Study of Marine Pollution Impact on the Habitat Selection of Indo-Pacific Humpback Dolphins

doi:10.13209/j.0479-8023.2017.115

Abstract

Abstract:

To explore impact on marine habitats caused by coastal pollution, predictive models of the Indo-Pacific humpback dolphin (Sousa chinensis) distribution were generated using the Maxent modelling approach, based on dolphin occurrence data in Sanniang Bay and its adjacent waters in Guangxi Province during 2006-2007, the independent oceanographic parameters derived from Google Earth Digital Elevation Model (DEM) and Landsat images, and the water environmental data from the marine investigation in 2012. Results indicated that the distance to the major river mouths was the strongest predictors, and water nutrition and persistent pollutants were the important factors for the dolphin distribution in both wet and dry seasons. Based on the results, habitat selection strategies of the dolphin were discussed: the dolphin tended to select estuaries with abundant prey and avoid highly polluted areas, which could be useful in designing Marine Protected Areas (MPAs) and formulating management strategies.

Key words: marine pollution, Indo-Pacific humpback dolphins (Sousa chinensis), habitat selection, MaxEnt modeling, POPs, heavy metals

摘要：

为了探索污染对海洋生物栖息地的影响, 以2006—2007年中华白海豚在广西钦州三娘湾及其邻近海域的出现数据为基础, 结合Google海洋地形数据、卫星遥感数据和2012年度海洋环境调查数据, 构建干、湿季节海豚分布预测的最大熵模型, 发现与主要河口的距离是决定海豚栖息地分布的最主要因素。比较基于不同环境变量的模型预测结果, 发现海豚分布范围还受到水体营养盐和持久性污染物质的限制性影响, 在干旱季节和湿润季节都出现预测栖息地面积缩小的现象。根据模型预测和比较结果, 得到中华白海豚的栖息地选择策略: 选择河口海域生活以获得丰富的鱼类食物, 同时趋向于回避高污染风险的区域。研究结论对海洋保护区的选址及管理策略具有参考意义。

关键词: 海洋污染, 中华白海豚, 栖息地选择, 最大熵模型, 持久性有机污染物(POPs), 重金属

Mei CHEN, Yuqin SONG, Dakong QIN, Qian ZHU, Chongxiao WEI. Study of Marine Pollution Impact on the Habitat Selection of Indo-Pacific Humpback Dolphins[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(6): 1068-1080.

陈梅, 宋豫秦, 秦大公, 祝茜, 韦重霄. 海洋污染对中华白海豚栖息地选择的影响研究[J]. 北京大学学报自然科学版, 2017, 53(6): 1068-1080.

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URL: https://xbna.pku.edu.cn/EN/10.13209/j.0479-8023.2017.115

https://xbna.pku.edu.cn/EN/Y2017/V53/I6/1068

Figures/Tables 9

References 71

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模型	变量数量	变量名称
模型	变量数量	海洋地理地形组	水环境组
SNW_DS_5	5	Aspect, Depth, Dis_ mrm, Dis_iso5, Slope	-
SNW_DS_10	10		BOD, WPb, WPCBs, B_chla, FHW
SNW_WS_5	5		-
SNW_WS_14	14		SST, SD, N, P, BOD, WDDT, WHg, WPCBs, B_chla

模型	变量数量	变量名称
模型	变量数量	海洋地理地形组	水环境组
SNW_DS_5	5	Aspect, Depth, Dis_ mrm, Dis_iso5, Slope	-
SNW_DS_10	10		BOD, WPb, WPCBs, B_chla, FHW
SNW_WS_5	5		-
SNW_WS_14	14		SST, SD, N, P, BOD, WDDT, WHg, WPCBs, B_chla

模型	样本量		AUC		ESS阈值	遗漏率/%
模型	训练数据	验证数据	训练数据	验证数据	ESS阈值	遗漏率/%
SNW_DS_5	66 (44.9)	21 (14.3)	0.905	0.880	0.437	18.4 (27/147)
SNW_DS_10	66 (44.9)	21 (14.3)	0.940	0.928	0.452	10.9 (16/147)
SNW_WS_5	56 (43.0)	18 (13.8)	0.917	0.794	0.449	16.9 (22/130)
SNW_WS_14	56 (43.0)	18 (13.8)	0.959	0.822	0.345	10.0 (13/130)

模型	样本量		AUC		ESS阈值	遗漏率/%
模型	训练数据	验证数据	训练数据	验证数据	ESS阈值	遗漏率/%
SNW_DS_5	66 (44.9)	21 (14.3)	0.905	0.880	0.437	18.4 (27/147)
SNW_DS_10	66 (44.9)	21 (14.3)	0.940	0.928	0.452	10.9 (16/147)
SNW_WS_5	56 (43.0)	18 (13.8)	0.917	0.794	0.449	16.9 (22/130)
SNW_WS_14	56 (43.0)	18 (13.8)	0.959	0.822	0.345	10.0 (13/130)

变量	贡献类型	贡献率/%
变量	贡献类型	SNW_DS_5	SNW_DS_10	SNW_WS_5	SNW_WS_14
Dis_mrm	累积	74.9	54.1	62.6	13.7
Dis_mrm	置换	60.6	59.1	49.8	39.0
Slope	累积	16.0	5.1	20.8	4.9
Slope	置换	17.5	4.8	26.4	3.0
Depth	累积	6.0	6.1	4.0	0.8
Depth	置换	13.7	13.0	19.9	0.7
Aspect	累积	2.3	1.5	2.2	0.1
Aspect	置换	4.0	1.4	3.3	0.2
Dis_iso5	累积	0.8	1.2	10.9	7.3
Dis_iso5	置换	4.2	0	0.5	8.9
B_chla	累积	0	20.5	0	0.6
B_chla	置换	0	10.1	0	1.4
SD	累积	0	0	0	26.3
SD	置换	0	0	0	18.9
WPCBs	累积	0	4.0	0	0.6
WPCBs	置换	0	3.3	0	1.8
BOD	累积	0	4.0	0	1.8
BOD	置换	0	0	0	0.7
FHW	累积	0	3.0	0	0
FHW	置换	0	0.9	0	0
WPb	累积	0	0.6	0	0
WPb	置换	0	7.3	0	0
P	累积	0	0	0	15.0
P	置换	0	0	0	12.3
N	累积	0	0	0	3.1
N	置换	0	0	0	6.0
WDDT	累积	0	0	0	19.2
WDDT	置换	0	0	0	2.7
WHg	累积	0	0	0	6.2
WHg	置换	0	0	0	4.2
SST	累积	0	0	0	0.4
SST	置换	0	0	0	0