Introduction
Species distribution models (SDMs), as a capital method in biogeography
research, are widely used in the response of target species under global
climate change, the potential predicted distribution of invasive
species, and the identification of conservation priorities (Araújo &
Guisan, 2006; Austin & Van Niel, 2011; Franklin, 2013; Guisan et al.,
2013; Guo et al., 2020). Known as ecological niche models, it’s a
mathematical model established by species occurrence data and
environmental information, estimating the ecological niche requirements
of species based on statistical information provided by sampling sites
and projecting to specific spatial and temporal regions to reflect the
degree of habitat preference of species in a probabilistic form and the
model results reflect the suitable habitats in the geospatial
distribution (Franklin, 2013; Guillera-Arroita et al., 2015; Guisan &
Thuiller, 2005; Guo et al., 2020; Naimi et al., 2014; Phillips et al.,
2006).
The correspondence of species locality and environmental information is
the crucial factor affecting the model performance (Abrahms et al.,
2019; El-Gabbas A et al., 2021; McCune & Baraloto, 2016; Ranc et al.,
2017). Since orchids could be considered flagship taxa for biodiversity
conservation due to their advanced evolution status, their biogeography
studies via SDMs are instrumental in identifying conservation priorities
and analyzing biogeographic patterns in biodiversity hotspots (Crain &
Fernandez, 2020; Luo et al., 2003). However, in the orchid SDMs,
researchers always use all occurrence data as sample inputs to the model
(Crain & Fernandez, 2020; Djordjevic et al., 2020; Djordjevic et al.,
2016; Faruk et al., 2021; Tsiftsis & Tsiripidis, 2020; Wan et al.,
2014). Orchids have broad ecological fitness and are dependent more on
the microenvironment (Kelly et al., 2013; Souza Rocha & Luiz Waechter,
2010). While biogeographic studies based on SDMs are usually conducted
at a large spatial scale, such as a global biodiversity hotspot,
signifying the target space is likely to contain enough heterogeneous
environments to provide enormous ecological space for a variety of
orchids. Meanwhile, physiological studies of orchids have shown distinct
ecological requirements between various orchids (McCormick & Jacquemyn,
2014; Zhang et al., 2018). The most recognized is that physiological
differences exist in different orchids’ lifeforms. Hence, from a
statistical point of view, the rough sample input may blur the
mathematical relationships corresponding to the occurrence data and
environmental information established by the model. Causing a bias would
increase the model uncertainty and then affect the model accuracy and
suitability maps.
Human activity is an issue of considerable concern in the biogeographic
research of orchids, often regarded as one of the threats limiting their
geographic distribution (Anibaba et al., 2022; Crain & Fernandez, 2020;
Djordjevic et al., 2020; Guisan & Thuiller, 2005; McCune & Baraloto,
2016; Pilar A. Hernandez, 2006). However, there is no exact method to
quantify or assess the impact of human activities on orchid distribution
in current research. This shortcoming may result in biases between the
predicted potential habitat generated by the lack of anthropogenic
dispersal constraints in predicting species distributions and the
potential geographic distributions (Franklin, 2023). In orchid
distribution pattern studies in Central America, model results show that
most orchid hotspots occur in the most densely populated provinces
(Crain & Fernandez, 2020). Although they can indicate the threat level
to orchids outside protected areas, the absence of verification of
ground truthing still does not rule out the possibility of prediction
uncertainty in models only under natural predictors (Eyre et al., 2022).
To further validate and confirm these issues, we used the case of the
Hengduan Mountains. This region is one of the global biodiversity
hotspots with a prominent representation of orchids amongst its flora.
Different modeling approaches and validation methods were employed to
explore the role of physiological characteristics and human activities
in orchid species distribution models (SDMs). The following questions
were addressed: 1. How to physiological characteristics and human
activities impact orchid SDMs? 2. How do these factors affect orchid
suitability prediction maps? 3. What are the orchid geographic
distribution patterns and critical locations in the Hengduan Mountains
based on different modeling strategies? These studies provide valuable
insight into the geographic distribution patterns of orchids in the
Hengduan Mountains and aid in assessing protected areas. Furthermore,
the results can inform the modeling process for other species or
regions.