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\begin{document}

\title{Combining modern tracking data and historical records improves
understanding of the summer habitats of the Eastern Lesser White-fronted
Goose Anser erythropus}



\author[1]{Haitao Tian}%
\author[2]{Diana  Solovyeva}%
\author[3]{Gleb Danilov}%
\author[4]{Sergey Vartanyan}%
\author[5]{Li Wen}%
\author[1]{Jialin Lei}%
\author[1]{Cai Lu}%
\author[6]{Peter Bridgewater}%
\author[1]{Guangchun Lei}%
\author[1]{Zeng Qing}%
\affil[1]{Beijing Forestry University}%
\affil[2]{Institute of Biological Problems of the North FEB RAS}%
\affil[3]{Peter the Great Museum of Anthropology and Ethnography RAS}%
\affil[4]{North-East Interdisciplinary Science Research Institute FEB RAS}%
\affil[5]{NSW Department of Planning Industry and Environment}%
\affil[6]{University of Canberra}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The Lesser White-fronted Goose (Anser erythropus), smallest of the
``grey'' geese, is listed as Vulnerable on the IUCN Red List and
protected in all range states. There are three sub-populations, with the
least studied being the East Asian sub-population, shared between Russia
and China. The extreme remoteness of breeding enclaves makes them
largely inaccessible to researchers. As a substitute for visitation,
remotely tracking birds from wintering grounds allows exploration of
their summer range. Over a period of three years, and using highly
accurate GPS tracking devices, eleven individuals of A. erythropus were
tracked from the key wintering site of Dongting Lake, China, to
breeding, molting, and staging sites in north-eastern Russia. Data
obtained from that tracking, bolstered by ground survey and literature
records, were used to model the summer distribution of A. erythropus.
Although earlier literature suggests the summer range is patchy, the
model confirms a contiguous summer range. The most suitable habitats are
located along the coasts of the Laptev Sea, primarily the Lena-Delta, in
the Yana-Kolyma Lowland, and smaller lowlands of Chukotka with narrow
riparian extensions upstream along major rivers such as the Lena,
Indigirka and Kolyma. The probability of A. erythropus presence is
related to sites with altitude less than 500 m with abundant wetlands,
especially riparian habitat, and a climate with precipitation of warmest
quarter around 55 mm and mean temperature of wettest quarter around
14oC. Human disturbance also affects site suitability, with a gradual
decrease in species presence starting around 160 km from human
settlements. Remote tracking of animal species can bridge the knowledge
gap required for robust estimation of species distribution patterns in
remote areas. Better knowledge of species' distribution is important in
understanding the large-scale ecological consequences of rapid global
change and establishing conservation management strategies.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

The Lesser White-fronted Goose \emph{Anser erythropus} is the smallest
of the so-called ``grey'' geese of the genus \emph{Anser} (BirdLife
International 2018). Excluding threatened taxa, grey geese are
traditionally used for subsistence and sport hunting in Eurasia. Arctic
nations especially continue to consider geese as a sustainable source of
fresh meat in spring. However, hunting bans in many European countries,
Republic of Korea and Japan have allowed the various species of grey
geese to become part of agricultural landscapes. In contrast, several
species of grey geese in China prefer to winter on wetlands with
typically low levels of human use, rather than exploiting agricultural
lands that are densely populated by people and their livestock
(Deng\emph{et al.} 2018). Since 1994, following rapid population
reduction,\emph{A. erythropus} has been globally list as Vulnerable in
the IUCN Red List (BirdLife International 2018).

Three sub-populations can be distinguished: Fennoscandian, West Asian,
and East Asian, with potential overlap of the breeding grounds between
the West and East Asian sub-populations (Jones \emph{et al} . 2008).
Aarvak and Oien (2018) note that the Fennoscandian sub-population
appears on the brink of extinction with only 30-35 pairs left, despite
captive breeding and restocking in Finland and Sweden during 1981 --
1999 (Ruokonen \emph{et al} . 2000; Andersson and Holmqvist 2010). The
number of the West Asian sub-population assessed from counts at
stop-over sites during autumn migration has risen from an estimated
10,000-21,000 in early 2000s (Fox \emph{et al} . 2010) to 30,000-34,000
in 2015 (Cuthbert and Aarvak 2016) and perhaps as high as 48,580 \selectlanguage{ngerman}± 2,820
in 2017 (Rozenfeld \emph{et al} . 2019). However, this increase could be
attributed to additional survey efforts for \emph{A. erythropus} at
previously infrequently or un-visited staging sites in Kazakhstan. The
most recent estimate of the East Asian sub-population is 14,000-19,000
individuals (Jia \emph{et al.} 2016), accounting for around 25\% of the
global \emph{A. erythropus} population (Jia \emph{et al} . 2016 and
Rozenfeld \emph{et al} . 2019). The eastern sub-population of \emph{A.
erythropus} extends from the Taymyr Peninsula eastward to Chukotka
region (Morozov 1995; Morozov and Syroechkovski -Jr 2002; Lei \emph{et
al} . 2019a), and in common with other subpopulations, is declining
(BirdLife International, 2018). A range of threats, including habitat
loss and degradation along the migration route and on the wintering
grounds proposed to fragmentation of the formerly continuous breeding
range, have all been identified being responsible for past population
declines (Madsen \emph{et al} . 1984; Grishanov 2006; Morozov 2006). In
addition, illegal and accidental hunting (i.e. the genuine confusion
with the similar looking Greater White-fronted Goose \emph{A. albifrons}
, a species that can be hunted legally in Russia) are also threats to
population viability.

