At-sea distribution and scale-dependent foraging behaviour of petrels and albatrosses: a comparative study
在海分布和规模依赖觅食行为和海燕信天翁：一项比较研究
第一个出版:27November2006https://doi.org/10.1111/j.1365-2656.2006.01186.xCitations:2 250人
大卫Pinaud,Centre d'etudes biologiques de Chizé,CNRS普遍定期审议1934年，79360Villiers-en-Bois，法国。 传真: +33(0)549 096 526. E-mail:puffin@cebc.cnrs.fr
Summary 摘要
- 1
In order to study and predict population distribution, it is crucial to identify and understand factors affecting individual movement decisions at different scales. Movements of foraging animals should be adjusted to the hierarchical spatial distribution of resources in the environment and this scale-dependent response to environmental heterogeneity should differ according to the forager's characteristics and exploited habitats.
为了研究和预测是人口分布，这是至关重要的，以确定和了解因素影响个体运动的决定，在不同的尺度。 运动的觅食的动物应该加以调整的分层空间分布的环境中的资源及这个尺度依赖对环境的异质性，应该根据不同的强盗的特性和利用的栖息地。 - 2
Using First-Passage Time analysis, we studied scales of search effort and habitat used by individuals of seven sympatric Indian Ocean Procellariiform species fitted with satellite transmitters. We characterized their search effort distribution and examined whether species differ in scale-dependent adjustments of their movements according to the marine environment exploited.
使用第一次通过时的分析，我们研究了比额表的搜索工作和人居署使用的个人的七同域分布印度洋Procellariiform种配有卫星发射器。 我们的特他们努力搜索分配和审查是否种不同的比例相关的调整他们的行动根据海洋环境的利用。 - 3
All species and almost all individuals (91% of 122 individuals) exhibited an Area-Restricted Search (ARS) during foraging. At a regional scale (1000s km), foraging ranges showed a large spatial overlap between species. At a smaller scale (100s km, at which an increase in search effort occurred), a segregation in environmental characteristics of ARS zones (where search effort is high) was found between species.
所有的物种和几乎所有的个人(91%的122人)表现出一种区域限制的搜索(索再解放联盟)在觅食。 在区域一级(1000公里)、觅食的范围表现出很大的空间重叠之间的种类。 在较小的规模(100公里，在其中增加一个搜索行动发生)，隔离在环境特性的ARS区(其中搜索的努力是高)之间发现的物种。 - 4
Spatial scales at which individuals increased their search effort differed between species and also between exploited habitats, indicating a similar movement adjustment for predators foraging in the same habitat. ARS zones of the two populations of wandering albatross Diomedea exulans (Crozet and Kerguelen) were similar in their adjustments (i.e. same ARS scale) as well as in their environmental characteristics. These two populations showed a weak spatial overlap in their foraging distribution, with males foraging in more southerly waters than females in both populations.
空间尺度上在其个人增加了他们努力搜索不同物种之间和也之间的利用的栖息地，表明一个类似的运动调整为食肉动物觅食一样的栖息地。 ARS区域两个人口的漂泊信天翁Diomedea exulans(克罗泽和凯尔盖朗)进行类似的调整(即同一索再解放联盟比例)以及在他们的环境特性。 这两个群体显示出弱空间的重叠，在他们的觅食的分配，男性觅食在更偏南海域比女性在这两个群体。 - 5
This study demonstrates that predators of several species adjust their foraging behaviour to the heterogeneous environment and these scale-dependent movement adjustments depend on both forager and environment characteristics.
这项研究表明，食肉动物的几种调整它们觅食行为上的异质环境以及这些比例相关的运动进行调整取决于这两个强盗和环境特点。
Introduction 介绍
Understanding the response of organisms to environmental heterogeneity is an important topic in ecology when studying species habitat selection and is at the basis of the growing landscape ecology discipline (Lima & Zollner 1996; Fortin & Agrawal 2005). To achieve this goal, an increasing number of studies have focused on the movements of foraging animals and on the perception of ecological scales by organisms through an adjustment of their movement in response to heterogeneity in the landscape (Johnson et al. 2002; Fauchald & Tveraa 2003; Fritz, Saïd & Weimerskirch 2003; Frair et al. 2005; Nams 2005; Pinaud & Weimerskirch 2005; Bailey & Thompson 2006). These studies are based on the hypothesis that a foraging animal should increase its search effort in areas where resources are plentiful rather than in areas where resources are scarce. For example, an animal should increase its turning rate and/or reduce its speed as a response to increases in intake rate or prey encounter rate, adopting a so-called Area-Restricted Search (ARS) behaviour (Kareiva & Odell 1987). Then one can study these changes in animal movements as a response to heterogeneity in its environment, helping to understand underlying processes acting on population dynamics across habitats (Lima & Zollner 1996). As environmental heterogeneity is present at several temporal and spatial scales, we expect that these adjustments in search effort vary also with scale (Fauchald 1999). In addition, as different species can exploit the same environment (resulting in a spatial overlap at large scale) but with different foraging strategies (e.g. orientated vs. random search in albatrosses; Weimerskirch 1998) or exploiting different resources, we can also expect differences in movement adjustment (i.e. in ARS) between species (Fritz et al. 2003). To our knowledge, the hypotheses have never been explored among predator species foraging in a heterogeneous and patchy environment. However, studying how different species of the same guild respond to environmental heterogeneity allows us to understand their coexistence in a spatially varying environment, where segregation at a finer scale (i.e. a foraging niche specialization) is expected to minimize competition (Brown 2000). Here we focus on a comparison of scale-dependent movement adjustment and spatial distribution between foraging seabird species.
