Geostatistical modelling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observation efforts

被引:109
作者
Monestiez, P
Dubroca, L
Bonnin, E
Durbec, JP
Guinet, C
机构
[1] INRA, Unite Biometrie, F-84914 Avignon 9, France
[2] CNRS, Ctr Etud Biol Chize, F-79360 Villiers En Bois, France
[3] Univ Mediterranee, Ctr Oceanol Marseille, F-13288 Marseille 9, France
关键词
relative abundance map; fin whale; sightings data; geostatistics; kriging; variogram estimation; Poisson distribution; bias correction;
D O I
10.1016/j.ecolmodel.2005.08.042
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Obtaining accurate maps of relative abundance is an objective that may be difficult to achieve on the basis of spatially heterogeneous observation efforts and infrequent and sparse animal sightings. However, characterizing spatial distribution of wild animals such as fin whales is a major priority to protect these populations and to study their interactions with their environment. We have associated a geostatistical model with the Poisson distribution to model both spatial variation and discrete observation process. Assuming few weak hypotheses on the distribution of abundance, we have improved the experimental variogram estimate using weights that are derived from expected variances and proposed a bias correction that accounts for the variability added by the Poisson observation process. In the same way the kriging system was modified to interpolate directly the theoretical underlying animal abundance better than noisy observations from count data. For cumulative count data of fin whales over the summers 1992-2001, the method gave a map of the relative abundance which is informative on the spatial patterns. Kriging interpolation variances were dramatically reduced - ratio from 0.015 to 0.26 - compared to usual Ordinary Kriging on raw data. Adding the hypothesis of stationarity over time the variogram estimated on cumulative data can be then used with more sparser annual data. (C) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:615 / 628
页数:14
相关论文
共 32 条
[1]   Zero-inflated models with application to spatial count data [J].
Agarwal, DK ;
Gelfand, AE ;
Citron-Pousty, S .
ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 2002, 9 (04) :341-355
[2]  
Burrough P.A., 2000, Principles of Geographic Information Systems
[3]   GIS and geostatistics: Essential partners for spatial analysis [J].
Burrough, PA .
ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 2001, 8 (04) :361-377
[4]   Cetacean distribution related with depth and slope in the Mediterranean waters off southern Spain [J].
Cañadas, A ;
Sagarminaga, R ;
García-Tiscar, S .
DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS, 2002, 49 (11) :2053-2073
[5]  
CHILeS J.-P., 1999, WILEY SER PROB STAT
[6]   Bayesian prediction of spatial count data using generalized linear mixed models [J].
Christensen, OF ;
Waagepetersen, R .
BIOMETRICS, 2002, 58 (02) :280-286
[7]  
DAVID L, 2002, THESIS U MONTPELLIER
[8]   Model-based geostatistics [J].
Diggle, PJ ;
Tawn, JA ;
Moyeed, RA .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 1998, 47 :299-326
[9]  
DIMIGLIO N, 1999, THESIS U MONTPELLIER
[10]   ABUNDANCE OF FIN WHALES AND STRIPED DOLPHINS SUMMERING IN THE CORSO-LIGURIAN BASIN [J].
FORCADA, J ;
DISCIARA, GN ;
FABBRI, F .
MAMMALIA, 1995, 59 (01) :127-140