Quantitative knowledge of a species spatial distribution is the
cornerstone for its effective conservation. Due to the remoteness and
restricted accessibility, historical observations of the summer range of
the East Asian sub-population are rather scarce (Ruokonen \emph{et al} .
2004, Morozov 1995; Morozov and Syroechkovski -Jr 2002; Lei \emph{et al}
. 2019a) Further, there are no systematic surveys covering the potential
range of eastern sub-population of \emph{A. erythropus} (Fig. 1).
Current knowledge on the breeding distribution and habitat preference of
\emph{A. erythropus} is therefore limited (Egorov and Okhlopkov 2007,
Solovieva and Vartanyan, 2011, Degtyaryev \emph{et al.}2014). In the
last 25 years, ornithologists generally considered that the East Asian
\emph{A. erythropus} had a patchy breeding distribution, and the number,
position and shape of those areas changed as new knowledge was acquired
from occasional visits to remote sites in East Siberia as illustrated in
Figure 1. Furthermore, an intensive multi-year survey in the area
adjacent to the breeding grounds along the Rauchua River, West Chukotka,
helped locate a number of breeding/molting groups and separated broods,
suggesting that the entire survey area was populated by \emph{A.
erythropus} (Fig. 2). This suggests that a single survey in one year,
the usual method employed to study distribution of geese in remote areas
of East Siberia (Egorov and Okhlopkov 2007, Solovyeva and Vartanyan
2011), may not allow for an effective understanding of the summering
distribution, limiting potential conservation actions for the species.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1 Historical summer (breeding and molting) range of the eastern
sub-population of Lesser White-fronted Goose. Black contours indicate
known breeding or molting enclaves. A) from Morozov 1995; B) from
Morozov and Syroechkovski-Jr 2002; and C) from Cao \emph{et al} . 2018.

As new tracking technologies have developed, the investigation and
quantification of spatial and temporal distributions of wide-ranged
migratory species, such as \emph{A. erythropus} , now typically involve
the deployment of telemetric tracking devices (Jiguet \emph{et al} .
2011; Pimm et al. 2015). Rapid accumulation of tracking data offers new
insights to assess distribution ranges and to explore habitat
preferences (Kays \emph{et al} . 2015). For example, tracking data can
be linked with environmental conditions and used in ecological niche
models to predict the overall space use by a population (Jiguet \emph{et
al} . 2011). In this context, this paper aimed to quantify to the
potential summering range of the East Asian \emph{A. erythropus}
sub-population by combining GPS tracking data, historical ground survey
records, and literature sources. Using bioclimatic, geomorphological,
land cover, and human disturbance layers, we used Maxent (a niche
modelling technique, Elith et al. 2006), to predict the summering
habitats of \emph{A. erythropus} within East Siberia in an ensemble
forecast framework, i.e. averaging predictions from many models (100 in
this study) to account for data uncertainties and model variability
(Pearson et al. 2006). Niche models using both historical records and
recent tracking data could help to get better understanding of the
summering distribution of the East Asian \emph{A. erythropus}
sub-population, and provide more accurate information for conservation
plans including identifying potential threats and prioritizing
management actions.

\section*{Materials and methods}

{\label{materials-and-methods}}

\subsection*{Study Area}

{\label{study-area}}

The study area was in northeast Siberia, extending eastwards from
Olenyok R (119.2 E) to the watershed between the Pacific and Arctic
drainage basins, including Republic of Sakha, Magadanskaya Oblast and
Chukotskiy Autonomous Okrug. \emph{A. erythropus} was never reported in
in the Arctic Archipelagos, these island areas are excluded in our
study.

