of Citizen Science. Bioscience, 66, 393–400.Google Scholar
Anderson, M.J., Crist, T.O., Chase, J.M. et al. (2011) Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecology Letters, 14, 19–28.Google Scholar
Araújo, M.B. & Guisan, A. (2006) Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677–1688.Google Scholar
Araújo, M.B., Anderson, R.P., Barbosa, A.M., et al. (2019) Standards for distribution models in biodiversity assessments. Science Advances, 5, eaat4858.Google Scholar
Araújo, M.B. & Pearson, R.G. (2005) Equilibrium of species’ distributions with climate. Ecography, 28, 693–695.Google Scholar
Bahn, V. & Mcgill, B.J. (2007) Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16, 733–742.Google Scholar
Barras, A.G., Marti, S., Ettlin, S., et al. (2020) The importance of seasonal environmental factors in the foraging habitat selection of Alpine Ring Ouzels Turdus torquatus alpestris. Ibis, 162, 505–519.Google Scholar
Barras, A.G., Liechti, F. & Arlettaz, R. (2021a) Seasonal and daily movement patterns of an alpine passerine suggest high flexibility in relation to environmental conditions. Journal of Avian Biology, 52, e02860.Google Scholar
Barras, A.G., Braunisch, V. & Arlettaz, R. (2021b) Predictive models of distribution and abundance of a threatened mountain species show that impacts of climate change overrule those of land use change. Diversity and Distributions, 27, 989–1004.Google Scholar
Barve, S., Ramesh, V., Dotterer, T.M. & Dove, C.J. (2021) Elevation and body size drive convergent variation in thermo-insulative feather structure of Himalayan birds. Ecography, 44, 680–689Google Scholar
Bastianelli, G., Wintle, B.A., Martin, E.H., Seoane, J. & Laiolo, P. (2017) Species partitioning in a temperate mountain chain: segregation by habitat vs. interspecific competition. Ecology and Evolution, 7, 2685–2696.Google Scholar
Besag, J., York, J. & Mollié, A. (1991) Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 1–20.Google Scholar
Bettega, C., Fernández-González, Á., Ramón Obeso, J. & Delgado, M.D.M. (2020) Circannual variation in habitat use of the White-winged Snowfinch Montifringilla nivalis nivalis. Ibis, 162, 1251–1261.Google Scholar
Betts, M.G., Hadley, A.S., Rodenhouse, N. & Nocera, J.J. (2008) Social information trumps vegetation structure in breeding-site selection by a migrant songbird. Proceedings of the Royal Society Series B, 275, 2257–2263.Google Scholar
Betts, M.G., Gutierrez Illan, J., Thomas, C.D., Shirley, S. & Yang, Z. (2019) Synergistic effects of climate and land-cover change on long-term bird population trends of the western USA. Frontiers in Ecology and Evolution, 7, 186.Google Scholar
Borcard, D. & Legendre, P. (2002) All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153, 51–68.Google Scholar
Bowler, D.E., Nilsen, E.B., Bischof, R., et al. (2019) Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld’s deer. Scientific Reports, 9, 7766.Google Scholar
Boyce, A.J., Shakya, S., Sheldon, F.H., Moyle, R.G. & Martin, T.E. (2019) Biotic interactions are the dominant drivers of phylogenetic and functional structure in bird communities along a tropical elevational gradient. Auk, 136, ukz054.Google Scholar
Bradter, U., Thom, T.J., Altringham, J.D., Kunin, W.E. & Benton, T.G. (2011) Prediction of National Vegetation Classification communities in the British uplands using environmental data at multiple spatial scales, aerial images and the classifier random forest. Journal of Applied Ecology, 48, 1057–1065.Google Scholar
Bradter, U., Kunin, W.E., Altringham, J.D., Thom, T.J. & Benton, T.G. (2013) Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm. Methods in Ecology and Evolution, 4, 167–174.Google Scholar
Bradter, U., Mair, L., Jönsson, M., et al. (2018) Can opportunistically collected Citizen Science data fill a data gap for habitat suitability models of less common species? Methods in Ecology and Evolution, 9, 1667–1678.Google Scholar
Bradter, U., Ozgul, A., Griesser, M., et al. (2021) Habitat suitability models based on opportunistic citizen science data: evaluating forecasts from alternative methods versus an individual-based model. Diversity and Distributions, 27, 2397–2411.Google Scholar
Brambilla, M. & Pedrini, P. (2011) Intra-seasonal changes in local pattern of Corncrake Crex crex occurrence require adaptive conservation strategies in Alpine meadows. Bird Conservation International, 21, 388–393.Google Scholar
Brambilla, M. & Rubolini, D. (2009) Intra-seasonal changes in distribution and habitat associations of a multi-brooded bird species: implications for conservation planning. Animal Conservation, 12, 71–77.Google Scholar
Brambilla, M. & Saporetti, F. (2014) Modelling distribution of habitats required for different uses by the same species: implications for conservation at the regional scale. Biological Conservation, 174, 39–46.Google Scholar
Brambilla, M., Bassi, E., Ceci, C. & Rubolini, D. (2010) Environmental factors affecting patterns of distribution and co-occurrence of two competing raptor species. Ibis, 152, 310–322.Google Scholar
