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\begin{document}

\title{The need for IUCN species distribution update - the case from a large
threaten ungulate in Southwest China}



\author[1]{Tianpei Guan}%
\author[2]{Jacob Owens}%
\author[1]{Jian Yang}%
\author[1]{Kong Yang}%
\author[3]{Xiaodong  Gu}%
\author[4]{Yanling Song}%
\affil[1]{Southwest Minzu University}%
\affil[2]{The Los Angeles Zoo \& Botanical Gardens}%
\affil[3]{ Forestry Department of Sichuan Province}%
\affil[4]{Institute of Zoology Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Species distributions are one of the fundamental factors needed for
understanding and conserving wildlife. While the IUCN Red List of
Threatened Species is the primary applied reference for biodiversity
conservation, limitations in data availability and analyses of the
distributions of some species may limit accurate threat classification
assessments and conservation recommendations. Improving the accuracy of
species distributions in light of growing data and analytical methods is
a key step to increasing the efficacy of the Red List. In this study, we
reassessed the distribution of takin (Budorcas taxicolor tibetana), a
large ungulate in Sichuan Province, southwest China , classified by the
IUCN as vulnerable. Using species distribution models and reported
habitat requirements, we updated the takin distribution map. Our updated
distribution range in the study area (79,449km2) was 61.31\% of current
distribution range (CDR) on the IUCN red list. This reduction was in
large part due to the inclusion in the CDR of substantial areas of
lowland plains, high human disturbance, and non-forest habitat, which
provide no suitable habitat for takin. According to our results,
suitable takin habitat covered 18.97\% of the CDR, suggesting a
substantially over-estimated distribution. However, there are high
proportions of habitat (40\%) still covered by the nature reserve
network, indicating the importance of protected areas (PAs) in
conservation threaten species. We recommend that experts apply the basic
approach presented herein to update the Red List distributions for more
species to increase the accuracy of assessments and resulting
conservation applications.%
\end{abstract}%



\sloppy


The need for IUCN species distribution update - the case from a large
threaten ungulate in Southwest China

Guan tianpei\textsuperscript{1\#}, Jacob R Owens\textsuperscript{2},
Yang Kong\textsuperscript{1},Yang Jian\textsuperscript{1}, Gu
Xiaodong\textsuperscript{3}, Song yanglin\textsuperscript{4\#}

(1. Institute of Qinghai-Tibetan Plateau, Southwest Minzu University,
Chengdu 610225,China)

(2. Los Angeles Zoo and Botanical Garden, Los Angeles, CA, 91602, USA)

(3.Sichuan Station of Wildlife survey and Management, Chengdu 610082,
China )

(4.Institute of Zoology, Chinese Academy of Sciences, Beijing 100101,
China )

\# Corresponding to: \emph{tp-guan@hotmail.com;} songyl@ioz.ac.cn

\textbf{Abstract:} Species distributions are one of the fundamental
factors needed for understanding and conserving wildlife. While the IUCN
Red List of Threatened Species is the primary applied reference for
biodiversity conservation, limitations in data availability and analyses
of the distributions of some species may limit accurate threat
classification assessments and conservation recommendations. Improving
the accuracy of species distributions in light of growing data and
analytical methods is a key step to increasing the efficacy of the Red
List. In this study, we reassessed the distribution of takin
(\emph{Budorcas taxicolor tibetana} ), a large ungulate in Sichuan
Province, southwest China , classified by the IUCN as vulnerable. Using
species distribution models and reported habitat requirements, we
updated the takin distribution map. Our updated distribution range in
the study area (79,449km\textsuperscript{2}) was 61.31\% of current
distribution range (CDR) on the IUCN red list. This reduction was in
large part due to the inclusion in the CDR of substantial areas of
lowland plains, high human disturbance, and non-forest habitat, which
provide no suitable habitat for takin. According to our results,
suitable takin habitat covered 18.97\% of the CDR, suggesting a
substantially over-estimated distribution. However, there are high
proportions of habitat (40\%) still covered by the nature reserve
network, indicating the importance of protected areas (PAs) in
conservation threaten species. We recommend that experts apply the basic
approach presented herein to update the Red List distributions for more
species to increase the accuracy of assessments and resulting
conservation applications.