\subsection*{Surveys in West Chukotka,
Russia}

{\label{surveys-in-west-chukotka-russia}}

During July-August 2002-2019 surveys were undertaken along rivers and
lake habitats in the area of 19,260 km\textsuperscript{2} of
assumed\emph{A. erythropus} range in Chukotka (Figure 2). Brood-rearing
adult\emph{A. erythropus} with their brood or flocks of molting
adult\emph{A. erythropus} were counted during downstream travel in a
motorboat from the upper reaches of rivers, which were reached by
helicopter. A description of the study area and survey results of
2002-2010 have been previously published (Solovieva and Vartanyan 2011).
No \emph{A. erythropus} were found on lakes and only surveys along
rivers have been used in this study (Figure 2). Positions and numbers
of\emph{A. erythropus} were given as (1) middle point and peak number
for each river from surveys in multiply years; (2) middle point and
number per river from single survey for the rivers surveyed once. As
rivers of the study area are relatively small (up to 320 km) and uniform
by habitat type, we considered each river as one data point for the
niche modelling. These surveys provided 11 records for the model
comprising eight breeding records and three molting records.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2 Survey route and peak counts of the Lesser White-fronted Geese
on the rivers of West Chukotka, 2002-2019.

\textbf{Data extraction from published sources}

A total of 13 records of breeding or molting \emph{A. erythropus} were
compiled from historical surveys along the rivers dated after 1998.
Originally 11 of these records were not attributed to GPS coordinates
and to geo-reference them, we converted descriptions of records (river
name and distance to the nearest village) to coordinates.

\subsection*{Capture methods and data
tracking}

{\label{capture-methods-and-data-tracking}}

Using techniques described in Lei \emph{et al} . (2019a),
individual\emph{A. erythropus} captured, during the winter of 2016/17 at
East Dongting Lake, China. This lake is the most important wintering
site for the species, supporting more than 70\% of the East Asian
sub-population (Wang \emph{et al} . 2012). A Total of 88 \emph{A.
erythropus} were captured and tagged by experienced hunters using baited
clap traps, and 11 individuals returned with a completed
wintering-migration-summering-migration-wintering cycle (Table 1).

Table 1. Summary of eleven tagged Lesser White-fronted Geese used for
this study.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
ID & Capture Date & GPS Start Date & GPS End Date & Nb days & Nb summers
& Nb of GPS fixes\tabularnewline
BFUL041 & 20.11.2016 & 23.11.2016 & 16.04.2018 & 509 & 1 &
7,227\tabularnewline
BFUL044 & 30.11.2016 & 02.12.2016 & 09.06.2018 & 554 & 1 &
8,459\tabularnewline
BFUL050 & 25.11.2016 & 27.11.2016 & 19.05.2018 & 538 & 1 &
8,351\tabularnewline
BFUL057 & 30.11.2016 & 02.12.2016 & 17.07.2018 & 592 & 1 &
4,093\tabularnewline
BFUL059 & 30.11.2016 & 02.12.2016 & 29.12.2017 & 392 & 1 &
4,050\tabularnewline
BFUL065 & 05.12.2016 & 07.12.2016 & 05.09.2017 & 272 & 1 &
4,832\tabularnewline
BFUL068 & 15.12.2016 & 16.12.2016 & 28.05.2018 & 528 & 1 &
9,347\tabularnewline
BFUL051 & 25.11.2016 & 28.11.2016 & 25.12.2018 & 757 & 2 &
7,812\tabularnewline
BFUL061 & 30.11.2016 & 02.12.2016 & 12.05.2019 & 891 & 2 &
11,490\tabularnewline
BFUL074 & 15.01.2017 & 19.01.2017 & 14.05.2019 & 845 & 2 &
6,932\tabularnewline
BFUL062 & 08.12.2016 & 11.12.2016 & 27.11.2019 & 1081 & 3 &
17,848\tabularnewline
\bottomrule
\end{longtable}

Birds were fitted with transmitters (Hunan Global Messenger Technology
Company, China) programmed to record GPS position and speed every 1-3
hours depending on the battery condition. Transmitters were solar
powered to enable the global system for mobile communication (GSM) to
transmit data \emph{via} the short message service (SMS). These
back-pack design transmitters were 55x36x26 mm in size and weighed 22g
(appr. 1.6\% of the bird's body mass; Lei \emph{et al} . 2019a). As
Mobile network coverage is sparse or non-existent in summering sites of
North-East Russia, the stored data obtained from that area were
downloaded when birds returned to China.

GPS records of locations (accuracy of \textless{}1000 m) were used in
the analysis of \emph{A. erythropus} journeys to Russia. For
non-breeding \emph{A. erythropus} (the longest one-way migration
recorded was 16,172 km in 60 days, Lei \emph{et al} . 2019b), it was
assumed the spring migration turned to summering activities when the
trans-latitudinal movement became mostly trans-longitudinal. Like spring
migration, we assumed summering was terminated when a pronounced
southbound movement was detected. For breeding birds, the date of
arrival at a breeding site was used to indicate the start of summering.
The site was classified as staging if the bird stayed at a location for
more than four days.

\subsection*{Environmental predictors}

{\label{environmental-predictors}}

To model the potential summering habitat, a range of environmental
variables were used including bioclimatic, geomorphological, land
production and human disturbance.