了解响应的生物对环境的不均是一个重要的主题，在生态学研究物种的栖息地的选择并且是在基础日益增长的景观生态学纪律(利马&佐纳1996年；Fortin&Agrawal2005年)。 为实现这一目标，越来越多的研究集中在该运动的觅食的动物，并在感知上的生态尺度的生物体通过一个调整他们的行动在应对异质性的景观(约翰逊等人。 2002年；Fauchald&Tveraa2003年；弗里茨，赛义德*&weimerskirch共2003年；Frair et al. 2005年；Nams2005年；Pinaud&Weimerskirch共2005年；贝利与汤普森2006年)。 这些研究是基于这一假设，即一个觅食的动物应该加强其搜索努力在地区资源丰富，而非在领域资源都是稀缺的。 例如，一个动物应该增加其转率和/或降低其速度作为响应，以增加入学率，或者猎物的收到率，采取一种所谓的区域限制的搜索(索再解放联盟)行为(Kareiva&奥德尔1987年)。 然后一个可以研究这些变化在动物的运动作为应对异质性的环境，有助于理解的基本进程的作用在人口动态全的栖息地(利马&佐纳1996年)。 作为环境的非均质性存在若干时间和空间尺度，我们希望，这些调整在搜索的努力有所不同，也有规模(Fauchald1999年)。 此外，如不同的物种可以利用同样的环境(从而在空间的重叠，在大型)，但不同的觅食的战略(例如为导向的与随机搜索信天翁;weimerskirch共1998年)或利用不同的资源，我们也可以期望差异的运动调整(即在索再解放联盟)之间的物种(Fritz et al. 2003年)。 据我们所知，假设具有从来没有探讨之中的食肉动物觅食一个异类和零散的环境。 然而，研究如何不同物种的同样的协会应对环境的非均质性使我们了解他们的存在空间上变化的环境中，那里的隔离，在更细的规模(即一个觅食的适当位置的专业化)预计减少的竞争(棕色2000年)。 在这里，我们把重点放在比较的规模依赖运动调节和空间分布之间的觅食的海鸟种类。
Marine predators are good focal organisms to study scale-dependent adjustments in relation to environmental heterogeneity because of the wide range of spatial and temporal scales occurring in their resource distribution and abundance. In the marine environment, interactions between ocean currents, bathymetry and other physical and biological processes promote growth and retention of plankton, leading to higher spatial heterogeneity in organism distribution (Haury, McGowan & Wiebe 1978), which influences the distribution of top predators such as seabirds (see Hunt et al. 1999). In such conditions, scale-dependent adjustments of movement are found in some foraging seabirds (Viswanathan et al. 1996; Fauchald & Tveraa 2003; Fritz et al. 2003; Pinaud & Weimerskirch 2005; see also Johnson et al. 2002 for a terrestrial example). One might expect these adjustments to be widely adopted by these predators.
海洋的掠食者是好的焦生物体的研究比例相关的调整有关环境的异质性，因为范围广泛的空间和时间尺度发生在他们的资源的分布和丰度。 在海洋环境之间的相互作用洋流、水深等物理和生物过程，促进增长和保留的浮游生物，导致更高的空间上的异质性的有机体分布(Haury,巨鳄&韦博1978年)，其影响分布的顶部的食肉动物，如海鸟(见亨特et al. 1999年)。 在这样的条件，按比例调整的运动中发现的一些觅食海鸟(维斯瓦纳坦et al. 1996年；Fauchald&Tveraa2003年；弗里茨et al. 2003年；Pinaud&weimerskirch共2005年；另见约翰逊等人。 2002年对于地面的实例)。 人们可能期望这些调整得到广泛通过这些掠食者。
Factors affecting these adjustments could be numerous. Considering that some habitats should be more profitable than others for a predator, Fritz et al. (2003) suggested that different species of predator should respond at different scales according to the exploited habitat, because of differences in prey distribution or density in habitats. In fact, Fauchald (1999) described a model where a predator should adjust its search radius according to its prey encounter rate, which is related to the prey density and the size of the patch. In this case, higher encounter rates (meaning small patches with high prey density) led to an adjustment with a smaller search radius. Then similarities in movement adjustment among a predator guild could be expected in relation to prey availability in a particular habitat.
