Nest Habitat Suitability Modeling for Red-Crown Crane (Grus Japonensis) Based on Ecological Niche Factor Analysis Zhi-xuan Zhao1,2, Jun Yin2, Zhan-feng Huang2,3, Bai-sha Weng1,2, Biao Zhang2,4, and Deng-hua Yan2 1
Civil Engineering College, Tianjin University. Tianjin 300072, China Water Resources Department China Institute of Water Resources & Hydropower Research (IWHR), Beijing 100044, China 3 Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China 4 School of Urban Construction, Hebei University of Engineering, Handan 056038, Hebei, China
[email protected] 2
Abstract. Zhalong wetland natural reserve is one of the most important breeding areas for Red-crown crane around the world. With the assistance of GIS, the landscape map was generated by utilized two Landsat TM images (2006) that covering the study area. Besides, the elevation data was collected. Then, the nest habitat suitability of this rare and endangered species in this reserve was modeled and evaluated based on ecological niche factor analysis methodology. The results showed that Red-crown cranes prefer to choose the low elevation zones with relatively low slops to build nests. They tend to select areas with South and Southeast aspects as their nest habitat. Reed swamp is the most important habitat for them during the breeding season and it seems that they never build nests on other landscapes in this area. Human disturbances play important roles in affecting the quality of the nest habitats, making them more separated and fragmentized. Because artificial landscapes usually have intensive disturbances, Red-crown crane tend to avoid areas that within or close to artificial landscape. Among the eco-geographical variables that describe the degree of human disturbance, the frequencies of the dry land (Mf=-0.314) is the major factor that treating the nest habitat, while distance to man-made pool and reservoir (Md=-0.107) plays the minimal role, mainly because they provide food sources during the breeding season. The total area of the suitable, less suitable nest habitats are calculated as 357.18km2 and 160.3km2 respectively, with 65.31% and 74.5% of which protected by the core area of the reserve. Suffering from the disturbances from human activities, 52.26% of the core area are not suitable to build nest. Besides, 27.51% (98.27km2) of suitable habitats located in the buffer zone, the other 7.18% (25.64km2) in the experimental area and tourism area, where the protection measures are less powerful, making these habitats more vulnerable by human activities. For all the considerations above, comprehensive measures should be taken to alleviate the negative affects made by human, and the nest habitat quality for this endangered species should be maintained or improved. Keywords: ecological niche factor analysis, Red-crown crane (Grus japonensis), nest habitat modeling, GIS &RS, Zhalong wetland. L. Jiang (Ed.): International Conference on ICCE2011, AISC 111, pp. 555–566. © Springer-Verlag Berlin Heidelberg 2011 springerlink.com
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Introduction
Nest habitat is the most important habitat for birds in their breeding season. The quality of their habitat determines the rate of reproductive success directly. It can also influence the population dynamics and community composition indirectly[1][2]. Red-crown crane (Grus japonensis) has been enrolled into the endangered species red book of IUCN and has been defined as the vulnerable species. It is the first class emphasized protective animal in China[3][4]. Recently, under the dual influence of drying and warming of regional climate and the disturbance of human activity, exacerbation of nest habitat has become one of the major factors that constraint the survival and breeding of Red-crown crane (Grus japonensis). Model method is an important method to study the habitats of wild animals[5][6]. With the development of computer technology, RS, GIS and GPS, many habitat models which connect the distribution data of certain species and circumjacent environmental variables have come into being. Different models have different input format requirement of the data of certain species. Models can be divided into two kinds according to the differences above. The first type need the presence and absence data, such as GLM, GAM, Logistic regression model, classification and regression tree analysis and ANN, and so on[7]. The second type just need presence data, such as Bioclim[8], Domain[9] and ENFA[10], and so on. ENFA can also be called ecological niche factor analysis model[11]. The greatest advantage of this model is it can combine the data of presence point of certain species with the process of evaluation without the data of absence point[12]. Besides, compared with other models, this model can reflect certain species’ actual degree of utilization and its interrelation with the factor of habitat. Therefore, taking Zhalong wetland natural reserve as an example, consulting relative achievement of the study of nest habitat of Red-crown crane (Grus japonensis), this paper uses ENFA model to simulate the suitability of Red-crown crane’s choosing of nest habitat. What’s more, this paper discusses the relationship between Red-crown crane’s utilization of nest habitat and eco-geographic variable, hoping to provide some proof for the planning of Zhalong wetland natural reserve and the protection of Red-crown crane (Grus japonensis).