\textbf{Key words:} Red list, geographic distribution, Giant panda,
Protected Area,

The distribution of a given species is the result of long-term
interactions with natural processes (Morris 2003) and both short---and
long-term anthropogenic impacts (Stabach et al., 2016). Numerous species
are threatened due to habitat loss and population decline caused by
anthropogenic changes across the global landscape and the
over-exploitation of natural resources (Moyle \& Williams, 2010;
S\selectlanguage{ngerman}ánchez-Bayo \& Wyckhuys,2019). Therefore, spatial distributions are one
of the very basic characteristics needed to understand the natural
history and ecology of any given species (Grenyer et al., 2006). They
are also dynamic, influenced by fluctuations in environmental
conditions, including climate (Perry et al.,2005), population growth
over time (Dou et al., 2013), and human disturbances (Moreno-Rueda \&
Pizarro, 2007; He et al., 2019). Thus, contemporary knowledge of the
dynamics of a species' distribution is a key indicator of their
conservation status, and critical to achieving desired outcomes (Liu et
al.,2020) and guiding resource use for conservation actions regionally
(Li et al., 2013; Myers et al., 2000). Unfortunately, species with
limited data on their population and distributions are still common
(IUCN 2020), reducing the accuracy and reliability of their respective
conservation status assessments (Beresford et al., 2011, Syfert et al.,
2014).

The International Union for Conservation of Nature (IUCN) Red List
Categories are internationally recognized as the standard for assessing
the extinction risk of species (Butchartet al. 2005; Mace et al. 2008).
One of the five standard assessment criteria in the Red List, which
determines the ``Threatened Category'' (e.g. critically endangered,
endangered, vulnerable, etc.), is the geographic range of the species
based on their extent of occurrence (EOO) and/or area of occupancy (AOO)
(IUCN 2019). EOO are estimated by minimum convex polygons (MCP) with
well sampled locations (Ramesh et al., 2017), providing the total area
the species may be present, including unsuitable or disjunct
(unoccupied) areas. AOO are a closer reflection of the actual habitat
used by the species because they are based on suitability models or
occupancy data within the EOO. Not all threaten species are well sampled
throughout their potential range, and their geographic distribution
delineated by MCP may differ significantly from reality, providing a
clear opportunity to improve our conservation assessments and actions
for those species (Pena et al., 2014). Therefore, potential improvements
to these data limitations and species distributions should be a priority
for species assessments, particularly in developing countries and
species lacking sufficient research.

The takin (\emph{Budorcase tibetanus} ) is a rare species of ungulate
found only in Asia, distribute along the south and east edge of
Qinghai-Tibetan plateau, including India, Myanmar and Bhutan. Due to
poaching, deforest and habitat isolation, takin are considered as
vulnerable by IUCN and listed in appendix II of Red list(Guan et
al.,2013), top level of national protected wildlife of China (Jiang et
al.,2016). Due to its massive body size and group-living behavior, takin
were once the most frequently poached mammal within its range, leading
to drastic declines in their populations and distribution in the last
century (Wu et al.,1990). There are currently four takin subspecies
recognized, including the Sichuan takin (\emph{B. t. tibetana} ) the
most widely distributed and abundant takin subspecies (Zeng et al.,
2003) found in primarily in Sichuan Province, with minor portions of
their range extending into Gansu Provinces (Guan et al., 2015). Data on
their population size and distribution range is limited, restricting the
accuracy of threat assessments and conservation actions, including
effective habitat management.

Review of recent studies on takin and the diversity and distribution of
large ungulates in Sichuan Province (Ge et al., 2011; Guan et al. 2013;
Guan et al., 2015; Hu et al., 2018; Hu et al., 2019; Chen et al., 2019)
indicated that the actual distribution of Sichuan takin should be much
smaller than indicated on the IUCN Red List, due to basic known
environmental limits for the species (e.g. elevation, distance to human
disturbance, etc.). Thus, an update of their geographic distribution is
essential. Furthermore, the Sichuan takin is sympatric with the giant
panda (\emph{Ailuropoda melanoleuca} ), a flagship species of global
wildlife conservation. Serving as an umbrella species, giant pandas
benefit from numerous protected areas (PAs) across their range, within
which extensive measures are taken to protect and restore habitat and
connectivity (Wang et al.,2021). In addition to updating the
distribution of Sichuan takin to be more accurate, conservation planning
and recovery efforts would be bolstered by determining the ratio of
takin habitat under protection by giant panda nature reserves or other
PAs (Guan et al, 2015; Zhang et al., 2020).