影响因素，这些调整可能是众多的。 考虑到一些栖息地的应该是更加有益于其他捕食者，弗里茨et al. (2003年)建议，不同物种的捕食者应该在不同尺度根据受剥削的栖息地，因为存在差异的猎物的分布和密度中的栖息地。 事实上，Fauchald(1999年)描述一个模型，其中一个捕食者应调整其搜索范围根据其猎物的收到率，这是关系到猎物密度和大小的修补程序。 在这种情况下，较高的收到率(即小块与高猎物密度)，导致调整有一个较小的搜索范围。 然后相似之处在移动调整在一个捕食者协会可望在关系到猎物位于一个特定的栖息地。
Among predators, differences in movement adjustments could be also expected in relation to targeted prey (difference in prey distribution) and the forager's own strategy. More than 10 species of large Procellariiforms (albatrosses and petrels > 1 kg in mass) inhabit the southern part of the Indian Ocean. They feed on prey (mainly fish, squid and crustacean in different proportion according to species, see review in Cherel & Klages 1998) known to present a heterogeneous, patchy, scale-dependent (hierarchical) resource distribution (Pakhomov & Froneman 2000). The distribution and foraging movements of such long-ranging central-place predators has been studied more frequently since the use of satellite tracking (Jouventin & Weimerskirch 1990). Between species, there is an extensive overlap in foraging distribution at the regional (1000s km) scale (Weimerskirch 1998; BirdLife International 2004; Phillips, Silk & Croxall 2005). Different foraging strategies can be identified (Weimerskirch 1998): some species use a ‘loop searching’ strategy, whereas others commute quickly to a specific area where foraging effort is high. These strategies can be related to the prey distribution. For example, the wandering albatross Diomedea exulans (L.) relies mainly on squid whose distribution is extremely patchy and scattered over extensive areas using a low-cost looping strategy to cover extensive surface (Weimerskirch, Gault & Cherel 2005). Therefore, one might expect intrinsic differences in foraging adjustments between species.
在掠食者，不同的运动的调整也可以预期，在关系到有针对性的猎物(差异的猎物分布)和强盗的自己的战略。 10多种类的大Procellariiforms(信天翁和海燕>1公斤的质量)居住在南部的一部分印度洋。 它们捕食的猎物(主要是鱼、鱿鱼和甲壳类动物在不同的比例根据种类，见审查在Cherel&.克拉格斯，1998年)已知的明异构零散，规模依赖(层级)的资源分配(Pakhomov&Froneman2000年)。 分布和觅食的动作的这种长等中央-地方的掠食者已经研究了更加频繁，因为使用的卫星的跟踪(Jouventin&weimerskirch共1990年)。 物种之间，有一个广泛的重叠，在觅食的分配在区域(1000公里)的规模(weimerskirch共1998年；禽鸟生命国际组织2004年；菲利普丝绸Croxall2005年)。 不同的觅食的战略，可以确定(weimerskirch共1998年)：有些物种的使用一个循环搜索'战略，而其他人的通勤快的一个特定区域里觅食的努力是很高的。 这些战略可以是有关猎物的分布。 例如，漂泊信天翁Diomedea exulans(L)主要依靠鱿鱼的分布极其零碎的和分散在广泛的领域使用的一种低成本的循环战略，以涵盖广泛的表面(weimerskirch共,Gault&Cherel2005年)。 因此，人们可能期望的内在差异觅食调整之间的种类。
In this study, we use a scale-dependent approach to analyse individual foraging trips of six albatross and a large petrel species (1·2–10 kg) in the South Indian Ocean, using satellite-tracking data and First-Passage Time (FPT) analysis (Fauchald & Tveraa 2003). FPT analysis is based on the calculation of time taken for an animal to cross a circle with a given radius. This calculation is done all along the path by moving the circle every d distance and for increasing radii. Then, FPT can be used as a scale-dependent measure of search effort. Furthermore, it allows the determination of the spatial scale at which a forager increases its search effort (when adopting an ARS) and adjusts its movements according to environmental heterogeneity. We used this approach to address the following points. First, one might expect movement adjustments (i.e. ARS) in these top predators. Second, by adopting this scale-dependent approach and determining ARS magnitude, we can identify interspecific and intraspecific differences in search effort distribution. We expect that responses in foraging movement of these predators should differ according to species and habitats, as species differ in their characteristics and in their foraging strategy, and also that oceanic habitats differ in prey distribution and availability.