2 2.1
Study Area and Research Method Study Area
Zhanglong wetland natural reservoir is located in the west of Song-nen plain and the downstream of Wu yuer river and Shuang yang river(46°52′N 47°32′N, 123°47′E 124°37′). The study area is 2114.62114.6km2 and is relatively flat. The average elevation is 147m. The climate of the study area is continental semiarid monsoon climate with multi-year average precipitation of 402.7mm, potential evaporation of 1506.2mm and perennial mean temperature of 3.5℃. The study area is Mongolia vegetation flora meadow grassland zone with major vegetation such as Carpex sp., Cyprus sp., Typha sp., Phragmites australis, Aneurolepidium chinense. The fauna is the Da Hinggan Mountain sub-region, the Changbai Mountain sub-region and Songliao plain sub-region in the ancient north Northeast area and the intermediate zone of the
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eastern prairie sub-region in Mengxin zone[13]. The major type of wetland is reed swamp which covers 80%~90% of the total area. In addition, a small amount of Typha angustifolia, Carex pseudocuraica and meadow grassland also exist in this area[14]. This natural reserve was enrolled into international key wetland list with 35 bird species which are classified as national key protected birds. There are 265 species belonging to 48 families and 16 orders. The population of Red-crown crane (Grus japonensis) in this area composes about 20% of the migration population in the world[15]. 2.2
Study Method
2.2.1 The Theory of ENFA Model ENFA model is established on the basis of ecological niche theory[16] which holds the opinion that ecological niche occupied by a certain species can be described by multi-dimensional space sub-set of EGV that suits its survival and breeding. The modeling method has some similarities with PCA: when calculating, correlation between different EGVs should be defined first. Then using linear combination of these EGVs, uncorrelated indexes are produced. Finally, these indexes can be used to describe differences between ecological niche of a certain species and the environment of the study area.he difference between these indexes and PCA is not only can the majority of the information of original EGV can retained, more direct ecological significance is contained in these indexes[10][11]. There are mainly 3 important indexes in ENFA: Marginality factor(M), Specialisation factor(S) and Tolerance factor(T). Their computational formula are as follows[10~12]:
M=
| mG − m S | 1.96σ G
(1)
σG σS
(2)
1 S
(3)
S=
T=
mG :global mean, σ G :standard deviation of the global distribution, mS :species mean,
σ S :standard deviation of the focal species.
Certain species’ ecological niche requirement for the environment differs from the EGVs of the whole study area. Positive coefficients indicate that the focal species prefers values that are higher than the mean with respect to the study area while negative coefficients indicate preference for higher-than-mean values. S is used to describe the range of certain species’ selection of habitat factors. The reciprocal of S is T whose value range is 0~1. The closer to 0, the narrower the species distribute in the study area. The closer to 1, the more widely the species distribute in the area. The derivation process of each coefficient can be seen in references 10 and 12.
