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Effects of interannual variations in environmental conditions on seasonal range selection by Mongolian gazelles

T.Y. Ito1, M. Tsuge1, B. Lhagvasuren2, B. Buuveibaatar3, B. Chimeddorj2, S. Takatsuki4, A. Tsunekawa1, M. Shinoda1
1 Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan
2WWF Mongolia Programme Office, Ulaanbaatar, Mongolia
3Institute of Biology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia
4School of Veterinary Medicine, Azabu University, Sagamihara, Japan

Abstract

To examine the effects of interannual variations in environmental conditions on the seasonal range selection and movement pattern of Mongolian gazelles Procapra gutturosa, we compared the summer and winter ranges of satellite tracked gazelles among 3 years, and we analyzed the environmental conditions in the gazelles’ seasonal ranges by using a satellite-derived normalized difference vegetation index (NDVI). The movement pattern of the tracked Mongolian gazelles was not regular migration between specific seasonal ranges. The locations of the summer ranges in 2003 and 2004 were similar for all gazelles, but in 2005, when the NDVI values were lower, the animals ranged about 30e70 km farther north, suggesting that the gazelles used areas of similar vegetation availability with former 2 years. The winter ranges were widely separated among years; the longest distance between winter ranges of the same individual in different years was about 340 km. During winter, the NDVI values of the winter ranges of tracked gazelles were almost always higher than or not significantly different from the mean of the 3-year range. Conservation strategies to allow access to wide ranges containing suitable areas in each year for gazelles, which location is changing interannually, are important.