在这项研究中，我们使用一个规模依赖性的方法来分析各个觅食旅行的六个信天翁和海燕大的物种(1·2至10公斤)在南印度洋，使用卫星跟踪数据和一通道的时间(FPT)分析(Fauchald&Tveraa2003年)。 FPT分析的基础上计算的时间采取一种动物穿过一个圈给半径。 这个计算是所有沿着路径移动圆每d距离并增加的半径。 然后，FPT可以用作一个规模依赖的措施搜索的努力。 此外，它允许所确定的空间尺度，在这个强盗增加，其搜索的努力(当采用一个索再解放联盟)和调整其行动根据环境异质性。 我们用这种方法，以解决以下几点。 第一，人们可能预期运动调整(即索再解放联盟)在这些顶部的食肉动物。 第二，通过采用这个尺度依赖的方法，并确定索再解放联盟的幅度，我们可以确定种和种差异在搜索的努力分配。 我们期望的反应，在觅食的运动的这些掠食者应该根据不同的物种和生境，物种在不同的自己的特性并在他们的觅食的战略，并且，海洋生境在不同的猎物的分布和可用性。
Materials and methods 材料和方法
satellite tracking and analysis
卫星跟踪和分析
The field studies were carried out on seven species of large size Procellariiforms equipped with the Argos system (satellite transmitters, Platform Terminal Transmitters, 32g PTT100 and 64g Toyocom 2038C, Microwave Telemetry, Columbia, MD, USA) during the same reproductive period (mid-incubation), from three study sites in South Indian Ocean: Crozet (46°26′S; 51°52′E), Kerguelen (49°40′S; 70°15′E) and Amsterdam (37°51′S; 77°31′E) Islands in south Indian Ocean. Deployments were carried out on different bird populations, from Crozet on wandering albatross Diomedea exulans L. (WAc) in 1998 (n = 17 individuals), 1999 (n = 14) and 2000 (n = 9), sooty albatross Phoebetria fusca Hilsenberg (SA) in 1992 (n = 5), 1993 (n = 3) and 1994 (n = 5), light-mantled albatross P. palpebrata Forster (LMA) in 1994 (n = 3) and white-chinned petrel Procellaria aequinoctialis L. (WCP) in 1996 (n = 6); from Kerguelen on wandering albatross (WAk) in 2002 (n = 12) and black-browed albatross Thalassarche melanophrys Temminck (BBA) in 1999 (n = 7); from Amsterdam, Amsterdam albatross D. amsterdamensis Roux et al. (AA) in 1996 (n = 6) and 2000 (n = 9) and Indian yellow-nosed albatross T. carteri Rothschild (YNA) in 2000 (n = 9) and 2002 (n = 17). Signal emission rate was the same for all PTTs (i.e. 90 s). Details about deployments can be found in Weimerskirch (1998), Weimerskirch et al. (1999), Pinaud & Weimerskirch (2002, 2005) and Waugh & Weimerskirch (2003). The satellite transmitter represented 0·5–2% of the mass of birds. In some cases, several trips existed for the same individual, so we used the first one for analysis to avoid pseudoreplication. Gender was determined for WA, AA and YNA only, using morphometric measurements. According to Argos location accuracy class (A, B, 0, 1, 2 and 3, Argos 1996), all class B fixes were removed since their accuracy was low (46 km ± 59, n = 67, calculated with PTTs at a fixed position). Locations were then filtered according to maximum speeds (see Weimerskirch et al. 1993, 2000; for details) obtained from calculations (Pennycuick 1982) and observations (Alerstam, Gudmundsson & Larsson 1993) for each species. After filtering the Argos locations, accuracy was better than 2 km. Position of the sun was calculated at each location, with the civil twilight considered at 6° below the horizon.
该领域的研究进行了对七种类的大大小Procellariiforms装有Argos系统(卫星发射器、终端平台发射机、32克PTT100和64g Toyocom2038C、微波遥测、哥伦比亚,MD,USA)期间同生殖期间(中期孵化)，从三个研究地点在南印度洋:Crozet(46°26；51°52'E),凯尔盖朗(49°40；70°15'E)和阿姆斯特丹(37°51；77°31'E) 群岛在南印度洋。 部署进行了不同的鸟类种群，从克罗泽海上漂泊信天翁Diomedea exulans L.(WAc)在1998年(n=17人)、1999年(n=14)和2000年(n=9)，乌黑的天翁Phoebetria仔、稚、Hilsenberg(SA)在1992年(n=5)、1993年(n=3)和1994年(n=5)、光满天天翁P.palpebrata Forster(LMA)，1994年(n=3) 和白chinned海燕Procellaria aequinoctialis L.(世界气候方案)在1996年(n=6)；从凯尔盖朗上漂泊信天翁(WAk)，2002年(n=12)和黑眉信天翁Thalassarche melanophrys Temminck(工商管理学士)1999年(n=7)；从阿姆斯特丹，阿姆斯特丹信天翁D.amsterdamensis Roux et al. (AA)在1996年(n=6)和2000年(n=9)和印度黄鼻子信天翁T.carteri Rothschild(YNA)在2000年(n=9)和2002年(n=17). 信号发射率是相同的所有PTTs(即90s)。 细节约的部署可以发现在weimerskirch共(1998年)，weimerskirch共et al. (1999年)，Pinaud&weimerskirch共(2002年，2005年)和沃&weimerskirch共(2003年)。 卫星的发射表示0·5-2％质量的鸟类。 在某些情况下，几次旅行存在的同一个人，所以我们用的第一个进行分析，以避免pseudoreplication. 性别问题被确定为WA，AA和YNA只，采用形态测量的测量。 根据阿尔戈斯定位精度等级(A,B,0,1,2和3，Argos1996)，所有B类修正是删除，因为他们的精确度很低(46公里±59,n=67，计算PTTs在一个固定的位置)。 地点然后过滤根据最高的速度(见weimerskirch共et al. 1993年，2000年；对于细节)从获得计算(Pennycuick1982年)和意见(Alerstam,Gudmundsson&Larsson1993)对于每个物种。 过滤后的阿尔戈斯的位置，精确度好于2公里。 太阳的位置是计算出每个位置，与民间暮认为至6°以下的地平线。