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2.2.2 Resources of Data and Its Servicing The input data of ENFA model contains the coordinate of the Red-crown crane’s nest habitat, natural geographic factors and interference factors of human activities. Natural geographic factors include elevation, slope, aspect and landscape type which affect Red-crown crane’s selection of nest site. Interference factors of human activities include towns, rural residential area , roads, dry farmland and paddy field which exist in and around the natural reserve. As to partial-existing seasonal human disturbance such as tourism, fishing, grazing and the harvesting of reed, because of the scale issue, this paper does not take them into consideration. The 63 nest sites of Red-crown crane used in this paper all come from field investigation[17] and are converted into vector point data by Arc Catalog 9.0 for use. The elevation data came form GTOPO30 which were established by EROS of WSGS. The landscape data came from utilized two Landsat TM images (2006) covering the study area whose orbit numbers are 120-27, 119-27 and phases are August 2006, September 2006 respectively. After the process of geometric rectification, coordinate transformation and enhancement, according to the features of the TM images, interpretation keys and interpretation precision can be fixed with the help of topographic maps and field investigation. Via artificial interactive process, interpretation is performed by direct interpretation, image processing method, information fusion and logical reasoning on the working platform of GIS. Landscape can be divided into two types: natural landscape and artificial landscape. Natural landscapes include forest, grassland, water, reed swamp, beach, river and saline alkali land. Artificial landscapes include paddy field, dry farmland, pool, town and rural residential area. The interpretation precision reaches 93.4% after field investigation, verification and rectification in July 2009. The input data of ENFA should cover the same study area, besides the resolution of grid and unit should be accordant. Therefore, unified Krasovsky 1940_Albers project is generated from the data above on the working platform of ArcGIS 9.0. The grid data is resample as 100m×100m, then using Grid Convert of Arcview 3.3, grid data is converted into Idrisi format. The quantitative data (such as elevation) of EGV can be used directly while the qualitative data (such as land use type) should be converted into frequency type or distance type before it is input into the model. The frequency type data is calculated by the method of moving windows. The size of the window is related to ecological habit of a certain species and the size of attention. Speaking of the nest habitat of birds, the radius of the window should not smaller than the feeding distance or their nest range in their breeding season[18] [19]. Taking references on Red-crowned crane’s selection of nest site[13~15][17], this paper set the radius as 1500m. The distance type data means the minimum distance between all the grid units and their corresponding variables on the
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Fig. 1. Nest habitat distribution for Red-crowned crane in Zhalong wetland
EGV map. These two types of qualitative data are computed under modules of Biomapper 4. Finally 22 eco-geographic variables can be produced (Fig.1). Then using Box-Cox algorithm[20], EGV is standardized. 2.2.3 Analysis of the Model and Its Verification Method Using Medians algorithm[21] and the first five EGVs that meet the requirement of cumulative contribution could be selected. Thus habitat-suitability maps for Red-crowned crane can be generated. These five EGVs show 98.3% of the nest habitat information. The value range of HIS is 0~100. 0 means the most unsuitable habitat while 100 means the most suitable habitat. This model uses K Fold Cross-Validation method to verify the simulation method of habitat. The main process is as follows: the coordinate of Red-crown crane’s nest sites are divided into 6 parts equally. Five of them are used to generate habitat-suitability map for Red-crowned crane while the last one is used to verify the model. AVI is
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adopted to describe the simulation precision of the model[20]. This process of verification above should be repeated 6 times to make sure that every nest site is used in this verification. Mean value and standard deviation are selected to measure the precision of the model.
3 3.1
Analysis of Results The Relationship between the Utilization of Red-Crowned Crane’s Nest Habitat and EGVs