Brambilla, M., Pedrini, P., Rolando, A. & Chamberlain, D.E. (2016) Climate change will increase the potential conflict between skiing and high-elevation bird species in the Alps. Journal of Biogeography, 43, 2299–2309.Google Scholar
Brambilla, M., Cortesi, M., Capelli, F., et al. (2017a) Foraging habitat selection by Alpine White-winged Snowfinches Montifringilla nivalis during the nestling rearing period. Journal of Ornithology, 158, 277–286.Google Scholar
Brambilla, M., Caprio, E., Assandri, G., et al. (2017b) A spatially explicit definition of conservation priorities according to population resistance and resilience, species importance and level of threat in a changing climate. Diversity and Distributions, 23, 727–738.Google Scholar
Brambilla, M., Resano-Mayor, J., Scridel, D. et al. (2018a) Past and future impact of climate change on foraging habitat suitability in a high-alpine bird species: management options to buffer against global warming effects. Biological Conservation, 221, 209–218.Google Scholar
Brambilla, M., Capelli, F., Anderle, M., et al. (2018b) Landscape-associated differences in fine-scale habitat selection modulate the potential impact of climate change on White-winged Snowfinch Montifringilla nivalis. Bird Study, 65, 525–532.Google Scholar
Brambilla, M., Gustin, M., Cento, M., Ilahiane, L. & Celada, C. (2019a) Predicted effects of climate factors on mountain species are not uniform over different spatial scales. Journal of Avian Biology, 50, e02162.Google Scholar
Brambilla, M., Scridel, D., Sangalli, B., et al. (2019b) Ecological factors affecting foraging behaviour during nestling rearing in a high-elevation species, the White-winged Snowfinch (Montifringilla nivalis). Ornis Fennica, 96, 142–151.Google Scholar
Brambilla, M., Scridel, D., Bazzi, G., et al. (2020a) Species interactions and climate change: how the disruption of species co-occurrence will impact on an avian forest guild. Global Change Biology, 26, 1212–1224.Google Scholar
Brambilla, M., Gustin, M., Cento, M., Ilahiane, L. & Celada, C. (2020b) Habitat, climate, topography and management differently affect occurrence in declining avian species: implications for conservation in changing environments. Science of the Total Environment, 742, 140663.Google Scholar
Brambilla, M., Resano-Mayor, J., Arlettaz, R., et al. (2020c) Potential distribution of a climate sensitive species, the White-winged Snowfinch Montifringilla nivalis in Europe. Bird Conservation International, 30, 522–532.Google Scholar
Brambilla, M., Gubert, F. & Pedrini, P. (2021) The effects of farming intensification on an iconic grassland bird species, or why mountain refuges no longer work for farmland biodiversity. Agriculture, Ecosystems & Environment, 319, 107518.Google Scholar
Brambilla, M., Rubolini, D., Appukuttan, O., et al. (2022) Identifying climate refugia for high-elevation Alpine birds under current climate warming predictions. Global Change Biology, 28, 4276–4291.Google Scholar
Braunisch, V., Patthey, P. & Arlettaz, R. (2011) Spatially explicit modeling of conflict zones between wildlife and snow sports: prioritizing areas for winter refuges. Ecological Applications, 21, 955–967.Google Scholar
Braunisch, V., Coppes, J., Arlettaz, R., et al. (2013) Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography, 36, 971–983.Google Scholar
Braunisch, V., Coppes, J., Arlettaz, R., et al. (2014) Temperate mountain forest biodiversity under climate change: compensating negative effects by increasing structural complexity. PLoS ONE, 9, e97718.Google Scholar
Braunisch, V., Patthey, P. & Arlettaz, R. (2016) Where to combat shrub encroachment in Alpine timberline ecosystems: combining remotely sensed vegetation information with species habitat modelling. PLoS ONE, 11, e0164318.Google Scholar
Broms, K.M., Hooten, M.B., Johnson, D.S., et al. (2016) Dynamic occupancy models for explicit colonization processes. Ecology, 97, 194–204.Google Scholar
Burner, R.C., Boyce, A.J., Bernasconi, D., et al. (2020) Biotic interactions help explain variation in elevational range limits of birds among Bornean mountains. Journal of Biogeography, 47, 760–771.Google Scholar
Ceresa, F., Brambilla, M., Monrós, J.S. & Kranebitter, P. (2020) Within-season movements of Alpine songbird distributions are driven by fine-scale environmental characteristics. Scientific Reports, 10, 5747.Google Scholar
Ceresa, F., Kranebitter, P., Monrós, J.S., Rizzolli, F. & Brambilla, M. (2021) Disentangling direct and indirect effects of local temperature on abundance of mountain birds and implications for understanding global change impacts. PeerJ, 9, e12560.Google Scholar
Chamberlain, D., Arlettaz, R., Caprio, E., et al. (2012) The altitudinal frontier in avian climate change research. Ibis, 154, 205–209.Google Scholar
Chamberlain, D.E., Negro, M., Caprio, E. & Rolando, A. (2013) Assessing the sensitivity of alpine birds to potential future changes in habitat and climate to inform management strategies. Biological Conservation, 167, 127–135.Google Scholar
Chamberlain, D., Brambilla, M., Caprio, E., Pedrini, P. & Rolando, A. (2016) Alpine bird distributions along elevation gradients: the consistency of climate and habitat effects across geographic regions. Oecologia, 181, 1139–1150.Google Scholar