environmental data 环境数据
Environmental data, i.e. bathymetry, sea surface temperature (SST) and chlorophyll-a concentration (Chl-a, as a proxy of biological production), were extracted for the study zone containing all locations, located between 10°E and 115°E and between 25°S and 70°S (see Fig. 1). Bathymetric data were obtained from the ETOPO5 5 × 5 min Navy database (http://ingrid.ldeo.columbia.edu/SOURCES/WORLDBATH/). In order to link search zones to an average primary productivity, seasonal (austral spring and summer) SeaWiFS Chl-a 1997–2004 climatologies were obtained from Level-3 Standard Mapped Images (http://seawifs.gsfc.nasa.gov/cgi/level3.pl) at a resolution of 9 km. We used monthly Sea Surface Temperature (SST) with a resolution of 9 km from the NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR), distributed by the Physical Oceanography Distributed Active Archive Center at the Jet Propulsion Laboratory (http://poet.jpl.nasa.gov/). SST data were obtained for the month of tracking for each bird and Chl-a data for the corresponding season. Oceanographic habitats were delimited using SST gradients calculated with ArcView 3·2 GIS (ESRI Inc., Redlands, CA, USA) over a grid of a resolution of 9 km. Fronts, as zones of strong SST gradients, appeared clearly by this method, and indicated precisely oceanographic habitat limits rather than by the method using SST values alone. In the study area according to Holliday & Read (1998), we defined from the north to the south the following oceanographic habitats: Benguela Current (BC), Agulhas Front (AF), Agulhas Current Retroflection (ACR), North Subtropical Front (NSTF), Subtropical Waters (north of ARF; STWn), Agulhas Return Front (ARF), Subtropical Waters (south of ARF; STWs), South Subtropical Front (SSTF), Sub-Antarctic Surface Waters (north of SAF; SASWn), Sub-Antarctic Front (SAF), Sub-Antarctic Surface Waters (south of SAF; SASWs), Polar Front (PF), Antarctic Surface Waters (AASW), Southern Boundary of Antarctic Circumpolar Current (SB), Marginal Ice Zone (MIZ).
环境数据，即水深测量、海洋表面温度(SST)和叶绿素-a的浓度(智利，作为代理的生物生产)，分别提取研究区域含所有地点的，位于10°之间的电子和115°E和25度和西经70度(见图。 1). 水深数据取自ETOPO5 5×5分钟海军数据库(http://ingrid.ldeo.columbia.edu/SOURCES/WORLDBATH/). 为了链接搜索区域的平均初级生产力，季节性(南半球春季和夏季)海洋观测宽投影区域Chl的-1997年至2004年气候学都获得3级的标准映射图像(http://seawifs.gsfc.nasa.gov/cgi/level3.pl)在一项决议的9公里。 我们使用的每月海水表面温度(SST)分辨率为9公里，从国家海洋与大气层管理局/美国航天局探路者的先进的极高分辨率辐射计(高级甚高分辨率辐射计)，分布式的物理海洋学分积极的归档中心在喷气推进实验室(http://poet.jpl.nasa.gov/). SST数据是获得对于一个月的跟踪每个鸟类和Chl-一个数据用于相应的季节。 海洋栖息地被分隔的使用SST梯度计算ArcView3·2GIS(ESRI公司， 雷德兰兹，加利福尼亚州，美国)超过一个网格中的一项决议的9公里。 战线，作为区域的强SST渐变，出现了清楚通过该方法，并指出精确的海洋生境限制而不是通过使用该方法的SST值单。 研究区域中根据霍利迪和阅读(1998年)，我们定义从北方到南方的下列海洋栖息地：本格拉洋流(BC)Agulhas前(AF)，Agulhas前Retroflection(DS)、北亚热带前(NSTF)、亚热带水域(北部的东盟区域论坛;STWn),Agulhas返回前(东盟区域论坛)、亚热带水域(南的东盟区域论坛;STWs)、南亚热带前(SSTF)，亚南极表面水域(北部的苏丹武装部队;SASWn), 亚南极前(苏丹武装部队)，分的南极地面水(南的苏丹武装部队;SASWs)、极前(PF)、南极地面水(AASW)、南部边界的南极极地附近的当前(SB)、边缘冰区(女士).
Argos satellite tracking locations for the seven Procellariiform species in south Indian Ocean (one colour per species/population). Chl-a concentration (mg m−3) austral summer climatology for the period 1997–2003 is represented in the background. Lines indicate oceanographic habitat limits in January 1999 as revealed by SST gradients (see text for details). Red stars indicate study colonies and continents are in grey. (upper panel) Amsterdam and wandering albatrosses; (lower panel) black-browed, yellow-nosed, light-mantled and sooty albatrosses and white-chinned petrel.
