Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

Bradter, Ute; Johnston, Alison; Hochachka, Wesley M.; Soultan, Alaaeldin; Brommer, Jon E.; Gaget, Élie; Kålås, John Atle; Lehikoinen, Aleksi; Lindström, Åke; Piirainen, Sirke; Pavón‐Jordán, Diego; Pärt, Tomas; Øien, Ingar Jostein; Sandercock, Brett K.

doi:10.1111/gcb.16355

Cited by 6 publications

(5 citation statements)

References 119 publications

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“…Araújo et al, 2005;Dobrowski et al, 2011;Rapacciuolo et al, 2012;Brun et al, 2016;Mor an-Ord oñez et al, 2017), palaeodata (e.g. Pearman et al, 2008;Veloz et al, 2012), simulations (Qiao et al, 2019;Santini et al 2021) predictions made using spatial data and time series data (Isaac et al, 2011) and space-time comparisons (Oedekoven et al, 2017;Bradter et al, 2022).…”

Section: Validation Of Sftsmentioning

confidence: 99%

“…Pearman et al ., 2008; Veloz et al ., 2012), simulations (Qiao et al ., 2019; Santini et al . 2021), comparisons of predictions made using spatial data and time series data (Isaac et al ., 2011) and space–time comparisons (Oedekoven et al ., 2017; Bradter et al ., 2022).…”

Section: Validation Of Sftsmentioning

confidence: 99%

“…However, relatively few studies have explored directly how timescale affects SFTS performance using historical data (e.g. Roberts & Hamann, 2012; Blois et al ., 2013; Morán‐Ordóñez et al ., 2017; Bradter et al ., 2022) or in silico simulations (Adler et al ., 2020). Additionally, dynamic process‐based models (e.g.…”

Section: Opportunities For Sftsmentioning

confidence: 99%

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Space‐for‐time substitutions in climate change ecology and evolution

Lovell,

Collins,

Martin

et al. 2023

Biological Reviews

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In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long‐term biological data to use the past to anticipate the future, spatial climate–biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These ‘space‐for‐time substitutions’ (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate‐focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable – population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate–biotic relationships and (ii) the transferability of these relationships, i.e. whether climate–biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.

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Section: Validation Of Sftsmentioning

confidence: 99%

Section: Validation Of Sftsmentioning

confidence: 99%

See 1 more Smart Citation

Space‐for‐time substitutions in climate change ecology and evolution

Lovell,

Collins,

Martin

et al. 2023

Biological Reviews

View full text Add to dashboard Cite

show abstract

“…However, as far as we are aware, very few studies have used these underlying metrics to assess ENM performance in the context of climate change projections (Searcy & Shaffer, 2016). SFTS validation of ENMs has been limited to use of historic datasets (Araújo et al, 2005;Pearman et al, 2008;Dobrowski et al, 2011;Rapacciuolo et al, 2012;Brun et al, 2016;Morán-Ordóñez et al, 2017), simulations (Beale, Lennon & Gimona, 2008;Qiao et al, 2019), comparisons of predictions made using spatial data and time series data (Isaac et al, 2011) or space-time comparisons (Oedekoven et al, 2017;Bradter et al, 2022).…”

Section: Validation Of Species Distribution-focussed Sfts Typically I...mentioning

confidence: 99%

“…However, relatively few studies have directly explored how timescale affects SFTS performance (e.g. Roberts & Hamann, 2012;Blois et al, 2013;Morán-Ordóñez et al, 2017;Adler et al, 2020;Bradter et al, 2022). Addressing this represents an important avenue for future work, with the potential to utilise historical or in silico data.…”

Section: Opportunities For Sftsmentioning

confidence: 99%

Space-for-time substitutions in climate change ecology and evolution

Lovell¹,

Collins²,

Simón³

et al. 2022

Preprint

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In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is one of the most urgent challenges faced in ecology and evolution. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic associations are often used as a proxy for predicting biotic responses to climate change over time. These ‘space-for-time substitutions’ (SFTS) have become near ubiquitous in global change biology, but with different subfields having largely developed in isolation. We review how climate-focussed SFTS are used in four subfields of global change biology, each focussed on a different response type – population phenotypes, population genotypes, species’ distributions, and ecological communities. We identify the similarities and differences between the methods, the limitations and opportunities within each subfield, and highlight the potential for different subfields to gain insight from each other. While SFTS are used for a wide range of applications, two main approaches are applied across subfields: in situ gradient methods (including ecological niche modelling) and transplants (common gardens and reciprocal transplants). All SFTS methods and applications share a number of key limitations and assumptions relating to (i) the causality of identified spatial associations and (ii) the transferability of these relationships over time. Despite their widespread use, key assumptions in SFTS remain largely untested, including the fundamental assumption that climate-biotic relationships observed over space are causal and are equivalent to those occurring over time. We highlight how the robustness of SFTS can be improved by addressing these assumptions and limitations, with a particular emphasis on where approaches could be shared between subfields.

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