Chmura, H.E., Glass, T.W. & Williams, C.T. (2018) Biologging physiological and ecological responses to climatic variation: new tools for the climate change era. Frontiers in Ecology and Evolution, 6, 92.Google Scholar
Crase, B., Liedloff, A., Vesk, P.A., Fukuda, Y. & Wintle, B.A. (2014) Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts. Global Change Biology, 20, 2566–2579.Google Scholar
Culp, L.A., Cohen, E.B., Scarpignato, A.L., Thogmartin, W.E. & Marra, P.P. (2017) Full annual cycle climate change vulnerability assessment for migratory birds. Ecosphere, 8, e01565.Google Scholar
Cunningham, S.J., Kruger, A.C., Nxumalo, M.P. & Hockey, P.A. (2013). Identifying biologically meaningful hot-weather events using threshold temperatures that affect life-history. PLoS ONE, 8, e82492.Google Scholar
D’Elia, J., Haig, S.M., Johnson, M., Marcot, B.G. & Young, R. (2015) Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus). Biological Conservation, 184, 90–99.Google Scholar
Dail, D. & Madsen, L. (2011) Models for estimating abundance from repeated counts of an open metapopulation. Biometrics, 67, 577–587.Google Scholar
Danchin, É., Giraldeau, L.A., Valone, T.J. & Wagner, R.H. (2004) Public information: from nosy neighbors to cultural evolution. Science, 305, 487–491.Google Scholar
de Frenne, P., Lenoir, J., Luoto, M., et al. (2021) Forest microclimates and climate change: importance, drivers and future research agenda. Global Change Biology, 27, 2279–2297.Google Scholar
de Gabriel Hernando, M., Fernández‐Gil, J., Roa, I., et al. (2021) Warming threatens habitat suitability and breeding occupancy of rear‐edge alpine bird specialists. Ecography, 44, 1191–1204.Google Scholar
de Knegt, H.J., Van Langevelde, F., Coughenour, M.B., et al. (2010) Spatial autocorrelation and the scaling of species-environment relationships. Ecology, 91, 2455–2465.Google Scholar
de Zwaan, D.R., Drake, A., Greenwood, J.L. & Martin, K. (2020) Timing and intensity of weather events shape nestling development strategies in three alpine breeding songbirds. Frontiers in Ecology and Evolution, 8, 570034.Google Scholar
Delgado, M., Arlettaz, R., Bettega, C., et al. (2021) Spatio-temporal variation in the wintering associations of an alpine bird. Proceedings of the Royal Society Series B, 288, 20210690.Google Scholar
Devictor, V., Julliard, R., Couvet, D. & Jiguet, F. (2008) Birds are tracking climate warming, but not fast enough. Proceedings of the Royal Society Series B, 275, 2743–2748.Google Scholar
Dobrowski, S.Z. (2011) A climatic basis for microrefugia: the influence of terrain on climate. Global Change Biology, 17, 1022–1035.Google Scholar
Dormann, C.F. (2007) Promising the future? Global change projections of species distributions. Basic and Applied Ecology, 8, 387–397.Google Scholar
Dormann, C.F., Schymanski, S.J., Cabral, J., et al. (2012) Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography, 39, 2119–2131.Google Scholar
Dormann, C.F., Bobrowski, M., Dehling, D.M., et al. (2018) Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecology and Biogeography, 27, 1004–1016.Google Scholar
Dray, S., Legendre, P. & Peres-Neto, P.R. (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, 196, 483–493.Google Scholar
Duclos, T.R., DeLuca, W.V. & King, D.I. (2019) Direct and indirect effects of climate on bird abundance along elevation gradients in the Northern Appalachian mountains. Diversity and Distributions, 25, 1670–1683.Google Scholar
Elith, J., Graham, C.H., Anderson, R.P., et al. (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.Google Scholar
Elith, J., Phillips, S.J., Hastie, T., et al. (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43–57.Google Scholar
Elsen, P.R. & Tingley, M.W. (2015) Global mountain topography and the fate of montane species under climate change. Nature Climate Change, 5, 772–776.Google Scholar
Engler, J.O., Stiels, D., Schidelko, K., et al. (2017) Avian SDMs: current state, challenges, and opportunities. Journal of Avian Biology, 48, 1483–1504.Google Scholar
Ewing, S.R., Baxter, A., Wilson, J.D., et al. (2020) Clinging on to alpine life: investigating factors driving the uphill range contraction and population decline of a mountain breeding bird. Global Change Biology, 26, 3771–3787.Google Scholar
Eyres, A., Böhning-Gaese, K. & Fritz, S.A. (2017) Quantification of climatic niches in birds: adding the temporal dimension. Journal of Avian Biology, 48, 1517–1531.Google Scholar
Feilhauer, H., Thonfeld, F., Faude, U., et al. (2013) Assessing floristic composition with multispectral sensors – A comparison based on monotemporal and multiseasonal field spectra. International Journal of Applied Earth Observation and Geoinformation, 21, 218–229.Google Scholar
Feldmeier, S., Schmidt, B.R., Zimmermann, N.E., et al. (2020) Shifting aspect or elevation? The climate change response of ectotherms in a complex mountain topography. Diversity and Distributions, 26, 1483–1495.Google Scholar
Fithian, W., Elith, J., Hastie, T. & Keith, D.A. (2015) Bias correction in species distribution models: pooling survey and collection data for multiple species. Methods in Ecology and Evolution, 6, 424–438.Google Scholar