In this study we had two primary objectives. First, we sought to update
the distribution of Sichuan takin, from an EOO to AOO, providing new
data necessary to more accurately assess their IUCN threat
classification and plan conservation efforts. Second, we compared the
results of our distribution, estimated via an ecological modeling method
based on basic habitat requirement data, to the current distribution
range (CDR) based on an MCP, to demonstrate the vast discrepancies
between these assessments. Our goal is to both directly improve the
conservation of Sichuan takin and encourage the broader conservation
community to re-examine our standards for estimating species
distributions.

\textbf{Study area}

Assessment of the distribution of Sichuan takin in this study was
limited to the core takin population, located in Sichuan Province(Figure
1), as environmental data availability in the provinces of Gansu was
restricted. Sichuan Province is located in southwest China, and is
characterized by a varied topography, including a large flat basin,
swaths of hilly landscapes, and high mountains and plateaus, stretching
from less than 400m asl to higher than 4000m asl. The heterogeneous
terrain has resulted in a landscape with numerous distinct environments
and high species diversity and richness (Zhang et al., 2009). Several
species are endemic or particularly abundant in this region, including
Sichuan takin, giant panda, golden snub-nosed monkey
(\emph{Rhinopithecus roxellana} ), Tibetan macaque (\emph{Macaca
thibetana} ), and Chinese monal (\emph{Lophophorus lhuysii} ). To date,
46 giant panda nature reserves have been designated in Sichuan, covered
5.24\% of the total area of the province (Forestry Department of Sichuan
Province, 2015). Due to its high richness of biodiversity, and as the
main component of the mountains of southwest China, large areas of
Sichuan are considered a global biodiversity hotspot (Myers et al.,
2000; Noroozi et al., 2018).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Current published Takin distribution in Sichuan province with
reference to giant panda distribution range (IUCN data)

\textbf{Data source and Analyses}

Data source

The spatial distribution data for evaluation were downloaded via the
IUCN website (https://www.iucnredlist.org/). To produce a suitable
habitat distribution map (SDM), we utilized MaxENT (v.2.0). All takin
occurrence data were collected from two sources: data prior to 2015 were
from the National Giant Panda Survey (Forestry Department of Sichuan
Province, 2015), and data from 2015 to 2020 were provided from Forestry
department of Sichuan and collected during ongoing biodiversity
monitoring surveys in each giant panda nature reserve in Sichuan.
Monitoring of the giant panda nature reserves was routine work performed
by local authorities and under the oversight of provincial and national
management plans. The surveys focus on giant pandas and their sympatric
species, and are typically conducted seasonally or every six months,
using an existing network of transects (Gu et al. 2003; Zheng et
al.,2012). More details about the giant panda surveys are found in the
related reports (Forestry Department of Sichuan Province,2015).

Table 1 Data and source applied during evaluation and analysis\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Data type & Source & Description\tabularnewline
Distribution range & IUCN Red List
website(https://www.iucnredlist.org/) & Spatial distribution range for
both takin and giant panda\tabularnewline
Species occurrence & Giant panda survey and monitoring records in nature
reserves, data copied holder with permission & Takin presence point
locations\tabularnewline
Vegetation & Finer Resolution Observation and Monitoring of Global Land
Cover (http://data.ess.tsinghua.edu.cn/fromglc2017v1.html) & Forest
distribution\tabularnewline
Terrain & Extracted from DEM, and DEM was downloaded via Geographic Data
Cloud (http://www.gscloud.cn/sources/accessdata/310?pid=302 & Elevation,
slope\tabularnewline
Disturbance data & Download from national geomatics center of China with
permission (http://www.ngcc.cn/ngcc/) & Main roads, towns\tabularnewline
Climate data & Download from worldclim
website(https://www.worldclim.org/) & V.2.0, 30 second spatial
resolution\tabularnewline
\bottomrule
\end{longtable}

