Discussion
This study found differences in the performance and ecological
plausibility of ENMs that incorporated temporal occurrence-environment
matching compared to models that used a standard 30-year average.
Using Cryptotis mexicanusas a use-case, the results indicated that the ten-year temporal
resolution performed similar to the standard 30-year average approach
based on cross-validation AUC, CBI and OR (with no significant
difference). Moreover, the results even showed an improvement in
performance when considering the fully withheld omission rate, with an
average rate that is lower by 0.1 than the standard approach. However,
given the significantly lower AUC values of other shorter temporal
resolutions (one and five years), this result may not be generalized to
all temporal resolutions. The ecological plausibility interpretation
supported the findings of model performance, showing that the ten-year
resolution had as much of a realistic geographical prediction of
suitability as the standard 30-year approach for almost all the known
shrew range, while the shorter temporal resolutions resulted in
unrealistic estimates of the potential shrew distribution.
Time-matched approaches have proven successful in obtaining reliable
model predictions for other species of small non-volant mammals (with
low vagility), but none of these studies compared the performance and
ecological plausibility of different temporal resolutions, as presented
in this study. For instance, time-specific models of American Pika
(Smith et al. 2019) and Australian Bettongs (Bateman et al. 2012)
achieved favourable results using resolutions of ten and five years,
respectively. The observed differences in model performance across the
three temporal resolutions here suggest that using an uninformed
time-matched approach will not translate into better models for small
non-volant mammals or other species with low mobility. Ideally, the
temporal resolution selection should be based on the species’ biology,
specifically the time lag in which a population could be impacted by the
change in the variables used in the model training (Levin 1992, Nadeau
et al. 2017). For instance, shorter-term resolutions, such as yearly,
monthly, or even daily resolutions (Reside et al. 2010, Abrahms et al.
2019), might be more useful for highly mobile species (Ingenloff and
Peterson 2021) than for species of low mobility, such as C.
mexicanus . Unfortunately, most species worldwide lack the necessary
demographic information to establish the time lag of population
response, although often information could be borrowed from
taxonomically related species with similar body sizes and natural
history information. As an alternative, time-matched models should
explore several temporal resolutions and tune them empirically.
For the ten-year temporal resolution, the modified version of the
target-group background obtained realistic predictions of the shrew’s
distribution, except for the northern region. This large area predicted
in the north is not expected, as it corresponds to xeric shrublands and
mesic lowland areas in which populations of C. mexicanus never
have been found. This could be due to the minimal representation of the
background data in the northern area (as seen in Fig. 3b), which may
fail to capture the full range of environmental conditions due to
under-sampling by biologists. In such cases, the target-group background
tends to overcompensate for the least sampled areas by increasing the
number of false positives (Syfert et al. 2013, Barber et al. 2022). This
tendency can be observed even in the standard 30-year average when used
to correct for spatial bias, but it increases when also correcting the
temporal bias in occurrence-environment approaches. Furthermore, while
the model performance may be affected by occurrences removed because
they fell outside the temporal range between 1971 and 2000, the
time-matched approaches allow the use of occurrences traditionally
discarded in model training, such as occurrences from the exact location
but captured at different times, which could experience different
environmental conditions. This situation can benefit species with a
restricted distribution or where spatial thinning (Boria et al. 2014)
may significantly reduce the occurrence data available for modelling.