FletcherJr, R.J., Hefley, T.J., Robertson, E.P., et al. (2019) A practical guide for combining data to model species distributions. Ecology, 100, e02710.Google Scholar
Fourcade, Y., Besnard, A.G. & Secondi, J. (2018) Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography, 27, 245–256.Google Scholar
Freeman, B.G., Tobias, J.A. & Schluter, D. (2019) Behavior influences range limits and patterns of coexistence across an elevational gradient in tropical birds. Ecography, 42, 1832–1840.Google Scholar
Frey, S.J.K., Hadley, A.S., Betts, M.G. & Robertson, M. (2016a) Microclimate predicts within-season distribution dynamics of montane forest birds. Diversity and Distributions, 22, 944–959.Google Scholar
Frey, S.J.K., Hadley, A.S., Johnson, S.L., et al. (2016b) Spatial models reveal the microclimatic buffering capacity of old-growth forests. Science Advances, 2, e1501392.Google Scholar
García-Navas, V., Sattler, T., Schmid, H. & Ozgul, A. (2020) Temporal homogenization of functional and beta diversity in bird communities of the Swiss Alps. Diversity and Distributions, 26, 900–911.Google Scholar
Gaspard, G., Kim, D. & Chun, Y. (2019) Residual spatial autocorrelation in macroecological and biogeographical modeling: a review. Journal of Ecology and Environment, 43, 1–11.Google Scholar
Gehrig-Fasel, J., Guisan, A. & Zimmermann, N.E. (2007) Tree line shifts in the Swiss Alps: climate change or land abandonment? Journal of Vegetation Science, 18, 571–582.Google Scholar
Goljani Amirkhiz, R., Dixon, M.D., Palmer, J.S. & Swanson, D.L. (2021) Investigating niches and distribution of a rare species in a hierarchical framework: Virginia’s Warbler (Leiothlypis virginiae) at its northeastern range limit. Landscape Ecology, 36, 1039–1054.Google Scholar
Guélat, J. & Kéry, M. (2018) Effects of spatial autocorrelation and imperfect detection on species distribution models. Methods in Ecology and Evolution, 9, 1614–1625.Google Scholar
Guisan, A. & Thuiller, W. (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993–1009.Google Scholar
Guisan, A., Graham, C. & Elith, J. (2007) Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions, 13, 332–340.Google Scholar
Hadley, A.S. & Betts, M.G. (2009) Tropical deforestation alters hummingbird movement patterns. Biology Letters, 5, 207–210.Google Scholar
Hannah, L., Flint, L., Syphard, A.D., et al. (2014) Fine-grain modeling of species’ response to climate change: holdouts, stepping-stones, and microrefugia. Trends in Ecology & Evolution, 29, 390–397.Google Scholar
Hawkins, B.A. (2012) Eight (and a half) deadly sins of spatial analysis. Journal of Biogeography, 39, 1–9.Google Scholar
He, K.S., Bradley, B.A., Cord, A.F., et al. (2015) Will remote sensing shape the next generation of species distribution models? Remote Sensing in Ecology and Conservation, 1, 4–18.Google Scholar
Heikkinen, R.K., Luoto, M., Virkkala, R., Pearson, R.G. & Körber, J. (2007) Biotic interactions improve prediction of boreal bird distributions at macro‐scales. Global Ecology and Biogeography, 16, 754–763.Google Scholar
Hirzel, A.H., Posse, B., Oggier, P.A., et al. (2004) Ecological requirements of reintroduced species and the implications for release policy: the case of the bearded vulture. Journal of Applied Ecology, 41, 1103–1116.Google Scholar
Hochachka, W.M., Fink, D., Hutchinson, R.A., et al. (2012) Data-intensive science applied to broad-scale citizen science. Trends in Ecology & Evolution, 27, 130–137.Google Scholar
Hoffmann, D., Vasconcelos, M.F. de & Martins, R.P. (2015) How climate change can affect the distribution range and conservation status of an endemic bird from the highlands of eastern Brazil: the case of the Gray-backed Tachuri, Polystictus superciliaris (Aves, Tyrannidae). Biota Neotropica, 15, e20130075.Google Scholar
Hostetler, J.A. & Chandler, R.B. (2015) Improved state-space models for inference about spatial and temporal variation in abundance from count data. Ecology, 96, 1713–1723.Google Scholar
Hotta, M., Tsuyama, I., Nakao, K., et al. (2019) Modeling future wildlife habitat suitability: serious climate change impacts on the potential distribution of the rock ptarmigan Lagopus muta japonica in Japan’s northern Alps. BMC Ecology, 19, 23.Google Scholar
Huber, N., Kienast, F., Ginzler, C. & Pasinelli, G. (2016) Using remote-sensing data to assess habitat selection of a declining passerine at two spatial scales. Landscape Ecology, 31, 1919–1937.Google Scholar
Illán, J.G., Thomas, C.D., Jones, J.A., et al. (2014) Precipitation and winter temperature predict long-term range-scale abundance changes in Western North American birds. Global Change Biology, 20, 3351–3364.Google Scholar
Isaac, N.J.B., van Strien, A.J., August, T.A., de Zeeuw, M.P. & Roy, D.B. (2014) Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5, 1052–1060.Google Scholar
Isaac, N.J.B., Jarzyna, M.A., Keil, P., et al. (2020) Data integration for large-scale models of species distributions. Trends in Ecology & Evolution, 35, 56–67.Google Scholar
