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

Abstract

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.

A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land‐use change

“…The best fitting model suggests that shifting environmental associations of species may have been related to non‐equilibria starting conditions, because changes in distributions were shaped by environmentally distinct colonization and persistence processes (Yackulic et al., 2015). However, parameter variability may also arise from adaptation, plasticity, or the use of predictors that were not strong causal drivers (Guisan et al., 2014; Lovell et al., 2023). The importance of site‐specific latent variables and species‐specific loadings provides a further indication that several important drivers or processes were missing within our models; based on model selection patterns, residual site‐by‐time variation appeared more important than residual species‐by‐time co‐variation.…”

A space‐for‐time framework for forecasting the effects of ocean stratification on zooplankton vertical habitat use and trait composition

“…Although subregions of a dynamic upwelling region are unlikely to be at steady‐state, the kind of broader cross‐shore changes in density stratification that we analyze here are persistent features that can nearly always be found. Where SFTS is less likely to be useful is when environmental conditions cross a threshold or “tipping point” (sensu Scheffer et al 2009) that have not been sampled in a spatial survey, leading to new functional relationships (Lovell et al 2023). It is not clear when or whether such tipping points will be crossed in the present study region, but in the interim, the SFTS hypothesis provides one of the few tools available to forecast future ecosystem states based on empirical measurements.…”

“…The principle of SFTS can be traced to Charles Darwin's insights about formation of coral atolls as a progression from extinct volcanoes and fringing reefs (Damgaard 2019). While SFTS has been criticized for not considering historical legacy effects (Pickett 1989), other non‐equilibrium conditions (Damgaard 2019), and for not always resolving causal relationships (Lovell et al 2023), these same authors have endorsed its use with appropriate caution.…”

Modelling thermal sensitivity in the full phenological distribution: A new approach applied to the spring arboreal caterpillar peak

“…By separating the effects of temperature in space and time we can gain a window into whether effects are likely to be causal and insights into the processes at play (Lovell et al, 2023). For the mean timing parameter, similar estimates in space and time suggest temperature has a causal effect and is consistent with plasticity being responsible for much of the spatiotemporal variation in mean timing (Phillimore et al, 2010).…”

“…We also examine whether estimated thermal sensitivities over space and time are consistent with a causal effect (i.e. where slopes are similar in space and time Lovell et al, 2023). Finally, using derived parameters, we explore thermal sensitivity in the duration of and area under the full phenological distribution.…”