Argos卫星跟踪地点的七个Procellariiform物种在南印度洋(一种颜色的每种类/人口). 叶绿素-a的浓度(毫米 −3 )南半球的夏季气候学期间是1997-2003年表示的背景。 线指示海洋栖息地限制在一月份1999年所显示的SST梯度(见上文)。 红色的星星研究表明殖民地和大陆都是灰色的。 (上小组)阿姆斯特丹和漂泊信天翁;(下面板)黑眉，黄鼻子、光满天和乌信天翁和白chinned海燕.
first-passage time analysis
第一个通道的时间分析
We followed Fauchald & Tveraa's (2003) method to calculate First-Passage Time (FPT) along each trip. Analyses were written and performed using the Software R (Version 2·0·0, R Development Core Team 2005). FPT is defined as the time required to cross a circle with a given radius r (see Fauchald & Tveraa 2003). Values of FPT were calculated every second kilometre travelled, for spatial scales r from 5 to 1000 km. The relative variance S(r) in FPT was calculated as a function of radius r. This variance is given by Var(log(t(r))), where t(r) is FPT for circle of radius r, and is log-transformed to make the variance S(r) independent of the magnitude of the mean FPT (see Fauchald & Tveraa 2003). S(r) was then plotted in relation to r (for r from 5 to 1000 km). According to Fauchald & Tveraa (2003), a peak in relative variance S(r) reveals presence of ARS behaviour and indicates the ARS scale r at which a bird increased its search effort. By inspecting the plot of FPT values at the correct spatial scale (where a peak of variance occurred) as a function of time elapsed since departure (see Fauchald & Tveraa 2003 in their fig. 3), we were able to locate where the bird increased its search effort implying more intense foraging behaviour (Weimerskirch, Wilson & Lys 1997). Following this FPT/travelling time plot, we selected the spatial locations of high search effort (i.e. high FPT values) called hereafter ‘ARS zones’. To do so, a FPT value threshold was determined according to its multimodal distribution: ARS zones corresponded to the mode of higher FPT values (see Fig. S1 in Supplementary materials for an example). Environmental variables were extracted according to the location of ARS zones. These zones were generally close in time together (mainly less than 6 h) and in the same oceanographic habitat for the same individual. For each individual trip, we calculated the distance from the main (i.e. highest duration in ARS) ARS zone to the colony. As these birds can move and search at night (Weimerskirch & Guionnet 2002), FPT was calculated all along the path, including locations during the night. But, in some cases (11 birds, independently of species) when inspecting the FPT/travelling time plot, high FPT values corresponded strictly to night-time periods for the whole path (see Fig. S2 in Supplementary materials for an example), particularly when the peak of variance occurred at very small spatial scales (10–20 km). In this case, we suspected that birds were sitting on water at night for a long period. This artefact (mix-up between searching at small scale and resting at night) can be caused by the accuracy inherent in the Argos system, which does not allow the discrimination of small-scale movements within a 1 km radius from the absence of movement (Weimerskirch, Salamolard & Jouventin 1992). Previous FPT analysis on paths including locations on the nest (motionless incubating birds equipped with PTT) showed that search at very small scale (< 10 km) cannot be separated from resting periods. High FPT values occurring every night could therefore reflect sitting on the water (sleeping or foraging at night waiting for prey (Weimerskirch et al. 1997) rather than in-flight searching behaviour). As we were interested in searching movements rather than resting, these periods with high FPT values at night were removed for these birds. To do so, all the locations corresponding to the ARS zone were deleted and time and positions in the path were recalculated accordingly. Then FPT analysis was done in order to detect changes in movements at a larger scale. After this removal, the influence of suspected resting periods at night was considered again in this second calculation by inspecting the FPT/travelling time plot and if this effect was still suspected (high FPT values at night), the bird was not considered for further analysis.
