Further directions
Time-matched models have potential benefits beyond current estimations
of biodiversity distributions. One such benefit is the ability to
estimate how species are responding and will respond to climate change,
based on fewer years of future climatic projections.
Occurrence-environment matching approaches can capture extreme weather
events hidden in long-term averages, events which could drastically
affect population growth or even drive local extirpation depending on
the species (Frederiksen et al. 2008, Bauerfeind and Fischer 2014,
Marcelino et al. 2020). Studies have shown that even in cases where
models that incorporate the frequency of extreme events have similar
geographic predictions to standard average models in the current time,
they could differ considerably in future geographic estimations
(Moran-Ordonez et al. 2018). Given that the frequency of extreme events
is becoming higher due to global warming, studies that incorporate
extreme events intrinsically in model training (as in this study) or by
using extreme-derived variables (e.g., Zimmermann et al. 2009, Feldmeier
et al. 2018) should be considered in any conservation study that
accounts for climate change.
Alongside investigating the differences between time-matched and
standard approaches in climate change impacts on species ranges, further
studies should explore the benefits of using time-matched remote sensing
data. Derived remote-sensing variables such as tree forest cover (Hansen
et al. 2013) or the Normalized Difference Vegetation Index may be used
together with temperature and precipitation at the exact same temporal
resolution to get almost real-time estimations of habitat suitability
(Crego et al. 2022, Paz et al. 2022). For instance, based on the
positive results using a ten-year resolution for estimating the C.
mexicanus distribution, time-matching of traditional bioclimatic
variables could be combined with the two decades of data captured by
remote sensing instruments. When sufficient recent occurrence data
exist, this represents a viable pathway that holds advantages over the
common alternative of building a climate-only model and post-processing
it by masking out regions no longer holding suitable habitat (e.g.,
based on remote sensing; Merow et al. 2022). This line of further
research integrating disparate environmental data streams and temporal
matching could enable us to obtain better model predictions and forecast
how species respond to the changing environment.