Keyword: Drylands,Long-distance movement,Migration,MODIS,NDVI Satellite tracking

Introduction

Ungulate migration is one of the most spectacular of natural phenomena, but it has been severely disrupted by human activities over the last 2 centuries and our understanding of ungulate migration is fragmentary (Berger, 2004; Bolger et al., 2008; Harris et al., 2009; Mallon and Jiang, 2009). Therefore, integrative approaches to understanding and conserving migratory ungulates are needed (Bolger et al., 2008). Animal movement patterns are determined along gradients of spatial heterogeneity and temporal predictability of resources, and animals will travel longer distances between resource patches in landscapes of coarser spatial heterogeneity and higher temporal predictability (Fryxell et al., 2005; Mueller and Fagan, 2008). Longdistance movement patterns vary widely, from regular migration between specific seasonal ranges along specific migration routes to nomadism without specific seasonal ranges or routes. If temporal predictability of resources is low, migration between specific seasonal ranges can switch to nomadism without specific seasonal ranges or migration routes, as individuals seek resources that are not dependably available (Fryxell et al., 2005; Mueller and Fagan, 2008). To examine such relationships between animal movement patterns and environmental conditions, long-term observation of movements of the same individuals is necessary. The normalized difference vegetation index (NDVI) is an index of plant biomass or productivity (Huete et al., 2002) that is used for habitat evaluations of many animal species (Pettorelli et al., 2005, 2011). For example, the round-trip migrations of wildebeest in the Serengeti in East Africa have been predicted well by using the NDVI and precipitation (Boone et al., 2006). In central Asia, in the case of the saiga antelope, the area of higher NDVI value changes seasonally between the antelope’s northern summer ranges and southern winter ranges (Singh et al., 2010b), and the NDVI values in its calving grounds peak in the calving season (Singh et al., 2010a). The Mongolian gazelle Procapra gutturosa inhabits the steppes and semi-deserts of Mongolia, northern China and southern Russia, and moves long distances (Jiang et al., 1998; Lhagvasuren and Milner-Gulland, 1997). The area is located in mid-latitude drylands characterized by short summers, long and severe winters, and large interannual variations in climatic and vegetation conditions (Han et al., 2010; Yu et al., 2004, 2003). Droughts in summer and cold conditions and large snowpacks in winter cause mass mortalities of livestock (Begzsuren et al., 2004; Tachiiri et al., 2008). Severe conditions also affect habitat selection and mortality in wild animals (Kaczensky et al., 2011a), and the locations of suitable habitats for ungulates are likely to change from year to year (Mueller et al., 2008). To examine effects of interannual change of environmental conditions on animals, continuous tracking of the same individuals over a long period is necessary. However, it was difficult because of battery limitations in transmitters. For this reason, most investigations have been based on animal tracking data from a single year or distribution data without individual identification. NDVI values in the summer and winter ranges of Mongolian gazelles shifted seasonally, suggesting that gazelles used the better sites during each period (Ito et al., 2006; Leimgruber et al., 2001). In these studies, however, gazelles’ seasonal ranges were defined through specialist knowledge (Leimgruber et al., 2001), or from the tracking data only froma single year (Ito et al., 2006). On the eastern steppe ofMongolia,where ismore humid than the area of the present study, the gazelle distribution is concentrated not in the high but in themid-range ofNDVI values, and the areas withNDVI values suitable for gazelles change from year to year (Mueller et al., 2008). During a severe drought in 2007 the availability of suitable habitat patches was highly restricted, and huge aggregations of gazelles were observed in these limited areas (Olson et al., 2009). These studies, however, were not associated with individual movements. The seasonal ranges of satellite-tracked gazelles on the eastern steppe differed from 1 study year to the next, suggesting that the gazelles’ movement pattern was not typical of migration but of nomadism (Olson et al., 2010), although no analysis of the relationship between seasonal ranges and environmental factors was conducted. In the distribution area of the Mongolian gazelle, gradients of precipitation and temperature from north to south are exist, that is, higher precipitation and lower temperature in north (Nandintsetseg and Shinoda, 2011). This gradient may drive movement pattern to typical migration. At the same time, inter annual variations of environmental conditions are large in the area. This unpredictability may drive movement pattern of gazelles to nomadism. If gazelles used the same areas in every summer and winter despite the inter annual variation of environmental conditions, it would reveal the importance of some specific calving grounds and/or winter ranges, suggesting that movement pattern of the gazelles would be typical migration. In this case, protection of the calving ground and/ or winter ranges would be a priority for conservation of gazelles. If gazelles changed locations of summer and winter ranges among years, it suggests nomadic movement pattern of gazelles. Besides, if the seasonal range selection by gazelles corresponds with interannual variation of environmental conditions, conservation strategies considering the inter annual variation would be needed.
The aim of the present study is to examine the effects of inter annual variations in environmental conditions on the seasonal range selection and movement pattern of Mongolian gazelles. We compared the locations of the summer and winter ranges of individual gazelles among 3 years. Two of them were the same individual analyzed the in Ito et al. (2006). We then analyzed the relationship between the interannual differences of the seasonal range locations and the NDVI distribution patterns.

Material and Methods

2.1. Study area
The study area is the steppe and semi-desert area located in central to southeastern Mongolia (Fig.1). The region is characterized by high elevation (about 1000 m above sea level).

The climate is strongly continental and arid and is characterized by cold winters (minimum temperatures below_35 _C), dry and windy springs, and relatively wet and hot summers (maximum temperatures above 40 _C). Annual precipitation increases from about 100 to 300 mm from the southern desert to the northern typical steppe (Nandintsetseg and Shinoda, 2011). Most precipitation occurs during summer (Nandintsetseg and Shinoda, 2011; Fig. 2 and Table 1), and the deepest snow occurs in January (Morinaga et al., 2003). Fine leafed grasses and Allium spp. dominate on the steppe, and semi shrubs, shrubs, and some grasses dominate in the semi-desert regions. Seminomadic pastoralists live throughout the region, at some of the lowest population densities in the country (0.44 people/km2; Milner-Gulland and Lhagvasuren, 1998). An international railroad between China and Russia bisects the habitat of the Mongolian gazelle. The nearly linear railroad runs northwest to southeast through Mongolia (Fig. 1), and barbed-wire fences have been built alongside it to prevent accidents involving livestock (Ito et al., 2005, 2008). All of the tracked gazelles were captured in the western side of the railroad (Fig.1). The study area in the present study was enlarged about twice to north from Ito et al. (2006) because the locations of the gazelles were different among years, and we added another gazelle.
 