我们随后Fauchald&Tveraa的(2003年)的方法来计算第一个通道的时间(FPT)每个沿着旅行。 分析编写和执行使用软件的R(版本2·0·0R发展核心团队2005年)。 FPT被定义为需要的时间跨圆给radius r(见Fauchald&Tveraa2003年)。 值的FPT计算了每一个第二个公里走过，对于空间尺度r5至1000公里。 相对差异S(r)在FPT计算为一个功能radius r. 这种差异是由Var(log(t(r)))，其中t(r)FPT圆形半径r，并登录转让的差异S(r)独立的幅度意味着FPT(见Fauchald&Tveraa2003年)。 S(r)然后绘制有关的r(r5至1000公里)。 根据Fauchald&Tveraa(2003年)一个峰值在相对的差异S(r)揭示了存在的ARS行为，并表示ARS规模r在这个鸟增加了搜索行动。 通过检查的情节FPT值在正确的空间规模(其中一个峰值的差异发生)作为一个功能经过的时间因离开(见Fauchald&Tveraa2003年在图。 3)，我们能够找到这里的鸟增强其搜索的努力，这意味着更加强烈的觅食的行为(weimerskirch共,Wilson&Lys1997年)。 以下这FPT/旅行时间的情节，我们选择的空间位置的高搜索的努力(即高FPT值)以下简称为'ARS区'. 为此，一个FPT值的阈值定根据其多式联运的分布：ARS区符合该模式的高FPT值(见图。 S1在补充材料，用于一个例子)。 环境变量的取根据的位置的ARS区。 这些区域通常是关闭的时间在一起(主要是小于6h)和在同一个海洋栖息地的同一个人。 对于每一个人的旅行，我们计算的距离主要的(即最高持续时间在索再解放联盟)索再解放联盟区域的殖民地。 作为这些鸟可以移动和搜查在夜间(weimerskirch共&Guionnet2002年)、FPT计算所有沿着路径，包括地点过夜。 但是，在某些情况下(11鸟类、独立的品种)当检查FPT/旅行时间的情节，高FPT值符合严格的夜晚时段的整个路径(见图。 S2在补充材料的一个例子)，尤其是高峰时的差异发生在非常小的空间尺度(10至20公里)。 在这种情况下，我们怀疑，鸟人坐在水晚上很长一段时间。 这个实体(混合行动之间的搜索，在小规模和搁在晚上的)可能引起的精确度固有的Argos系统，该系统不允许歧视的小规模运动在1公里半径范围从没有运动(weimerskirch共,Salamolard&Jouventin1992年)。 以前的FPT分析路径，包括地点在巢(不动孵化的小鸟配备PTT)显示，搜索在非常小的比例(< 10公里)是分不开的休息时间。 高FPT值发生的每天晚上可能会因此反映坐在水(睡觉或在晚上觅食等待猎物(weimerskirch共et al. 1997年)，而不是在飞行搜索行为). 因为我们感兴趣搜索运动的而不是静止的，这些时期的高FPT值在晚上被删除对这些鸟类。 为此，所有的位置相对应的ARS区被删除，时间和位置的路径是重新计算。 然后FPT分析样做是为了检测到的变化运动在更大的规模。 在此之后清除的影响的可疑的休息时间，晚上再次审议在这个第二算通过检查FPT/旅行时间的情节，如果这种影响仍然怀疑(高FPT值夜晚)，小鸟是不是认为对于进一步的分析。
We explained ARS scales (log-transformed) as a function of species, oceanographic habitats and Chl-a concentrations. To do so, Type I linear models (i.e. considering each term in addition) were compared and selected using Akaike's Information Criterion (AIC, Burnham & Anderson 1998). Individual search effort in a habitat was defined as the sum of FPT in this habitat (as an index of search effort at the spatial scale at which effort was increased) divided by the total distance moved in this habitat according to Argos tracking. Distribution of search effort was compared with the general distribution at sea used by the considered population. This was achieved by calculating the utilization distribution (UD) kernel estimation (Worton 1989) of all Argos locations by population, as an assessment of general distribution at sea. UD was estimated using ‘adehabitat’ package on R software (Calange & Basille 2004). We set the smoothing factor h constant for each population at 2·59 decimal degrees (mean of the h calculated on each population using Least-Squares Cross-Validation, Silverman 1986), in order to allow comparison as h could vary between each population. To study interspecific and intraspecific differences in search effort distribution according to environmental characteristics (bathymetry, SST and Chl-a), we performed a normalized linear discriminant analysis on ARS zones under systat 9 and R package ‘ade4’ (Thioulouse, Dufour & Chessel 2004). This allows the discrimination (as a measure of nonoverlapping) of the environmental characteristics for each ARS zone according to species (or populations). Each ARS zone was characterized by median (to minimize influence of extreme values and effect of difference in ARS scales) SST, bathymetry and Chl-a concentration (log-transformed), and its position in oceanographic habitat. Data were transformed to satisfy normality, and statistics used the R and systat 9 software.
我们解释ARS秤(日志变换)作为一个功能的物种、海洋生境和叶绿素-a的浓度。 为此，种类型我的线性模型(即考虑到每个术语在外)进行比较和选择使用赤池的信息的标准(AIC,Burnham&安德森1998年)。 各个搜索努力在人居署被定义为的总和FPT在此人居署(作为索引的，搜索的努力在空间尺度上在其努力增加)的总的距离移动在这栖息地根据阿尔戈斯的跟踪。 分发的搜索的努力是比较一般分布在海上使用的所考虑的人口。 这是通过计算利用率的分布(特拉华)核估计(Worton1989年)的所有阿尔戈斯的地点通过人口，作为评估的一般分布在海上。 UD估计使用'adehabitat'包R软件(Calange&Basille2004年)。 我们设定的平滑的因素h定为每个人口在2·59十进制度(平均h计算的每个居民使用最小二乘交叉验证，Silverman1986年)，以允许比较，因为h之间可能有所不同，每人口。 研究种和种差异在搜索行动分布根据环境特征(水深测量、SST和叶绿素-a),我们进行归一化的线性别分析在ARS区在systat9和R包ade4'(Thioulouse,杜福尔&谢塞尔，2004年)。 这使得歧视(作为一项措施的互不重叠)的环境特性，每个ARS区根据物种(或群体). 每个索再解放联盟区域的特点是通过中间值(以尽量减少影响的极端的价值和作用的差别ARS秤)SST、水深测量和叶绿素-a的浓度(日志变换)，其地位在海洋栖息地。 数据变换，以满足正常状态和统计数据中使用的R和systat9软件。
Results 结果，
Large petrels and albatrosses breeding at Crozet, Kerguelen and Amsterdam Islands foraged over a wide geographical range, from Antarctica close to the ice edge to subtropical regions and South Africa (Fig. 1). All oceanographic habitats were crossed during movements at sea, but the majority of Argos locations were concentrated in the sub-Antarctic and subtropical waters around colonies.