Bioclimatic Bioclimatic variables were taken from the 30 second
WorldClim (v2.1) climate data, downloaded from http://www.worldclim.org,
which were generated through interpolation of monthly mean temperature
and rainfall data from weather stations for the period of 1970-2000
(Hijmans \emph{et al} ., 2005). We selected five variables that are
relevant to geese summering including Max Temperature of Warmest Month
(Bio5), Mean Temperature of Wettest Quarter (Bio8), Mean Temperature of
Warmest Quarter (Bio10), Precipitation of Wettest Month (Bio13) and
Precipitation of Warmest Quarter (Bio18).

\emph{Geomorphological}Topographic heterogeneity is important for
species distribution (Austin and Van Niel 2011). Three topographic
variables were included in the modelling, namely elevation, LDFG (Local
Deviation from Global Mean) and TRI (terrain ruggedness index). The
global 1 km resolution digital elevation model (DEM) for the study area
was downloaded from (http://srtm.csi.cgiar.org/) and cropped with the
study. Based on the DEM, LDFG and TRI were calculated as:

\(LDFG=y_{i}-\overset{\overline{}}{y}\) (1)

where \(\overset{\overline{}}{y}\) is mean evaluation of the 3 by 3 window, and
\(y_{i}\) is the elevation of the focus grid. Positive LDFG
values represent locations that are higher than the average of their
surroundings, as defined by the neighborhood (ridges). Negative LDFG
values represent locations that are lower than their surroundings
(valleys). LDFG values near zero are either flat areas (where the slope
is near zero) or areas of constant slope (where the slope of the point
is significantly greater than zero).

\(TRI={(\sum{(Z_{c}-Z_{i})}^{2})}^{1/2}\ \) (2)

where \emph{Z\textsubscript{c}} is the elevation of the central grid
and\emph{Z\textsubscript{i}} is the elevation of one of the eight
neighboring grids. The terrain ruggedness index (TRI) is a topographic
measurement developed by Riley, \emph{et al} . (1999) to quantify
topographic irregularities in a region.

As \emph{A. erythropus} is ecologically dependent on wetlands, and often
observed breeding along river valleys (Solovieva \emph{et al} . 2011),
we included a layer of distance to steams in the modelling. We generated
the raster using polylines in the Global River Widths from Landsat
(GRWL) dataset (George, 2018) as the central lines. The polylines were
checked to be a good represent of the rivers in the study area.

Land production To characterize land production, we calculated three
variables (EVI\textsubscript{max}, EVI\textsubscript{hom} and
EVI\textsubscript{range}) using EVI (Enhanced Vegetation Index) time
series (2000-2009). The 10-day global EVI images with 333 \selectlanguage{ngerman}× 333 m
resolution were downloaded from Copernicus Global Land Service
(https://land.copernicus.eu/global/products/ndvi, data downloaded on 28
August 2019). EVI\textsubscript{max} is an indicator of peak land
productivity and was calculated as the 10-year mean of annual max EVI.
EVI\textsubscript{range} is the range of land productivity (i.e.
EVI\textsubscript{max} -EVI\textsubscript{min}). EVI\textsubscript{hom}
is the similarity of EVI between adjacent eight pixels, and was computed
as (Tuanmu and Jetz 2015):

\(\text{EVI}_{\hom}=\sum_{i,j=1}^{m}\frac{P_{i,\ j}}{1+{(i-j)}^{2}}\)(3)

where m is the number of all possible scaled EVI values (i.e. 100)
and\(P_{i,\ j}\) is the probability that two adjacent pixels have
scaled EVI values of \emph{i} and \emph{j} , respectively. Both
EVI\textsubscript{hom} and EVI\textsubscript{range} can be indicator of
habitat diversity.

Human Disturbance Human disturbance can lead to declines and local
extinctions of avian species as well as habitat loss (Vollstädt \emph{et
al} . 2017). The inclusion of human disturbance data can increase the
performance and accuracy of SDM (species distribution model - Stevens
and Conway, 2020). We compiled a database of all human settlements
including villages and towns in the study area (i.e. Republic of Sakha,
Magadanskaya Oblast and Chukotskiy Autonomous Okrug) and generated a
layer of distance to settlements as a proxy of human disturbance.
Settlements with zero registered inhabitants (abandoned and closed
before 2011) were excluded.

Land Cover Forcey \emph{et al} . (2011) found that land use has strong
effects on waterbird distribution, and the percentage of waterbird
abundance is positively related to the area of wetland. In this study,
we used the 2015 global land cover map derived from satellite
observations by Land Cover Climate Change Initiative (CCI) and available
from\emph{https://maps.elie.ucl.ac.be/CCI/viewer/download.php} . The map
classifies the global terrestrial system into 28 major classes using
United Nations Food and Agriculture Organization's land cover
classification system (Di Gregorio 2005).

R (R Core Team, 2019) packages ``raster'' (Hijmans \emph{et al} . 2015)
and spatialEco (Evans and Ram, 2018) were used for raster manipulation
and calculation.