 2.2. Seasonal ranges of gazelles
We captured and collared 2 adult female gazelles on 18 October 2002 and 1 adult female on 24 July 2003 in southeastern Gobi, Mongolia (Fig. 1). The location of the site of capture for the 2 gazelles in 2002 was the same, but the capturing trials and movements after the release were different, suggesting that the 2 gazelles belonged different groups. Each gazelle was collared with a satellite transmitter (platform terminal transmitter [PTT]; models ST-18, and ST-20, Telonics Inc., Mesa, AZ, USA; Kaczensky et al., 2010).
The weight of a PTT with collar was 550 g (ca. 2% of an adult gazelle’s body weight). The PTTs were programmed to transmit radio signals for an 8-h period each week, that is, they transmitted location data 1 day a week. The location data were received through computer communications and computer disks sent from the Collecte Localisation Satellites Service in France. We obtained location data for the 3 gazelles from the day of capture to the day on which the transmitter batteries ran out of power. All 3 gazelles continued to move throughout the data acquisition period, indicating that they were alive. We analyzed data for 3 years after the first capturing, that is, from October 2002 to October 2005. The tracking periods of each gazelle are shown in Table 2.
Location class (LC) is an index of accuracy of location data, and ranged from 0 to 3. Their estimated errors were <250 m for LC 3, 250e500 m for LC 2, 500e1500 m for LC 1, and >1500 m for LC 0 (CLS, 2007). Less accurate data are also provided as LC A and B (CLS, 2007). To plot the gazelles’ migration routes we selected the best data from each day according to the LC scores. When several data with the same best ranking were available from the 1 day, we selected the last location. Of all the best location data selected on each day, 95.7% fell into LC 3, 2,1, or 0 (LC 3, 43.2%; LC 2, 28.4%; LC 1, 19.8%; LC 0, 4.3%); only 3.5% fell into LC A and 0.8% in LC B.We used the LC A and B location data in our analyses, because these locations fell into the gazelle ranges delineated without using data of LC A and B. Gazelle home ranges were calculated by using the kernel method (Worton, 1989) with ArcGIS (Environmental System Research Institute Inc., Redlands, CA, USA) and the animal movement extension (Hooge and Eichenlaub, 2000).We categorized the home range into the annual range, summer range, and winter range. The annual and whole-period ranges of each of the 3 gazelles were defined as the 95% area calculated by using the kernel method on the basis of location data on the each gazelle from the date of capture to the last location data (see Table 2 about tracking periods of each gazelle). The smoothing parameter (h) obtained by the least-square cross validation method was used to estimate home ranges. The 3-year rangewas defined as 95% kernel area used by the all 3 gazelles during the whole tracking period (from 18 October 2002 to 29 Oct 2005). The summer and winter ranges in each year for each gazelle were defined as 50% core areas on the basis of location data from 1 June to 31 August and from 1 December to 28 February, respectively. This was because the 50% core area corresponded best to the gazelles’ seasonal range selection in relation to the NDVI value among the 95%, 80%, 70%, 60%, and 50% kernel areas in their seasonal ranges in a previous study including the same individual gazelles (Ito et al., 2006).
 
2.3. Environmental data in gazelle ranges
Precipitation data for Choir, the city located almost in the center of the tracked gazelles’ 3-year range (Fig. 2), were collected from the Institute of Hydrology and Meteorology, Mongolia (Table 1 and Fig. 1). NDVI values were calculated by using imagery from the moderate-resolution imaging spectrometer (MODIS, Raytheon Co., Waltham, MA, USA; Huete et al., 2002) on the Aqua satellite. NDVI values ranged between _1.0 and þ1.0. Positive values indicated the existence of plants and were positively related to plant biomass, whereas negative values generally represented unvegetated surfaces such as barren land, rock, water, or ice. We downloaded NDVI data (16-day composites at 250-m resolution) for the area of the 3-year range of the tracked gazelles (tile number: h25v04) from USGS Land Processes Distributed Active Archive Center web page (https://lpdaac.usgs.gov, MODIS vegetation indices, MYD13Q1 Product; NDVI, 250 m). We used NDVI imagery from 3 years (16 October 2002 [the first date of the 16-day composite] to 30 September 2005, a total of 69 time series) for analysis. The mean NDVI of each range was calculated by using Erdas Imagine (Leica Geosystems GIS & Mapping, LLC, Heerbrugg, Switzerland) for every time interval. We used Friedman tests to examine differences in NDVI values in a range between years and between ranges, and we used Wilcoxon signed rank tests to examine differences in NDVI values between the 3-year range and each seasonal range during a period. In the both test, we defined 6 time intervals from 3 December to 18 February and another 6 intervals from 7 June to 29 August as winter and summer in each year, respectively.