Jackson, M.M., Gergel, S.E. & Martin, K. (2015) Citizen science and field survey observations provide comparable results for mapping Vancouver Island white-tailed ptarmigan (Lagopus leucura saxatilis) distributions. Biological Conservation, 181, 162–172.Google Scholar
Jähnig, S., Alba, R., Vallino, C., et al. (2018) The contribution of broadscale and finescale habitat structure to the distribution and diversity of birds in an Alpine forest-shrub ecotone. Journal of Ornithology, 159, 747–759.Google Scholar
Jankowski, J.E., Londoño, G.A., Robinson, S.K. & Chappell, M.A. (2013) Exploring the role of physiology and biotic interactions in determining elevational ranges of tropical animals. Ecography, 36, 1–12.Google Scholar
Jantz, S.M., Barker, B., Brooks, T.M., et al. (2015) Future habitat loss and extinctions driven by land-use change in biodiversity hotspots under four scenarios of climate-change mitigation. Conservation Biology, 29, 1122–1131.Google Scholar
Jedlikowski, J., Chibowski, P., Karasek & T. & Brambilla, M. (2016) Multi-scale habitat selection in highly territorial bird species: exploring the contribution of nest, territory and landscape levels to site choice in breeding rallids (Aves: Rallidae). Acta Oecologica, 73, 10–20.Google Scholar
Johnston, A., Fink, D., Hochachka, W.M. & Kelling, S. (2018) Estimates of observer expertise improve species distributions from citizen science data. Methods in Ecology and Evolution, 9, 88–97.Google Scholar
Johnston, A., Hochachka, W.M., Strimas-Mackey, M.E., et al. (2021) Analytical guidelines to increase the value of community science data: an example using eBird data to estimate species distributions. Diversity and Distributions, 27, 1265–1277.Google Scholar
Jombart, T., Dray, S. & Dufour, A.B. (2009) Finding essential scales of spatial variation in ecological data: a multivariate approach. Ecography, 32, 161–168.Google Scholar
Joseph, L.N., Elkin, C., Martin, T.G. & Possingham, H.P. (2009) Modeling abundance using N-mixture models: the importance of considering ecological mechanisms. Ecological Applications, 19, 631–642.Google Scholar
Kammann, E.E. & Wand, M.P. (2003) Geoadditive models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 52, 1–18.Google Scholar
Kearney, M.R., Gillingham, P.K., Bramer, I., Duffy, J.P. & Maclean, I.M.D. (2020) A method for computing hourly, historical, terrain‐corrected microclimate anywhere on earth. Methods in Ecology and Evolution, 11, 38–43.Google Scholar
Keller, V., Herrando, S., Voříšek, P., et al. (2020) European Breeding Bird Atlas 2: Distribution, Abundance and Change. Barcelona: European Bird Census Council & Lynx Edicions.Google Scholar
Kerr, G.D., Bull, C.M. & Cottrell, G.R. (2004) Use of an “on board” datalogger to determine lizard activity patterns, body temperature and microhabitat use for extended periods in the field. Wildife Research, 31, 171–176.Google Scholar
Kéry, M. & Royle, J.A. (2016). Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS: Volume 1: Prelude and Static Models. Amsterdam: Elsevier/AP.Google Scholar
Kéry, M., Gardner, B. & Monnerat, C. (2010a) Predicting species distributions from checklist data using site-occupancy models. Journal of Biogeography, 37, 1851–1862.Google Scholar
Kéry, M., Royle, J.A., Schmid, H., et al. (2010b) Site-occupancy distribution modeling to correct population-trend estimates derived from opportunistic observations. Conservation Biology, 24, 1388–1397.Google Scholar
Kim, Y., Still, C.J., Roberts, D.A. & Goulden, M.L. (2018) Thermal infrared imaging of conifer leaf temperatures: comparison to thermocouple measurements and assessment of environmental influences. Agricultural and Forest Meteorology, 248, 361–371.Google Scholar
Kim, H., McComb, B.C., Frey, S.J., Bell, D.M. & Betts, M.G. (2022) Forest microclimate and composition mediate long‐term trends of breeding bird populations. Global Change Biology, 28, 6180–6193.Google Scholar
Knaus, P., Antoniazza, S., Wechsler, S., et al. (2018) Swiss Breeding Bird Atlas 2013–2016. Distribution and Population Trends of Birds in Switzerland and Liechtenstein. Sempach: Swiss Ornithological Institute.Google Scholar
Laiolo, P., Seoane, J., Obeso, J.R. & Illera, J.C. (2017) Ecological divergence among young lineages favours sympatry, but convergence among old ones allows coexistence in syntopy. Global Ecology and Biogeography, 26, 601–608.Google Scholar
Laiolo, P., Pato, J. & Obeso, J.R. (2018) Ecological and evolutionary drivers of the elevational gradient of diversity. Ecology Letters, 21, 1022–1032.Google Scholar
Latimer, C.E. & Zuckerberg, B. (2021) Habitat loss and thermal tolerances influence the sensitivity of resident bird populations to winter weather at regional scales. Journal of Animal Ecology, 90, 317–329.Google Scholar
Lehikoinen, A. & Virkkala, R. (2016) North by north‐west: climate change and directions of density shifts in birds. Global Change Biology, 22, 1121–1129.Google Scholar
Lembrechts, J.J., Nijs, I. & Lenoir, J. (2018) Incorporating microclimate into species distribution models. Ecography, 42, 1267–1279.Google Scholar