\subsection*{Modeling}

{\label{modeling}}

A total of 96 geo-referenced records were compiled by combining the
tracking data and historical surveys (post 1999) (Table S2 in
Supplementary). To analyze the potential breeding range, maximum entropy
implemented in the Maxent package (version 3.4.1) was used. Maxent is
among the most robust and accurate SDM techniques (Elith et al., 2006).
In the past two decades, it has gained popularity in conservation
studies, partly because the technique is less sensitive to the number of
recorded sites and uses presence-only data (Elith et al., 2011). In
developing the SDM, the program was set to take 75\% of the occurrence
records randomly for model training and the remaining 25\% for model
testing. The mean area under the receiver operating characteristic curve
(AUC) was used to evaluate model performance, and AUC values
\textgreater{} 0.75 are considered as suitable for conservation planning
(Lobo et al., 2008). The modelling process was replicated 100 times and
we reported the mean as summering ranges to reduce the sampling bias
(Merow \emph{et al} ., 2013).

Although collinearity is less of a problem for machine learning methods
in comparison with statistical methods (Elith \emph{et al} ., 2011),
minimizing correlation among predictors prior to model building is
recommended (Merow \emph{et al} ., 2013). We used VIF (Variance
inflation factor) to select predictors (Dupuis and Victoria-Feser,
2013). Nine variables with VIF less than 10, including two bioclimatic
variables (Bio10 and Bio18), two topographic variables (DEM and LDFG),
two productivity variables (EVI\textsubscript{hom} and
EVI\textsubscript{range}), land cover, Distance to stream, and Distance
to settlement, were included in model building.

Using the logistic outputs of MaxEnt, we applied the minimum training
presence threshold (MTP) to produce binary habitat map. MTP threshold
finds the lowest predicted suitability value for an occurrence point and
ensures that all occurrence points fall within the area of the resulting
binary model (Elith \emph{et al} ., 2011).

\section*{Results}

{\label{results}}

\subsection*{\texorpdfstring{Potential summering range of the East Asian
sub-population of \emph{A.
erythropus}}{Potential summering range of the East Asian sub-population of A. erythropus}}

{\label{potential-summering-range-of-the-east-asian-sub-population-of-a.-erythropus}}

The mean training AUC of the 100 models was 0.9510 suggested these
models are very useful (Swets 1988) for predicting the summering range
of \emph{A. erythropus} . The standard deviation of AUC was very small
(0.0007) indicating the models were stable. Moreover, the mean testing
AUC was 0.9356 (SD = 0.0739), which was comparable to the training AUC,
suggesting excellent predictive power of the fitted model (Lobo et al.,
2008).

The average of summering distribution prediction of the 100 models was
presented in Figure 3. The most suitable habitats are located along the
coasts of the Laptev Sea, primarily the Lena-Delta, in the Yana-Kolyma
Lowland, and smaller lowlands of Chukotka with narrow strips extended
upstream to catchments of major rivers such as the Lena, Indigirka, and
Kolyma (Fig. 3). The binary map (Fig. 4) produced using the criteria of
minimum training presence threshold indicated that 36.44\% of the study
area was suitable summering habitats.

Lowland wetlands including large deltas, estuaries, tundra, and swampy
floodplains (i.e. floodplain contains numerous ponds and shallow lakes),
which extend from the Lena Delta at the west to the Kolyma River at the
east, provide the most extensive and continuous breeding ground
for\emph{A. erythropus} in our study area (Figs. 3 and 4). This is
particularly the case for the very large Lena Delta
(\textasciitilde{}29,000 km\textsuperscript{2}, Schneider \emph{et al} .
2009), where the predicted summering habitats include tundra together
with numerous interlaced channels and lakes (Dutta \emph{et al} . 2006).

Most of predicted breeding habitats are covered by a range of plant
types including grasses, sedges, herbs, as well as abundant mosses and
lichens. This tundra vegetation is also characterized by widely spaced
shrubs (e.g. \emph{Betula nana (s.l.), Dushecia} \emph{fruticosa} and
several species of \emph{Salix} ) (Yurkovskaya 2011). Such tundra
vegetation along major rivers within the taiga biome also have potential
to be suitable habitat (Fig. 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3 Fitted Maxent model showing the probability of summering
habitats of the Eastern population of the Lesser White-fronted Goose.
Red color indicates the strongest probability, with orange and yellow
less so. Background: Aerial Imagery from ESRI
(\emph{http://services.arcgisonline.com/arcgis/rest/services} ).
Projection: Asia North Albers Equal Area Conic.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4 Breeding habitats of the Eastern population of the Lesser
White-fronted Goose based on the minimum training presence threshold.
Projection: Asia North Albers Equal Area Conic. Background: World
Imagery from ESRI
(http://services.arcgisonline.com/arcgis/rest/services).