Result

Annual precipitation (OctobereSeptember) in Choir, in the center of the 3-year range of the tracked gazelles (Fig. 1), was lower than the 30-year average in every analyzed year (Table 1 and Fig. 2). Most precipitation was observed from April to September, but the seasonal patterns and peak months differed among years (Fig. 2). In April and May there was less precipitation in 2005 than in the other 2 years, making the total precipitation from spring to early summer (April to June) lower in 2005 than in the previous 2 years and the 30-year average (Table 1, Fig. 2). In contrast, precipitation from spring to early summer in 2003 and 2004 was higher than the 30-year average (Table 1). NDVI values in the study area during summer were generally higher in the north and lower in the south (Fig. 4). NDVI values in summer in the 3-year range were highest in 2003 and lowest in 2005 (Figs. 3 and 4). The NDVI value was greater than 0.1 in most areas of the 3-year range in summer 2003, but itwas less than 0.1 in a substantial part of the southern part of the 3-year range in summer 2005 (Fig. 4).


During winter, the spatial distribution patterns of NDVI values differed among years (Fig. 4). Most of the 3-year range had NDVI values of less than 0.1, and parts of this range had values below 0. Areas with NDVI values below 0 were widely distributed in the northern part of the 3-year range in 2002e03 and 2003e04, but they were limited to small parts of the range in 2004e05. The tracking periods of the 3 gazelles and the distances moved are shown in Table 2. All of the tracked gazelles had used only the western side for the whole tracking periods (Fig. 1). The highest overlap of annual ranges (95% kernel area) of a gazelle in 2 or more years to the area where the gazelles used for the 3 years was 51% (gazelle A, Table 3). The percentage overlaps of the winter and the summer ranges among years were smaller than those of the annual ranges, except in the case of the summer range of gazelle C; there was no winter range overlap among years in the case of gazelles B and C (Table 3).

The summer ranges of the tracked gazelles were located in similar areas in 2003 and 2004 (Fig. 4). Summer ranges in 2005 were located about 30e70 km north of those in the previous 2 years. The locations of the winter ranges of every tracked gazelle differed among years (Fig. 4). Gazelle A used the southern part of the 3-year range during winter 2002e03 and 2003e04, but it used the northern part in 2004e05. The minimum linear distance between the edges of the 50% core areas of the winter ranges in 2002-03 and 2004-05 was about 340 km. In winter, gazelle B used the central part of the 3-year range in 2002e03, and it used the northern part in 2003-04 and 2004-05. Gazelle C used the southern part in winter 2003-04 and the northern part in winter 2004-05. NDVI values in the summer ranges were lower than in the mean
3-year range during summer for all gazelles in every summer (P ¼ 0.028, Fig. 5). In the areas where the summer ranges were located, the NDVI values showed similar trends of decrease during summer from 2003 to 2005 (P < 0.05 except the 2003 summer range used by gazelle B, P ¼ 0.070; and the 2005 summer range used by gazelle C, P ¼ 0.070) and in the 3-year range (P ¼ 0.006, Fig. 5). However, the NDVI values of the summer ranges actually used in each year did not differ significantly among years for the gazelles tracked until summer 2005, because in 2005 the NDVI values of the summer ranges used in the year were higher than those of the summer ranges that had been used in 2003 and 2004 (P < 0.05, Fig. 5).
NDVI values in the winter ranges actually used each year were higher than or not significantly different from the values in the mean 3 year-range during winter, except in the case of gazelle B during winter 2003e04 (P ¼ 0.028, Fig. 6). The mean NDVI values in the 3-year range during winter were lowest in 2002e03 and highest in 2004e05 (P ¼ 0.030, Fig. 6), although there were no significant differences between 2003e04 and other 2 winters in post hoc test. For all gazelles, NDVI values in the winter ranges used in 2004e05, all of which were located in the northern part of the 3- year range (Fig. 4), were significantly higher than the values in the same areas in 2002e03 and 2003e04, when these areas were not used (P < 0.05); for all gazelles they were close to or below 0 in all years in which these ranges were unused. However, NDVI values in the winter ranges actually used in each year did not differ significantly among years in the case of gazelles A and C (Fig. 6).