The results of ENFA shows(Fig 1) that Red-Crowned cranes in Zhalong wetland prefer locations with lower elevation value (less than the mean location=147m and marginality of M=-0.187) and lower slope (less than the mean slope=20.4° and marginality of M=-0.172) to build their nest. What’s more, the slope direction tends to be south or southeast (marginality of M=0.231). As with landscape type, reed swamp close to water is Red-crowned crane’s favorite. The occurrence frequency of reed swamp around nest habitat almost reaches 100% (marginality M=0.498). The distance between nest and reed swamp is 0m (marginality of distance of Md=-0.245). When the original landscape maps are superimposed, the results are as follows: among the 63 nests, there are 54 nests located within the reed swamp and there is some distance from the boundary (larger than 500m). 9 nests lie on the edge of reed swamp and 6 nests of them are surrounded by grassland (the minimum distance ≈200m, Md=-0.058; the occurrence frequency >42%, marginality of frequency of Mf=-0.104). The other 3 nests have water surrounding (the minimum distance ≈150m, marginality of distance of Md=-0.161; the occurrence frequency >30%, marginality of frequency of Mf=-0.024). The results above show that reed swamp is the dominant landscape which determines the location of Red-crowned crane’s nest. Except for water and grassland, saline and alkali land also lies close to the nest of Red-crowned crane (the minimum distance≈550m, Md=-0.027). It shows that these landscapes also play important roles when Red-crowned cranes select their nest habitat. The distance between forest, river, beach and the nest sites is comparatively long. Meanwhile, the occurrence frequency of these landscapes around the nest habitat is comparatively low. It shows that these landscapes are of secondary importance in the selection of nest habitat. In artificial landscape, towns and industrial buildings, rural residential sites, roads (307 national highway and Funen railway), farmland (paddy field and dry farmland) have significant influence on the selection of Red-crowned crane’s nest site. The marginality factor of these variables are all larger than 0.2 except towns and pools. The minimum distance between nest sites and paddy field and dry farmland are about 900m and 1100m respectively. The minimum distance to town (the city of Daqing ),rural residential sites, Funen railway, 307 national highway are about 15500m, 900m, 9800m and 6100m respectively. It shows that Red-crowned cranes tend to avoid these landscapes in their selecting their nest sites. Dry farmland is the most influential factor among the interference factors of human activity (Mf=-0.314; Md=0.277) and pool has the smallest influence (Md=-0.107).
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Table 1. List of EGVs in ENFA, variance explained by the first five (out of 22) ecological factors, and coefficient values for the 23 variables Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
(57.3%)
(28.6%)
(6.6%)
(4.2%)
(1.90%)
Frequency of reed
0.498
-0.002
-0.147
-0.264
0.349
Distance to forest
0.427
-0.004
-0.022
-0.01
-0.016
Frequency of dry farmland
-0.314
-0.354
-0.738
-0.379
0.504
Distance to dry farmland
0.277
0.002
0.009
0.011
0.004
Distance to rural residential
0.253
-0.004
-0.009
-0.001
0.004
Distance to reed
-0.245
-0.051
0.16
-0.171
-0.61
Aspect
0.231
-0.034
-0.087
-0.025
0.045
Distance to railroad
0.214
0.013
0.031
-0.125
-0.042
Distance to highway
-0.212
0.604
-0.055
0.386
0.101
Distance to paddy
-0.207
-0.2
0.387
-0.09
0.173
Elevation
-0.187
0.005
-0.013
-0.007
-0.054
EGVs
Distance to town
0.179
0.172
-0.36
-0.214
-0.206
Slop
-0.172
-0.012
0.085
0.12
0.103
Frequency of saline land
-0.168
0.001
-0.071
-0.13
0.148
Distance to water surface
-0.161
0.001
-0.015
-0.002
-0.004
Distance to reservoir & pond
-0.107
-0.02
0.092
-0.003
0.058
Frequency of grass
-0.104
-0.003
-0.107
-0.178
0.25
Distance to river
-0.069
-0.142
0.036
-0.664
-0.166
Distance to grass
-0.058
-0.001
-0.003
-0.007
-0.002
Distance to floodplain
0.038
0.11
0.296
-0.148
0.137
Distance to saline land
-0.027
-0.001
0.019
0.021
0.001
Frequency of water surface
-0.024
-0.003
-0.063
-0.106
0.146
Notes: EGVs are sorted by decreasing absolute value of coefficients on the marginality factor. Factor 1 is marginality factor and the others are specialization factors. The amount of specialization accounted for is given in parentheses in each column heading. Factors in each column correspond the value of marginality of M. Positive values on M mean the species prefer location with higher values on the corresponding EGV than the mean location. Negative values on M mean the species prefer location with lower values. The larger the marginality of M is, the preference is greater.
3.2
Habitat-Suitability Map
Habitat-suitability map for Red-crowned crane in Zhalong natural reserve is generated by using the algorithm of Medians and the spatial distribution map of LSI in the study area can be got. Using the instruction called Reclassify of the post-processer in Biomapper4, habitat-suitability maps can be divided into 4 parts: (1) the most unsuitable (0≤HSI≤25); (2) unsuitable (25