\subsection*{\texorpdfstring{Effects of environmental factor on the
summering range of \emph{A.
erythropus}}{Effects of environmental factor on the summering range of A. erythropus}}

{\label{effects-of-environmental-factor-on-the-summering-range-of-a.-erythropus}}

Of the nine environmental variables included in model building,
elevation was the most important, strongly contributing to the scaling
of the Maxent model (59.7\% based on the model gain and 50.1\% based on
re-evaluation of the random permutation of training presence and
background data, Table 2). Other highly influential variables (with more
than 5\% permutation contribution) include precipitation of the warmest
quarter, distance to streams, and mean temperature of the warmest
quarter (Table 2).

Although highly correlated environmental predictors were excluded from
model fitting, there are still collinearities in the remaining
variables. For example, the Pearson r between Bio10 (precipitation of
the warmest quarter) and Bio18 (mean temperature of the warmest quarter)
is relatively high (-0.82) in the study area. Thus, the variable
contributions in Table 2 should be interpreted with caution (Phillips
2005).

Table 2 Relative contributions of the environmental variables to the
breeding habitat distribution of \emph{A. erythropus} ranked by
permutation importance.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Predictor} & \textbf{Percent contribution} & \textbf{Permutation
importance}\tabularnewline
Elevation & 59.4 & 54.3\tabularnewline
Precipitation of warmest quarter & 5.0 & 25.2\tabularnewline
Distance to streams & 20.3 & 6.5\tabularnewline
Mean temperature of warmest quarter & 5.2 & 5.8\tabularnewline
Range\_EVI & 0.9 & 2.6\tabularnewline
Distance to settlement & 2.4 & 2.2\tabularnewline
Land cover & 5.5 & 2.0\tabularnewline
Homogeneity\_EVI & 1.0 & 0.8\tabularnewline
Local deviation from global & 0.2 & 0.6\tabularnewline
\bottomrule
\end{longtable}

The marginal effects of the predictors on habitat suitability of\emph{A.
erythropus} (i.e. occurrence probability responds to changes in a
specific explanatory variable while other covariates are assumed to be
held constant as mean) were presented in Figure 5. The response curves
showed that the effects of environmental factors on the occurrence
of\emph{A. erythropus} were strongly nonlinear.

For topographic variables, the probability of \emph{A.
erythropus}presence declines with increasing elevation up to 500 m, with
locations higher than 500 m elevation were virtually devoid of \emph{A.
erythropus} (Fig 5A). Also, the response curve of LDFG indicated that
the geese prefer relatively flat sites (Fig 5I). In terms of bioclimatic
variables, the probability of \emph{A. erythropus} presence increases
with precipitation of the warmest quarter to around 55 mm and mean
temperature of the warmest quarter to around 14\textsuperscript{o}C,
after which there is a sharp decrease (Fig. 5B, Fig. 5D). Human
disturbance also influences summering habitat, with suitability
increasing the further the site is from human settlement (Fig. 5F). The
response curve of habitat occurrence probability to distance from rivers
(Fig. 5C) suggests that the geese were highly dependent on wetlands and
riparian areas (Fig. 5C). Within the riparian zone, the summering
habitat suitability decreases sharply with increasing distance from
water courses, and after about 4.5 km virtually no birds are
found.\emph{A. erythropus} generally prefers land cover types waters
(code 210) and shrubland (120; Fig. 5G). The modeling results suggest
that the probability of occurrence increases with land productivity
range (Fig. 5E) and homogeneity (Fig. 5H).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5 The relationships between the probability of \emph{A.
erythropus} occurrence and the top ten environmental variables based on
permutation. Blue lines are mean response curves, and grey shades are 1
standard deviation.

\section*{Discussion and Conservation
Implications}

{\label{discussion-and-conservation-implications}}

Due to the remoteness and restricted accessibility, there are few
historical observations of the summering ground of this population
(Ruokonen \emph{et al} . 2004), and our current knowledge on the
breeding distribution and habitat preference is limited (Fig. 1 and see
Artiukhov and Syroechkovski-Jr. 1999; Egorov and Okhlopkov 2007;
Solovieva and Vartanyan, 2011). In this context, rapid development of
animal tracking technologies offers new insights to determine
distribution range and habitat preferences (Kays \emph{et al} . 2015).
In this study, we combined historical records with recent tracking data
to model potentially suitable areas of the east sub-population of
\emph{A. erythropus} across the more than 7,400,000
km\textsuperscript{2} of arctic and subarctic of north-eastern Russia.
Our findings assist conservation of this threatened species by
identifying the most suitable breeding grounds and assessing existing
and future threats. As \emph{A. erythropus} often co-occurs with other
geese (e.g. Greater White-fronted goose (\emph{A. albifrons} ), Bean
Goose (\emph{A. fabalis} ) and Brent Goose~(\emph{Branta bernicla} ) and
other waterfowl including ducks and tundra swan (Hodges and Eldridge,
2001; Pozdnyakov, 2002; Krechmar and Kondratiev 2006), the breeding
habitat map could also be used for prioritizing waterbird conservation
including through identification of high-priority conservation areas.