Discussion

Sample size of tracked gazelles is only 3 in the present study. However, Mongolian gazelles are gregarious and often form large groups (Jiang et al., 1998; Lhagvasuren and Milner-Gulland, 1997; Olson et al., 2009).

 
In fact, the tracked gazelles belonged to large herds of hundreds of animals when they were captured. Therefore, it is likely that several hundred gazelles moved together with the tracked gazelles. Tracked period is also short, that is, only for 3 years. It is not enough to draw general conclusions on species responses to interannual changes in productivity. However, the results clearly revealed that the gazelles used different areas among years, and the difference of seasonal range location would be caused by the interannual differences of environmental conditions. Our study is novel because it compares seasonal ranged of long distance movement ungulates and environmental conditions for continuous 3 years, and because it revealed that the gazelles changed seasonal ranges interannually responding to environmental conditions. This supports previous studies suggesting that movement pattern of Mongolian gazelles are nomadic (Mueller et al., 2011; Olson et al., 2010), differing from typical round-trip migration represented by caribous in Alaska (Mueller et al., 2011) and wildebeest in Serengeti, East Africa (Boone et al., 2006), where temporal predictability of resources is high. Every tracked gazelle used the southern or central part of the 3 year-range in some winters but the northern part in other years, and the longest distance between winter ranges of the same individual in different years was about 340 km. Summer ranges were also located about 30e70 km apart between years. These results showed that the gazelles’ movement pattern was not one of regular migration between a specific summer range and a winter range. However, the locations of the summer ranges were similar in 2003 and 2004 for all gazelles, and those of the winter ranges were also similar in 2 of the 3 winters in the case of those gazelles that were tracked for a full 3 years. The movement patterns of the gazelles, therefore, can be classified into an intermediate type between regular migration and typical nomadism. The smaller differences in location of the summer ranges than the winter ranges among years may reflect the importance of calving ground selection (e.g., Leimgruber et al., 2001). Even if calving ground was important, however, the gazelles did not go back to the same ranges. This may mean that the availability of areas required to fulfill the criteria for calving grounds is not so limited within the whole ranges over which gazelles can move.
In the study area, NDVI distribution patterns during summer showed a similar trend in every summer, that is, high in the north and low in the south. However, NDVI values during summer were different interannually, and highest in 2003 and lowest in 2005. In summer 2005, areas with NDVI values lower than 0.1 were widely distributed in the southern part of the 3-year range. This seems to correspond with that precipitation during spring to early summer (fromApril to June)was lowest in 2005. NDVI values of less than 0.1 indicate that there are little vegetation available to gazelles; this poor vegetation condition would have been the main reason why the gazelles used areas farther north during summer 2005 than in the previous 2 summers. During summer 2005, the gazelles used the areas with similar plant availability with previous 2 summers by using the farther north areas. Although gazelles are likely to avoid areas of low NDVI values, the tracked gazelles did not use areas with highest NDVI values located in the northern part of the 3-year range during every summer. Mongolian gazelles on the eastern steppe, which is more humid than our study area, are also distributed in the mid-range of NDVI values within the area (Mueller et al., 2008). Areas with moderate or relatively low NDVI values may actually be better than those with higher NDVI values for Mongolian gazelles. Some herbivores prefer vegetation with moderate or relatively low biomass (Albon and Langvatn, 1992; Festa-bianchet, 1988; Fryxell et al., 2004; Wilmshurst et al., 1999b, 1999a); because dry matter intake rate is positively related to sward biomass, yet energy digestibility declines along the same gradient, daily energy intake rate is highest on swards of intermediate biomass (Fryxell, 1991;
Gross et al., 1993). There are other possibilities as to why the tracked gazelles selected areas with relatively low NDVI values. The group of tracked gazelles might have been distributed according to an ideal free distribution (Fretwell and Lucas, 1970). Large numbers of Mongolian gazelles are distributed over the eastern steppe of Mongolia