Modelling Procedure

To build the model, the authors referred to the protocols suggested by
various studies (Bradie \& Leung,2017; Halvorsen 2013; Kaky et al.,2020;
Zhang et al., 2019), and selected several basic environmental factors,
including terrain, vegetation, human disturbance variables, and climate
data (Table 1), which are widely available for many locations around the
world. MaxENT was adopted to develop the models (Elith et al., 2011;
Phillips \& Dud\selectlanguage{ngerman}ík, 2008), as it is a widely applied method to predict
species distributions and proven to be reliable for numerous taxa (Kumar
\& Stohlgren, 2009; Onojeghuo et al., 2015; Khadka et al., 2017; Thapa
et al., 2018). Before running MaxENT, all relevant accessible factors in
the dataset were given full consideration and transformed into raster
format (90m × 90m), using UTM coordination system. A total 19 climate
variables, 2 terrain variables (elevation, slope), vegetation (forest),
2 disturbance variables (distance to towns, density of main roads) were
included in the analysis. A jack-knife method based on 10 repetitions
was used to evaluate model performance, using the AUC value to evaluate
the performance of the models. Finally, we adopted the average results
of the 10 repetitions. The threshold value to divide the suitability
into suitable habitat or unsuitable habitat was based on the Maximum
Youden index (Fluss et al., 2005).

To improve the accuracy of takin distribution map, a suitable habitat
distribution map based on the final model was created. The raw takin
habitat map was created after converting the resulting raster file into
a shape file, which was overlayed with 5 kmx5km polygons with the
resulting habitat suitability value. All known takin occurrence
locations were used to generate a raster map based the euclidean
distances to the locations and divided the raster value into 5 classes
using 10km intervals. Based on the takin distribution map and euclidean
distance map, polygons at the edge of suitable habitat were identified.
The edge was selected where habitat present or distance to nearest
location less than 20km, producing a species distribution polygon with
jagged edges. To smooth the shape and cover as much potential habitat in
the distribution range, the edge angles were modified to those less than
90 degrees or equal to 90 degrees by connecting consecutive corners with
a straight line, making all angles larger than 90 degrees. After
smoothing, this map was overlapped with CDR of takin from the IUCN and
considered the overlapped area as the updated distribution range (UDR).

Conservation status evaluation

To evaluate the relationship between takin habitat distribution and
protected area contribution in Sichuan province, the size of
distribution range and suitable habitat within nature reserves was
calculated. The number and size of PAs in five major mountains were also
calculated, to quantify the relative contribution of takin habitat under
some level of conservation protection.

\textbf{Results}

Current distribution and suitable habitat distribution predictions

According the distribution map from IUCN (Fig1), takin range in Sichuan
Province comprised 129,582 km\textsuperscript{2} within three distinct
patches. The largest patch was located in middle of Sichuan (Patch A),
covering 114,038km\textsuperscript{2}, or 88\% of takin distribution in
Sichuan. Patch B and patch C covered 5,857km\textsuperscript{2} and
9,709km\textsuperscript{2} respectively.

Table 1 Analysis of variable contributions in models\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Variable & Explanation & Percent contribution (\%) & \textbf{Permutation
importance (\%)}\tabularnewline
bio12 & Annual Precipitation & 10.6 & \textbf{26.6}\tabularnewline
Dem & Digital elevation model & 18.2 & \textbf{24}\tabularnewline
bio04 & Temperature Seasonality & 4.3 & \textbf{7.1}\tabularnewline
Distown & Distance to towns & 8.6 & \textbf{6.9}\tabularnewline
bio14 & Precipitation of Driest Month & 0.2 &
\textbf{6.7}\tabularnewline
bio10 & Mean Temperature of Warmest Quarter & 7.3 &
\textbf{6.1}\tabularnewline
bio06 & Min Temperature of Coldest Month & 12.3 &
\textbf{3.5}\tabularnewline
bio15 & Precipitation Seasonality & 25.6 & \textbf{3.1}\tabularnewline
bio09 & Mean Temperature of Driest Quarter & 3.9 &
\textbf{2.3}\tabularnewline
bio02 & Mean Diurnal Range & 1.3 & \textbf{2}\tabularnewline
bio01 & Annual Mean Temperature & 0.3 & \textbf{2}\tabularnewline
Accumulated contributions & & 92.6 & \textbf{90.3}\tabularnewline
\bottomrule
\end{longtable}

With 10 repeats, the AUC was 0.951, indicating a reliable performance of
the model. Among the candidate environmental variables, annual
precipitation and elevation were most important indicators of takin
distribution, based on the resulting permutation importance estimations
(Table1). Temperature, seasonality, distance to towns, precipitation of
driest month, and mean temperature of warmest month also impacted takin
distributions.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

The model predicted there were 24,583km\textsuperscript{2} classified as
takin suitable habitat(Figure 2), equaling 18.97\% of CDR. The number of
patches larger than seasonal home-range of takin
(5km\textsuperscript{2}; Guan et al.,2015) was 122, covering
23,710km\textsuperscript{2} or 96.45\% of the UDR. The total habitat
size in patch B was less than 7km\textsuperscript{2}, and the largest
contiguous habitat within patch B (2.8km\textsuperscript{2}) is much
less than takin annual home range (15km\textsuperscript{2}, Guan et
al.,2015). There was no suitable habitat in patch C.