\subsection*{Model accuracy and breeding
range}

{\label{model-accuracy-and-breeding-range}}

In recent years, animal tracking point data have been used in SDM
construction either through direct use for model fitting
(Williams\emph{et al} . 2017) or for validating the output of the model
(Pinto\emph{et al} . 2016). By combining three-year tracking data and
historical surveys, our dataset represents the most comprehensive
presence record and offers a solid basis to delineate the breeding range
of the poorly-known eastern sub-population of \emph{A. erythropus} . The
cross-validation results showed that the training and testing AUC are
both high (i.e. greater than 0.92) and comparable, suggesting that the
output is highly reliable (Phillips and Dud\selectlanguage{ngerman}ík 2008).

The Maxent output suggested a continuous rather than patchy breeding
range of the \emph{A. erythropus} on the plains adjusted to the Laptev,
East-Siberian and Chukchi Seas and in the Anadyr Lowland. Within this
over 4,000 km area of coastal plains, the Lena Delta, the wide
Yana-Kolyma Lowland and smaller lowlands of Chukotka represent the most
extensive breeding area with the highest probability of occurrence
(Figures 3 and 4). While there are suggestions that breeding ranges of
West and East Asian sub-populations overlap between 103 and 118 E, our
work did not confirm this. The flat and rolling subarctic tundra is
among the most productive wetland system in north-eastern Russia
(Gilg\emph{et al} . 2000). Vegetation characteristic in this area is
typical tundra, southern tundra with shrubs and forest-tundra with
sparse patches of larch (\emph{Larix spp.)} Yurkovskaya (2011). A
current IBA (Important Bird Area), including the four main deltas (i.e.
the Kolyma, Indigirka, Yana and Lena), covers about 34\% of the modeled
breeding range (BirdLife International, 2017). However, the majority of
the coastal plains, extending up to 450 km inland (Figures 3 and 4), and
valleys of large rivers are not included in this IBA. Although there are
several Wetlands of International Importance under the Ramsar Convention
on the Kamchatka Peninsula, the closest to the study area (Parapolsky
Dol) does not contain habitat the modelling suggests as suitable. Highly
suitable habitats in the study areas have legal protection through
declaration as Federal (State) Nature Reserves: Ust-Lenskiy, Olekminskiy
and Magadanskiy, and also by Kytalyyk and Beringia National Parks.

\subsection*{Environmental characteristics of breeding
habitat}

{\label{environmental-characteristics-of-breeding-habitat}}

The selection of environmental variables is a critical step in SDM
(Araujo and Guisan 2006; Fourcade \emph{et al} . 2018), and hundreds of
environmental factors have been utilized in Maxent (Bradie and Leung
2017). These predictor variables can be loosely grouped into four main
groups: limiting factors that control the ecophysiology of the species
concerned (e.g. temperature, precipitation, pH); resource factors (e.g.
vegetation, water areas), which are supplies needed by the organisms to
survive; disturbance factors including anthropogenic and natural
perturbations in the environment; and landscape factors, which can be
related to the species dispersal limitations (Guisan and Thuiller 2005;
Vuilleumier and Metzger 2006).

The geomorphological predictors (i.e. elevation, distance to streams and
local deviation from global) collectively contributed to 61.4\% of the
model gain based on permutation test. This level of relative importance
was considered very high for Maxent modeling (Bradie and Leung 2017).
The decisive role of topography in controlling the distribution of
summering grounds might be attributed to strong preference of river
valleys and lowlands, especially considering reduced mobility of geese
during breeding and molting periods (Akesson and Raveling 1982). Kosicki
(2017) demonstrated the importance of topography for modeling the
distribution of both lowland and upland bird species, and omitting
topographic variables could lead to substantial overestimation of
distribution range, especially for rare species. The response curves
show that \emph{A. erythropus} selects lowlands with a concave shape as
preferred habitat, which is consistent with field observations (e.g.
Artiukhov and Syroechkovski-Jr. 1999; Egorov and Okhlopkov 2007;
Solovieva and Vartanyan 2011), which reported the bird bred and molt in
river valleys.

The majority of Maxent models include climate variables as limiting
factors, and most studies found temperature and precipitation were very
important variables (Bradie and Leung 2017) as climate is believed to be
the most important factor for species distributions (Gaston, 2003). It
is therefore not surprising that climate variables including
precipitation and temperature were also important for \emph{A.
erythropus} . A significant finding of the study is that there was an
optimal window of mean summer temperature in 9-14\textsuperscript{o}C
(Fig. 5D) and dry continental or high Arctic precipitation of the
wettest quarter in 55 mm (Fig. 5B), within which the habitat suitability
is maximized.