(Olson et al., 2005), and the gazelles selected areas with mid-range NDVI values (Mueller et al., 2008). NDVI values in the gazelles’ summer range in the present study were lower than those selected by gazelles on eastern steppe. Therefore, the northern part of the 3- year range may have been suitable for summer ranges, and many other gazelles with ranges different from those in our study may have used these northern areas during summer. Competition with livestock (Campos-Arceiz et al., 2004; Yoshihara et al., 2008), and human activities such as hunting (Reading et al., 1998) and mining with accompanying heavy road traffic would also have affected gazelle habitat selection. The barrier effects of the railroad may also cause gazelles not to use suitable areas (Ito et al., 2005, 2008). The same 2 gazelles tracked for 1 year in Ito et al. (2005) and an additional individual apparently did not cross the railroad during the 3-year period in the present study, although they often used areas close to it. The influence of this barrier on habitat selection of the gazelles seems larger in winter than summer, because difference of seasonal range location was larger in winter than summer, and some winter ranges were adjacent to the railroad. Annual ranges of gazelles might be smaller than those observed, although it is difficult to estimate locations and sizes of annual ranges of the gazelles, if there were no railroad and fence. In contrast to summer, during winter, NDVI values tended to be higher in the south of the study area and lower in the north, and many areas where the NDVI values were less than 0 were observed in 2002e03 and 2003e04 in the north. Because NDVI values on snow are about or less than 0 (Cihlar et al., 1991), our values would reflect the presence of increased snow cover in the northern area, where it is more humid and colder than in the south (Morinaga et al., 2003). However, the NDVI distribution patterns differed markedly among the 3 winters; these differences corresponded to large interannual differences in the snowfall pattern (Morinaga et al., 2003). Despite the wide separation of winter ranges among years, the NDVI values in the most of winter ranges were higher than or not significantly different from the average of the 3-year range. Moreover, the NDVI values in the most of winter ranges located in the northern part of the 3-year range were higher in the winters when these ranges were used (i.e. 2003-04 and 2004-05) than in the winters when they were not used; those in the winters when they were not used were quite low (around or below 0). These results suggest that the gazelles sought out higher NDVI areas, where would be higher plant availability and not have been covered by snow. Comparing summer and winter in the study area, interannual variation of spatial distribution pattern of NDVI values was larger in winter. Comparing northern and southern area in the study area, interannual variation of NDVI values in winter was larger in northern area, despite NDVI values in summer was higher. Such environmental conditions would reflect the gazelles’ movement patterns that were not regular migration between specific seasonal ranges, and the larger interannual differences of winter range locations than those of summer ranges. Severe winter conditions would also have critical impacts on habitat selection and mortality of Mongolian gazelles as well as droughts (Olson et al., 2009), and as well as the case of re-introduced Przewalski’ horses (Kaczensky et al., 2011b) if gazelles’ habitat is fragmented. Conservation strategies to allow access to wide ranges containing suitable areas in each year for gazelles, which location is changing interannually, are important, under the current situation that mining activities with construction of roads and railroads are progressing, which may cause potentially habitat fragmentation.

Conclusion

Acknowledgments
We thank Z. Jiang, D. Enkhbileg, L. Banzgagch, G. Sukhchuluun, D. Enkhbujin, the drivers, and the local people for capturing the gazelles; M. Ueno for her help with the Geographic Information System analysis; and the Institute of Meteorology and Hydrology, Mongolia, for providing the precipitation data. This research was supported by the Japan Ministry of Education, Culture, Sports, Science, and Technology’s Grants-in-Aid for Scientific Research (B) 14405039, (A) 18255002, and (A) 20255001, and by the 21st Century COE (Centers of Excellence) Program, and Global COE program.

Acknowledgement

a-Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan b-WWF Mongolia Programme Office, Ulaanbaatar, Mongolia c-Institute of Biology, Mongolian Academy of Sciences, Ul

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