Figure 2 Takin habitat prediction in Sichuan with reference to current
distribution range and environment limits.

Takin distribution update By delineating the grids corners on the edges
(Figure 3), we created a range based on conservative predictions and
known environment limits. Then, after overlapping the range with polygon
published in the IUCN Red List, we derived the updated map of takin
distribution (Figure 4). The size of UDR of takin in Sichuan was
79,449km\textsuperscript{2}, 61.31\% of the total area of the CDR.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} Example of the method used to smooth the distribution
edge by delineating consecutive corners of rectangles.

Conservation status\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Within the UDR, there were 44 giant panda related nature reserves in
five mountains, totaling 24,290km\textsuperscript{2}. Relative to these
PAs, 20,834km\textsuperscript{2} (26.22\%) of the area they comprise was
located in the UDR. Thus, 86.95\% of the PAs protected 40.3\% of takin
suitable habitat in Sichuan (Table 2), with an average patch size of
240.4 \selectlanguage{ngerman}±36.2 km\textsuperscript{2}. Among these five mountains, Minshan
and Qionglai covered 35.8\% suitable habitat and 88.83\% of all the area
contributed by PAs. By contrast, Daxiangling, Xiaoxiangling and
Liangshan contributed less than 1\% suitable takin habitat. The nature
reserve that comprised the largest suitable habitat area was Wolong in
Qionglai Province (4.86\%, 1,195km\textsuperscript{2}), following by
Baodinggou (3.2\%, 788 km\textsuperscript{2}), Baiyang (2.36\%,
581km\textsuperscript{2}) and Xuebaoding (2.34\%,
573km\textsuperscript{2}).

Figure4. Suggested updated takin distribution range (UDR) in Sichuan,
China

Table 2 The contributions of protected areas to takin distribution and
suitable habitat in the five mountain ranges occupied by takin in
Sichuan Province. Percentages indicate the proportion in each mountain
relative to the total in Sichuan.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Mountains & Distribution range (km\textsuperscript{2})/Percentage &
Suitable habitat (km\textsuperscript{2})/Percentage & Size of PAs &
Number of NPA* & Number of PPA\textsuperscript{\#}\tabularnewline
Minshan & 9481/11.93\% & 5867/23.86\% & 10705 & 9 & 10\tabularnewline
Qionglai & 5390/6.78\% & 2936/11.94\% & 5766 & 3 & 5\tabularnewline
Daxiangling & 671/0.84\% & 480/1.95\% & 674 & 0 & 2\tabularnewline
Xiaoxiangling & 3037/0.38\% & 508/2.06\% & 4933 & 2 & 2\tabularnewline
Liangshan & 2244/0.28\% & 116/0.47\% & 2212 & 3 & 3\tabularnewline
Total & 20834/26.22\% & 9908/40.3\% & 24290 & 17 & 22\tabularnewline
\bottomrule
\end{longtable}

* NPA=national protected area, \#PPA=provincial protected area

\textbf{Discussion}

The takin is a large montane species with a seasonal migration (Guan et
al.,2013), and preference for habitat with forest cover and low human
disturbance (Guan et al., 2015). The previously published species
distribution map used in the threat classification in the IUCN Red List
includes large areas of distribution in the Sichuan Basin plain,
characterized by relatively low elevation and a high density of human
disturbances, where no suitable takin habitat exists. Our analysis,
based on readily available environmental data and a standard habitat
suitability modeling approach, indicate that the CDR in Sichuan is
substantially larger than its likely realized distribution(Figure 2).
Our informed prediction of the total area of habitat available was only
19\% of the area used to determine its threat classification,
potentially vastly underestimating their extinction risk. Therefore, we
suggest two steps to adjust Sichuan takin CDR and improve the accuracy
of the IUCN Red List assessment. First, eliminate patch B and C where
there is no effective habitat for takin. Second, shrink the east range
of area A based on the boundary of these results and the known elevation
lower limits (1,000m).

