The importance of classification modeling based on physiological
characteristics
Most orchids are in an actively evolving and specializing process from
the biologically evolutionary aspect and are generally regarded as the
flag group for biodiversity conservation (Luo et al., 2003). Their
diversity hotspot was proven to correspond to other taxon distribution
centers (Anderson et al., 2008; Gaskett & Gallagher, 2018; Seaton et
al., 2010). Consequently, analysis of orchids’ geographical distribution
via SDMs makes it possible to understand regional fundamental
geographical distribution patterns and identify priority conservation in
a biodiversity hotspot (Crain & Fernandez, 2020; Souza Rocha & Luiz
Waechter, 2010; Xing & Ree, 2017). SDMs are mathematical models
established by the targeted species occurrences as well as environmental
data that estimate the ecological niche requirements of species based on
statistical information provided by sampling sites and mapped to
specific spatial and temporal regions to reflect the degree of habitat
preference of species in a probabilistic form (Araújo & Guisan, 2006;
Dyderski et al., 2018; Elith & Leathwick, 2009; Guillera-Arroita et
al., 2015; Guisan & Thuiller, 2005; Guo et al., 2020; Ranc et al.,
2017). The model results are the response to their suitable habitat
distributions. However, the orchid family has shown their wide
ecological suitability (Souza Rocha & Luiz Waechter, 2010) and
significant physiological characteristics among different lifeforms
(McCormick & Jacquemyn, 2014; Zhang et al., 2018). From the statistical
point of view of SDMs, when we do not take measures to pretreatment the
orchid occurrences and directly input models, this would expand the
environmental information provided by the sampling sites and may obtain
an inaccurate and rough ecological requirement for orchids, thus
affecting the model accuracy and suitability maps.
This has been confirmed in this study. Different modeling strategies and
verification methods were adopted to test the physiological
characteristic’s effect on orchid SDMs. The result indicates that the
models’ accuracy would improve significantly when we confront and manage
the physiological features, especially in epiphytic and
mycoheterotrophic orchids. It is possible that the environmental
relationship and dependence of these two types can be better represented
by modeling separately. Another situation also proves the above
conjecture that, without pretreatment for orchids, it may erroneously
expand ecological niche requirements. In most of our model experiments,
the predicted suitability area of unclassified tended to be higher than
that results by the classification models.
Uncertainty in species distribution data is a factor that affects SDMs,
which commonly includes uncertainty in the location of species
occurrence, incomplete sampling, and selective bias (Guo et al., 2020).
In this study, we put forward another situation that will cause the
increase in model uncertainty: ignoring the pretreatment of targeted
species occurrences data with inherent physiological differences. Not
only limited to orchids, but the more precise matching of species
occurrence with environmental information is also essential for species
with distinct ecological preferences, which is more common in dynamic
SDMs studies of migratory animals in the ocean (El-Gabbas A et al.,
2021). We emphasize that when serving the prediction of suitable
habitats for target species using SDMs, in addition to optimizing the
model structure, adjusting the model parameters, and improving the
spatial resolution of the environment to improve the performance of
models, it is necessary and efficient to pre-process the data with the
physiological differences embedded in the occurrence.