Lembrechts, J.J., Lenoir, J., Roth, N., et al. (2019) Comparing temperature data sources for use in species distribution models: from in-situ logging to remote sensing. Global Ecology and Biogeography, 28, 1578–1596.Google Scholar
Lenoir, J., Hattab, T. & Pierre, G. (2017) Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography, 40, 253–266.Google Scholar
Lillesand, T.M., Kiefer, R.W. & Chipman, J.W. (2008) Remote Sensing and Image Interpretation, 6th ed. New York: Wiley.Google Scholar
MacKenzie, D.I., Nichols, J.D., Hines, J.E., Knutson, M.G. & Franklin, A.B. (2003) Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology, 84, 823–835.Google Scholar
Mackenzie, D.I., Nichols, J.D., Seamans, M.E. & Gutiérrez, R.J. (2009) Modeling species occurrence dynamics with multiple states and imperfect detection. Ecology, 90, 823–835.Google Scholar
Maclean, I.M., Mosedale, J.R. & Bennie, J.J. (2019) Microclima: an r package for modelling meso‐and microclimate. Methods in Ecology and Evolution, 10, 280–290.Google Scholar
Mair, L. & Ruete, A. (2016) Explaining spatial variation in the recording effort of citizen science data across multiple taxa. PLoS ONE, 11, e0147796.Google Scholar
Marcacci, G., Gremion, J., Mazenauer, J., et al. (2020) Large-scale versus small-scale agriculture: disentangling the relative effects of the farming system and semi-natural habitats on birds’ habitat preferences in the Ethiopian highlands. Agriculture, Ecosystems & Environment, 289, 106737.Google Scholar
Margalida, A., Jiménez, J., Martínez, J.M., et al. (2020) An assessment of population size and demographic drivers of the bearded vulture using integrated population models. Ecological Monographs, 90, e01414.Google Scholar
McCain, C.M. (2009) Global analysis of bird elevational diversity. Global Ecology and Biogeography, 18, 346–360.Google Scholar
Meineri, E. & Hylander, K. (2017) Fine-grain, large-domain climate models based on climate station and comprehensive topographic information improve microrefugia detection. Ecography, 40, 1003–1013.Google Scholar
Mertes, K. & Jetz, W. (2018) Disentangling scale dependencies in species environmental niches and distributions. Ecography, 41, 1604–1615.Google Scholar
Mod, H.K., Scherrer, D., Luoto, M. & Guisan, A. (2016) What we use is not what we know: environmental predictors in plant distribution models. Journal of Vegetation Science, 27, 1308–1322.Google Scholar
Morelli, T.L., Maher, S.P., Lim, M.C.W., et al. (2017) Climate change refugia and habitat connectivity promote species persistence. Climate Change Responses, 4, 1–12.Google Scholar
Morelli, T.L., Barrows, C.W., Ramirez, A.R., et al. (2020) Climate-change refugia: biodiversity in the slow lane. Frontiers in Ecology and Environment, 18, 228–234.Google Scholar
Morin, X., Augspurger, C. & Chuine, I. (2007) Process-based modeling of species’ distributions: what limits temperate tree species’ range boundaries? Ecology, 88, 2280–2291.Google Scholar
Neate-Clegg, M.H.C., Jones, S.E.I., Tobias, J.A., Newmark, W.D. & Şekerciogˇlu, Ç.H. (2021) Ecological correlates of elevational range shifts in tropical birds. Frontiers in Ecology and Evolution, 9, 215.Google Scholar
Nogués-Bravo, D., Araújo, M.B., Errea, M.P. & Martínez-Rica, J.P. (2007) Exposure of global mountain systems to climate warming during the twenty-first Century. Global Environmental Change, 17, 420–428.Google Scholar
Perez-Navarro, M.A., Broennimann, O., Esteve, M.A., et al. (2021) Temporal variability is key to modelling the climatic niche. Diversity and Distributions, 27, 473–484.Google Scholar
Pernollet, C.A., Korner-Nievergelt, F. & Jenni, L. (2015) Regional changes in the elevational distribution of the Alpine Rock Ptarmigan Lagopus muta helvetica in Switzerland. Ibis, 157, 823–836.Google Scholar
Petchey, O.L. & Gaston, K.J. (2002) Functional diversity (FD), species richness and community composition. Ecology Letters, 5, 402–411.Google Scholar
Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.Google Scholar
Poggiato, G., Münkemüller, T., Bystrova, D., et al. (2021) On the interpretations of joint modeling in community ecology. Trends in Ecology & Evolution, 36, 391–401.Google Scholar
Pollock, L.J., Tingley, R., Morris, W.K., et al. (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5, 397–406.Google Scholar
Ponti, R., Arcones, A., Ferrer, X. & Vieites, D.R. (2020) Seasonal climatic niches diverge in migratory birds. Ibis, 162, 318–330.Google Scholar
Potter, K.A., Arthur Woods, H. & Pincebourde, S. (2013) Microclimatic challenges in global change biology. Global Change Biology, 19, 2932–2939.Google Scholar
Pypker, T.G., Unsworth, M.H., Mix, A.C., et al. (2007) Using nocturnal cold air drainage flow to monitor ecosystem processes in complex terrain. Ecological Applications, 17, 702–714.Google Scholar
Randin, C.F., Engler, R., Normand, S., et al. (2009) Climate change and plant distribution: local models predict high-elevation persistence. Global Change Biology, 15, 1557–1569.Google Scholar
Randin, C.F., Ashcroft, M.B., Bolliger, J., et al. (2020) Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sensing of Environment, 239, 111626.Google Scholar