Our approach in this study was conservative. In reality, takin
populations are sensitive to intense human disturbances (Zeng et al,
2008, Guan et al.,2015) and their populations are strongest within the
nature reserve network and nearby forests that are protected by rangers
from extensive human activities. This is supported by both the species
distribution model results of this study, which indicated that more than
40\% suitable habitat was located within PAs, and our previously
published distribution, in which we found 77\% of suitable takin habitat
was located within 3km from the border of PAs in northern Minshan (Guan
et al.,2015). It is likely that further species distribution models,
with additional habitat disturbance variables, would indicate that the
real world distribution of takins would be much smaller than current
maps.

The IUCN Red List assessment is the most widely applied standard and
reference for wildlife conservation used to determine priorities for
conservation and ecological studies (Pimm et al., 2014). Thus, the
accuracy of Red List assessments has global impact. The distribution of
takin in our reassessment makes a clear case for the use of basic
occurrence data to reduce inaccuracy, as about 40\% of the CDR was
outside of their possible distribution area. By contrast, giant panda
distribution range in the same region is much more elaborate in shape
and closer to their real world distribution (Tang et al., 2020), as the
result of enormous monitoring and focal surveys (Zheng et
al.,2012;Foretry Department of Sichuan Province,2015 ). Such condition
demonstrate that the accuracy of estimats are highly varied among
species, even those that are sympatric.

Species distributions are extremely important topics in both biology and
ecology (Jetz \& Guralnick, 2012; Pimm et al., 2014; Schluter\& Pennell,
2017), since they evolve through both long-term evolutionary pressures
and interactions with contemporary environmental and anthropogenic
impacts. Thus, they are also a primary concern in wildlife conservation.
For many common species on Red List, the occurrence data for
distribution assessments and population estimates are still lacking
(Roberts et al., 2016; Ocampo-Pe\selectlanguage{ngerman}ñuela et al., 2016; Popov et al.,2017).
To date, there are 17,878 species assessed by IUCN classified as ``Data
Deficient'', though comprising only 17.8\% of all assessed species (IUCN
2020). As more research is done this percentage decreases, supporting
more reliability in conservation assessments and more effective
planning. We recognize that the data for species assessments often
originate from widespread and long-term surveys, which are dependent on
numerous factors, including the number of individuals participating in
the research, support from pertinent stakeholders, and funding
availability. However, as even the most conservative estimates indicate
that the rate of extinction is 100 times higher than the normal
background rate between the Earth's six mass extinction events
(Ceballos, et al., 2015). It is imperative that more targeted studies
and surveys are completed to rapidly produce the critical data for
conservation. Our results are likely true for numerous species, and
there are significant opportunities for IUCN Red List species
distribution estimates to be improved by including even basic biological
limits and conservation dependencies (e.g. takin are not found within
cities or other densely populated areas or below 1000m asl) into
distribution maps (Sillero et al.,2014; Syfert et al.,2014; Ramesh et
al.,2017;Gomes et al.,2018).

Biodiversity conservation has often been seen to conflict with economic
development, lead to relatively limited conservation-related investment
from governments, particularly those of developing countries. With the
global economy growth and poverty reductions, long term monitoring and
survey work in large areas become more affordable and feasible (Cordero
et al., 2020); the body of related data is growing. For example, a
recently deployed widespread infrared camera monitoring network in China
has contributed national species distribution data (Li, 2020). However,
the practical use of these data into species assessments and
conservation actions remain to be seen. As data becomes increasingly
available, it is absolutely essential that ecologists, biologist,
conservation scientists and other organizations share data through
transparent collaborations to improve our capacity to assess and protect
wildlife.

Conclusion

By using the takin as a case study, we found significant disparities
between their current published range in the IUCN Red List based on
basic habitat requirements of the species. These differences can have
substantial implications on the conservation of the species, including
its threat classification, prioritization for funding and research,
political and public attention, and more. Our findings related to the
takin are likely exemplary of numerous species, even those not
classified as ``Data Deficient.'' We strongly encourage an increased
emphasis on widespread assessment of species ranges, even with only the
most basic and intuitive data, to increase their accuracy and efficacy
in conservation planning.

\textbf{Acknowledgment:} This work was funded by National Natural
Science Foundation of China (31300319). We are grateful for the support
from the Forestry Department of Sichuan Province, including data, and
extend our thanks those who conducted field surveys and persistent
monitoring in takin range for years, constructing the very foundation of
our study.

\textbf{Data Accessibility}

Shape files for the MaxENT and final maps are uploaded at Dryad,
Dataset, https://doi.org/10.5061/dryad.n8pk0p2xd

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