1 INTRODUCTION
The geographical distribution pattern of species and utilizing species
distribution models (SDMs) to predict the potential geographical
distribution of species are one of the hot research issues in the fields
of biogeography and biodiversity conservation (Gaston, 2000; Zhang &
Ma, 2008; Ning et al., 2018; Tripathi et al., 2019a). Studying the
geographical distribution pattern and predicting the potential
geographical distribution of species are of great significance for
developing effective biodiversity conservation strategies (Zhang et al.,
2019; Lazo-Cancino et al., 2020), preventing and managing the spread of
invasive alien species (IAS) diffusion (Fernandes et al., 2019) and
assessing the impact of climate change on species distribution
(Lazo-Cancino et al., 2020), and they are also an effective means of
protecting and managing
some
rare, endangered and/or threatened species.
The
maximum entropy (MaxEnt) model is a species distribution model based on
the environmental factors matching method, and is often used to predict
the potential geographical distribution of species.
The
MaxEnt model employs the data of species distribution locations and
environmental variables to jointly simulate the potential geographical
distribution of species and has many advantages over other species
distribution models, including easy operation and use, high simulation
precision, good performance with incomplete datasets (Li et al., 2020).
Currently, the MaxEnt is the most widely used species distribution model
(Merow et al., 2013; Gong et al., 2015; Ning et al., 2018). The research
results of Hernandez et al. (2006) and Deb et al. (2017) both showed
that in the case of few geographical locations (< 10), even as
low as 4 or 5, the MaxEnt model can still
produce
effective prediction results with high precision. At present, the model
has been applied to simulate and predict the
potential
geographical distribution of endemic species
(e.g.,Impatiens hainanensis ; Ning et al., 2018), national protected
species (e.g., Phellodendron amurense ; Huang et al., 2018) and
many other key species.
It is helpful to improve the scientific understanding of the spatial
relationship between species and environment by studying the impact of
different types of environmental factors on the potential geographical
distribution of species.
The
spatial distribution pattern of species diversity is related to
variations
in environmental factors (e.g., Latitude, elevation and climate)
(Tripathi et al., 2019a). However, the relationship between them often
has spatial non-stationarity (i.e., Relationship between independent and
dependent variables will change with geographical location) (Gouveia et
al., 2013). Geographically weighted regression (GWR) model, which is an
extension of traditional regression model
(e.g.,
Ordinary least squares, OLS) (Ștefănescu et al., 2017; Tripathi et al.,
2019a; Tripathi et al., 2019b; Xue et al., 2020), has become one of the
crucial spatial heterogeneity modeling tools (Lu et al., 2020). In
recent years, many domestic and foreign scholars have carried out
in-depth and extensive research in various fields by using GWR model,
including social environmental factors and regional economy, regional
house prices and pollution (McCord et al., 2018; Xu et al., 2019), the
impacts of environmental heterogeneity and land use change on wild
animal distribution (Liu et al., 2019; Wang et al., 2020; Xue et al.,
2020), vegetation activity and climate change (Gao et al., 2019).
However, there are few studies that
in combination MaxEnt with GWR models to analyze the potential
geographical distribution and explore environmental explanations for
some key species, especially in biodiversity hotspot areas.
According to the Living Planet
Report 2020
(https://www.wwf.org.uk/press-release/living-planet-report-2020),
published by World Wide Fund for Nature (WWF) and Zoological Society of
London (ZSL), population sizes of mammals, birds, fish, amphibians and
reptiles have fallen an average of 68% globally. Northwest Yunnan of
China is one of the global biodiversity hotspots (Myers et al., 2000; Ye
et al., 2020a; Ye et al., 2020b). Plenty of rare, endangered, threatened
and endemic species that need critical conservation are distributed in
this area (Yang et al., 2017). In recent decades, due to the
interference of human activities
(e.g.,
Mining
and collecting herbs) and the influence of the external natural
environment, especially climate change, the populations and
distribution
area of some
rare,
endangered and threatened species have been decreasing (Yu et al.,
2014). Living Planet Report 2020 also stated that from 2000 to 2018, the
species habitat index dropped by 2%, which indicated that the habitat
quality of species shows a general trend of rapid decline. In addition,
some species have the characteristics of geographical isolation and
narrow distribution (Wang et al., 2013), as a result, their survival and
development are faced with severe threats. Therefore, it is urgent to
protect these key species and their suitable habitats in this area. In
order to protect these key species reasonably and effectively, their
geographical distribution must be clear first. However, at present, we
are still unclear about the potential geographical distribution and main
limited environmental factors for these key species in Northwest Yunnan.
Therefore, in this study, 25 key species were the focus of analysis,
including some rare, endangered and/or national protected key higher
plant species in Northwest Yunnan.
Initially,
we adopted the method of combining the MaxEnt model together with GWR
model to predict the potential geographical distribution of each species
on the grid map with a resolution of 0.05° × 0.05° (for more details
about resolution, see Ye et al., 2020a). Then, we overlaid the potential
geographical distribution of each species, and obtained the potential
geographical distribution pattern of species richness on the spatial
scale of the ecological niche model with a resolution of 0.05° × 0.05°.
Ultimately, we analyzed
the
explanation capabilities of various environmental factors on the
potential geographical distribution patterns of these key species, and
explored the main restrictive environmental factors.
These
research results would contribute to a more comprehensive understanding
of the potential geographical distribution pattern of some key higher
plant species in Northwest Yunnan, and would provide a scientific basis
for the conservation and management of suitable habitat for rare,
endangered and many other key species in this region.