Rapinel, S., Mony, C., Lecoq, L., et al. (2019) Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities. Remote Sensing of Environment, 223, 115–129.Google Scholar
Ratcliffe, D.A. (2010) Bird Life of Mountain and Upland. Cambridge: Cambridge University Press.Google Scholar
Resano-Mayor, J., Korner-Nievergelt, F., Vignali, S., et al. (2019) Snow cover phenology is the main driver of foraging habitat selection for a high-alpine passerine during breeding: implications for species persistence in the face of climate change. Biodiversity Conservation, 28, 2669–2685.Google Scholar
Revermann, R., Schmid, H., Zbinden, N., Spaar, R. & Schröder, B. (2012) Habitat at the mountain tops: how long can Rock Ptarmigan (Lagopus muta helvetica) survive rapid climate change in the Swiss Alps? A multi-scale approach. Journal of Ornithology, 153, 891–905.Google Scholar
Riddell, E.A., Iknayan, K.J., Hargrove, L., et al. (2021) Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science, 371, 633–636.Google Scholar
Rodríguez, J.P., Brotons, L., Bustamante, J. & Seoane, J. (2007) The application of predictive modelling of species distribution to biodiversity conservation. Diversity and Distributions, 13, 243–251.Google Scholar
Royle, J.A., Chandler, R.B., Yackulic, C. & Nichols, J.D. (2012) Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods in Ecology and Evolution, 3, 545–554.Google Scholar
Rushing, C.S., Royle, A.J., Ziolkowski, D.J. & Pardieck, K.L. (2020) Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change. Proceeding of the National Academy of Sciences, 117, 12897–12903.Google Scholar
Schano, C., Niffenegger, C., Jonas, T. & Korner-Nievergelt, F. (2021) Hatching phenology is lagging behind an advancing snowmelt pattern in a high-alpine bird. Scientific Reports, 11, 1–11.Google Scholar
Schaub, M., Zink, R., Beissmann, H., Sarrazin, F. & Arlettaz, R. (2009) When to end releases in reintroduction programmes: demographic rates and population viability analysis of bearded vultures in the Alps. Journal of Applied Ecology, 46, 92–100.Google Scholar
Scheiner, S.M., Chiarucci, A., Fox, G.A., et al. (2011) The underpinnings of the relationship of species richness with space and time. Ecological Monographs, 81, 195–213.Google Scholar
Scherrer, D., Schmid, S. & Körner, C. (2011) Elevational species shifts in a warmer climate are overestimated when based on weather station data. International Journal of Biometeorology, 55, 645–654.Google Scholar
Scridel, D., Brambilla, M., Martin, K., et al. (2018) A review and meta-analysis of the effects of climate change on Holarctic mountain and upland bird populations. Ibis, 160, 489–515.Google Scholar
Scridel, D., Brambilla, M., de Zwaan, D., et al. (2021) A genus at risk: current and future potential distribution of all three Lagopus species reveal sensitivity to climate change and efficacy of protected areas. Diversity and Distributions, 27, 1759–1774.Google Scholar
Segurado, P., Araújo, M.B. & Kunin, W.E. (2006) Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology, 43, 433–444.CrossRefGoogle Scholar
Şekercioğlu, Ç.H., Schneider, S.H., Fay, J.P. & Loarie, S.R. (2008) Climate change, elevational range shifts, and bird extinctions. Conservation Biology, 22, 140–150.Google Scholar
Şekercioğlu, Ç.H., Primack, R.B. & Wormworth, J. (2012) The effects of climate change on tropical birds. Biological Conservation, 148, 1–18.Google Scholar
Şekercioğlu, Ç.H., Wenny, D.G. & Whelan, C.J. (2016) Why Birds Matter: Avian Ecological Function and Ecosystem Services. Chicago: University of Chicago Press.Google Scholar
Shaw, D. & Flick, C. (1999) Are resident songbirds stratified within the canopy of a coniferous old-growth forest? Selbyana, 20, 324–331.Google Scholar
Sheldon, K.S., Yang, S. & Tewksbury, J.J. (2011) Climate change and community disassembly: impacts of warming on tropical and temperate montane community structure. Ecology Letters, 14, 1191–1200.Google Scholar
Singer, A., Schweiger, O., Kühn, I. & Johst, K. (2018) Constructing a hybrid species distribution model from standard large-scale distribution data. Ecological Modelling, 373, 39–52.Google Scholar
Sirami, C., Caplat, P., Popy, S., et al. (2017) Impacts of global change on species distributions: obstacles and solutions to integrate climate and land use. Global Ecology and Biogeography, 26, 385–394.Google Scholar
Storlie, C., Merino-Viteri, A., Phillips, B., et al. (2014) Stepping inside the niche: microclimate data are critical for accurate assessment of species’ vulnerability to climate change. Biology Letters, 10, 20140576.Google Scholar
Strinella, E., Scridel, D., Brambilla, M., Schano, C. & Korner-Nievergelt, F. (2020) Potential sex-dependent effects of weather on apparent survival of a high-elevation specialist. Scientific Reports, 10, 8386.Google Scholar
Sullivan, B.L., Wood, C.L., Iliff, M.J., et al. (2009) eBird: a citizen-based bird observation network in the biological sciences. Biological Conservation, 142, 2282–2292.Google Scholar
Sutherland, C.S., Elston, D.A. & Lambin, X. (2014) A demographic, spatially explicit patch occupancy model of metapopulation dynamics and persistence. Ecology, 95, 3149–3160.Google Scholar
