Discussion and Conservation
Implications
Due to the remoteness and restricted accessibility, there are few
historical observations of the summering ground of this population
(Ruokonen 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 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 A.
erythropus across the more than 7,400,000 km2 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 A.
erythropus often co-occurs with other geese (e.g. Greater White-fronted
goose (A. albifrons ), Bean Goose (A. fabalis ) and Brent
Goose (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.
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 (Williamset al . 2017) or for validating the output of the model (Pintoet 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 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 Dudík 2008).
The Maxent output suggested a continuous rather than patchy breeding
range of the 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 (Gilget al . 2000). Vegetation characteristic in this area is typical
tundra, southern tundra with shrubs and forest-tundra with sparse
patches of larch (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.
Environmental characteristics of breeding
habitat
The selection of environmental variables is a critical step in SDM
(Araujo and Guisan 2006; Fourcade 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 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 A.
erythropus . A significant finding of the study is that there was an
optimal window of mean summer temperature in 9-14oC
(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 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 et
al . 2004) and food resources. In terms of food resources, the A.
erythropus is an herbivorous browser, i.e. it tends to increase the
portion of the selective resources in their feeding range (Markkolaet 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 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. grassesPuccinellia phryganodes , Phragmites australis and sedges