Land cover is also important and contributes strongly to model
performance (Table 2). The response curve indicates that two land cover
types are favored by \emph{A. erythropus} including shrubland and
open-water areas. The land-cover preference can be linked to the
requirement of nest shelters during breeding season (Hilton \emph{et al}
. 2004) and food resources. In terms of food resources, the \emph{A.
erythropus} is an herbivorous browser, i.e. it tends to increase the
portion of the selective resources in their feeding range
(Markkola\emph{et al} . 2003). The wet sedge meadows on the alluvial
floodplains that are preferred by herbivorous geese (Sedinger and
Raveling 1984), and are critical for brood rearing (Markkola \emph{et
al} . 2003) offer a range of highly nutritious species with an adequate
protein--water ratio and low portions of cellulose and lignin, (e.g.
grasses\emph{Puccinellia phryganodes} , \emph{Phragmites australis} and
sedges

\emph{Carex spp).}

Finally, the most suitable habitats had higher land productivity
heterogeneity (Fig. 5E and 5H) which was expected as species richness
and abundance often increases with habitat diversity (Chasko and Gates
1982; Wen et al. 2015). Although human disturbance can sometimes
increase diversity in such wetland systems, here the habitat suitability
decreases with human disturbance (Fig. 5F), reflecting the negative
impacts of human presence (Lei et al. 2019b).

\subsection*{Conservation challenges}

{\label{conservation-challenges}}

The results of this study highlight a major challenge from future
climate change on the \emph{A. erythropus} . First, many climate change
models predict increasing spring temperatures and earlier snow melting
(IPCC 2014), which will lead to flooding, submergence, permafrost
erosion and loss and change in low-lying coastal wetlands
(Prowse\emph{et al} . 2006). As the predicted summering habitats were
concentrated in the lowland coastal zone of the Laptev and East Siberian
Seas, the projected sea level rise (IPCC 2014; Wrona \emph{et al} .
2016) and increasing river flows (Karlsson \emph{et al} . 2012; Wrona
\emph{et al} . 2016) could cause extensive habitat loss. The response
curves of habitat suitability to topographic variables suggest that the
relatively hilly and rugged landscape would restrict extension of
suitable habitat landward and such ``habitat squeeze'' (Leo \emph{et al}
. 2019) would be highly detrimental to \emph{A. erythropus} . Second,
the models suggested that there was an ``optimal window'' in terms of
mean summer temperature and precipitation, which could be interpreted as
the realized climatic niche of \emph{A. erythropus} (Merow \emph{et al}
. 2016)\emph{.} Rising temperatures under future climate change
scenarios means that the temperature niche could shift northerly, which
is sea. Third, studies have shown that encroachment of shrubs following
projected climate change (e.g. \emph{Salix ovalifolia} and
\emph{Dushecia fruticosa} ) into the wet meadows (Carlson \emph{et al} .
2018), would likely decrease quantity and quality of available food
resources.

Finally, there is the threat from increasing anthropogenic
disturbance;\emph{A. erythropus} avoids locations near active mines
(although can colonize such areas after mining is finished) (Egorov and
Okhlopkov 2007; Solovieva and Vartanyan 2011). Currently, human
population levels in the predicted summering range is among the lowest
in the world, and the coastal areas of this region are some of the least
explored. However, the coast of the Russian Arctic is likely to undergo
rapid development as there are reserves of oil, gas, metals and other
natural resources which could be exported, with additional
infrastructure, through the North-East Passage to European and Asian
ports (Martini\emph{et al} . 2019), more information on these potential
developments can be found
at\emph{http://ecoline-eac.com/proekty/peschanka/deposit.html} ), and
these developments present perhaps the most difficult challenges to the
future of eastern sub-population of \emph{A. erythropus} .

\section*{ACKNOWLEDGMENTS}

{\label{acknowledgments}}

We greatly appreciate all the individuals involved in the capture and
banding of \emph{A. erythropus} that generated the data for this study.
We recognize the contributions of staffs from East Dongting National
Nature Reserve of China.

\textbf{Funding statement} : This study is supported by National Key
Research and Development Program of China (2017YFC0405300). Logistic and
transportation support to surveys in Chukotka was provided by Chukotka
Gold Mining Co, a subsidiary of Kinross Gold.

\textbf{Ethics statement} : All \selectlanguage{english}field methods used in this study were
approved by the Forestry Department of Hunan Province of China under
scienti\selectlanguage{english}fic research license (No.11 Xiang Forest Protection (2014)). Field
research was conducted with permission from the Bureau of East Dongting
National Nature Reserve.

\textbf{Author contributions} : L.G., D.S. and Z.Q. developed the study
design, D.S., G.D., S.V. participated the breeding surveys, and T.H. and
L.J. participated in the tracking at wintering sites, all authors were
involved in analyses and producing figures, D.S., Z.Q. L.W. and T.H.
drafted the manuscript, P.B and L. W. edited the manuscript, P.B. L.W.
and Z.Q. organized submission materials and revised the manuscript.

\textbf{Data deposits} : All data for analysis is available in Dryad.

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