Thenkabail, P.S., Lyon, J.G. & Huete, A. (2011) Advances in hyperspectral remote sensing of vegetation and agricultural croplands. In Hyperspectral Remote Sensing of Vegetation. Thenkabail, P.S. & Lyon, J.G. (eds.). Boca Raton: CRC Press, pp. 3–26.Google Scholar
Thomas, C.D., Cameron, A., Green, R.E., et al. (2004) Extinction risk from climate change. Nature, 427, 145–148.Google Scholar
Tingley, M.W., Monahan, W.B., Beissinger, S.R. & Moritz, C. (2009) Birds track their Grinnellian niche through a century of climate change. Proceedings of the National Academy of Sciences, 106 (Suppl. 2), 19637–19643.Google Scholar
Tornberg, R., Rytkönen, S., Välimäki, P., Valkama, J. & Helle, P. (2016) Northern Goshawk (Accipiter gentilis) may improve Black Grouse breeding success. Journal of Ornithology, 157, 363–370.Google Scholar
Trivedi, M.R., Berry, P.M., Morecroft, M.D. & Dawson, T.P. (2008) Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. Global Change Biology, 14, 1089–1103.Google Scholar
Tuomisto, H. (2010) A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography, 33, 2–22.Google Scholar
Tye, C.A., McCleery, R.A., Fletcher, R.J., Greene, D.U. & Butryn, R.S. (2017) Evaluating citizen vs. professional data for modelling distributions of a rare squirrel. Journal of Applied Ecology, 54, 628–637.Google Scholar
Urban, M.C., Bocedi, G., Hendry, A.P., et al. (2016) Improving the forecast for biodiversity under climate change. Science, 353, aad8466.Google Scholar
Vickery, J.A., Ewing, S.R., Smith, K.W., et al. (2014) The decline of Afro-Palaearctic migrants and an assessment of potential causes. Ibis, 156, 1–22.Google Scholar
Villéger, S., Novack-Gottshall, P.M. & Mouillot, D. (2011) The multidimensionality of the niche reveals functional diversity changes in benthic marine biotas across geological time. Ecology Letters, 14, 561–568.Google Scholar
Vincent, C., Fernandes, R.F., Cardoso, A.R., et al. (2019) Climate and land-use changes reshuffle politically-weighted priority areas of mountain biodiversity. Global Ecology and Conservation, 17, e00589.Google Scholar
von dem Bussche, J., Spaar, R., Schmid, H. & Schröder, B. (2008) Modelling the recent and potential future spatial distribution of the Ring Ouzel (Turdus torquatus) and Blackbird (T. merula) in Switzerland. Journal of Ornithology, 149, 529–544.Google Scholar
von Humboldt, A. & Bonpland, J.R. (1807) Ideen zu einer Geographie der Pflanzen nebst einem Naturgemälde der Tropenländer. Tübigen: F.G. Costa/F. Schoell.Google Scholar
Wakulinśka, M. & Marcinkowska-Ochtyra, A. (2020) Multi-temporal sentinel-2 data in classification of mountain vegetation. Remote Sensing, 12, 2696.Google Scholar
Wenger, S.J. & Olden, J.D. (2012) Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods in Ecology and Evolution, 3, 260–267.Google Scholar
White, H.J., Montgomery, W.I., Storchová, L., Hořák & D. & Lennon, J.J. (2018) Does functional homogenization accompany taxonomic homogenization of British birds and how do biotic factors and climate affect these processes? Ecology and Evolution, 8, 7365–7377.Google Scholar
Whittaker, R.H. (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30, 279–338.Google Scholar
Wolf, C., Bell, D.M., Kim, H., et al. (2021) Temporal consistency of undercanopy thermal refugia in old-growth forest. Agricultural and Forest Meteorology, 307, 108520.Google Scholar
Yackulic, C.B., Chandler, R., Zipkin, E.F., et al. (2013) Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution, 4, 236–243.Google Scholar
Yates, K.L., Bouchet, P.J., Caley, M.J., et al. (2018) Outstanding challenges in the transferability of ecological models. Trends in Ecology & Evolution, 33, 790–802.Google Scholar
Yu, H., Luedeling, E. & Xu, J. (2010) Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proceedings of the National Academy of Sciences, 107, 22151–22156.Google Scholar
Zellweger, F., Braunisch, V., Baltensweiler, A. & Bollmann, K. (2013) Remotely sensed forest structural complexity predicts multi species occurrence at the landscape scale. Forest Ecology and Management, 307, 303–312.Google Scholar
Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. (2019) Advances in microclimate ecology arising from remote sensing. Trends in Ecology & Evolution, 34, 327–341.Google Scholar
Zhao, Q., Royle, J.A. & Boomer, G.S. (2017) Spatially explicit dynamic N-mixture models. Population Ecology, 59, 293–300.Google Scholar
Zohmann, M., Pennerstorfer, J. & Nopp-Mayr, U. (2013) Modelling habitat suitability for alpine rock ptarmigan (Lagopus muta helvetica) combining object-based classification of IKONOS imagery and Habitat Suitability Index modelling. Ecological Modelling, 254, 22–32.Google Scholar
Zurell, D. (2017) Integrating demography, dispersal and interspecific interactions into bird distribution models. Journal of Avian Biology, 48, 1505–1516.Google Scholar
Zurell, D., Graham, C.H., Gallien, L., Thuiller, W. & Zimmermann, N.E. (2018) Long-distance migratory birds threatened by multiple independent risks from global change. Nature Climate Change, 8, 992–996.Google Scholar

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