大海燕和信天翁繁殖，在克罗泽，凯尔盖朗和阿姆斯特丹群岛觅食过一个广泛的地域范围，从靠近南极洲的冰边缘的亚热带地区和南部非洲(图。 1). 所有的海洋生境被交叉的运动期间在海上，但是大多数的阿尔戈斯的地点集中在次南极和亚热带水域周围的殖民地。
area-restricted search and scale of search effort
区域限制的搜索和规模的搜索行动
According to FPT analysis, 110 individuals (for a total of 122) showed a peak of variance, varying from 30 to 120 km (Table 1 and see Fig. 2A for some examples), indicating the presence of ARS behaviour during foraging trips for all species (71–100% of individuals according to species/populations). Examples of individual foraging trips showing an ARS behaviour (peak of variance) are represented in Fig. 2. Among these 110 individuals, seven birds (four YNA, two WA and one AA) presented a high search effort associated strictly with the night, suspecting that birds were resting on water for a long time rather than moving (see Fig. S2 in Supplementary materials). As we were not able to identify clearly changes in search effort in these birds, they were not considered from further analysis. As revealed by the pattern of variation (FPT values in function of time since departure, see an example in Fig. S1, Supplementary materials), birds used from one to six ARS zones per trip, generally grouped in time, indicating that birds generally concentrated their search effort in one habitat. Averaged parameters for each species are presented in Table 1. With species sampled at different years, no significant differences were observed between years.
根据FPT分析，110名个人(总共122)显示出高峰的差异，从30至120公里(见表1和图。 2A供的一些例子)，表明存在ARS行为期间觅食的旅行所有种类(71-100%的个人根据物种/种群). 例的各个觅食的旅行表示ARS行为(峰值方差)的代表在图。 2. 这其中110人，七个鸟类(四YNA，两个WA和一个AA)提出了一个高搜索的努力相关的严格的夜晚怀疑鸟类被搁在水里很长时间而不是运动(见图。 S2在补充材料中)。 因为我们不够清楚地查明变化的搜索努力在这些鸟，他们不认为进一步分析。 作为显示通过模式的变化(FPT价值观中的功能的时间，因为出发，看到一个例子图。 S1，补充材料中)，鸟类使用从一个到六个ARS区，每次旅行，通常进行分组时，表示鸟一般集中他们的搜索努力在一个栖息地。 平均参数对于每个物种列于表1。 有物种的取样，在不同年，没有观察到明显的差别之年。
表1。 规模的搜索行动之间的距离殖民地和主要ARS区，平均值为环境参数，集中搜索地区的觅食的旅行表示ARS行为的七Procellariiform物种("a"阿姆斯特丹,'c'对于克罗泽和'k'的凯尔盖朗。). 身体质量信息来自该出版物中引用的材料和方法部分。 值表示±SD
| Species (island) 物种(岛屿) | Mean body mass (kg) 意思是体重(公斤) |
Birds with ARS (n/total) 鸟类与索再解放联盟(n/总) |
Birds with ARS (% total) 鸟类与索再解放联盟(%的总) |
ARS scale (km) ARS规模(公里) | Distance ARS zone to colony (km) 距离ARS区域的殖民地(公里) |
Median SST (°C) 中值SST(°C) | Median Bathymetry (m) 中位数的水深测量(m) | Median Chl-a conc. (mg m−3) 正中叶绿素-a的浓。 (毫米 −3 ) |
|---|---|---|---|---|---|---|---|---|
| AA (a) AA(a) | 6·5 | 10/14 | 71·4 | 72 ± 119 72±119 | 915 ± 825 915±825 | 19·4 ± 1·2 19·4±1·2 | −3581 ± 1188 -3581±1188 | 0·138 ± 0·065 0·138±0·065 |
| BBA (k) 工商管理学士(k) | 3·7 | 7/7 | 100 | 29 ± 15 29±15 | 187 ± 68 187±68 | 2·1 ± 0·7 2·1±0·7 | −1006 ± 824 -1006±824 | 0·531 ± 0·232 0·531±0·232 |
| LMA (c) LMA(c) | 3·1 | 3/3 | 100 | 73 ± 46 73±46 | 1089 ± 645 1089±645 | 3·7 ± 3·1 3·7±3·1 | −4052 ± 799 -4052±799 | 0·232 ± 0·059 0·232±0·059 |
| SA (c) SA(c) | 2·6 | 10/13 | 76·9 | 78 ± 50 78±50 | 543 ± 277 543±277 | 4·4 ± 5·5 4·4±5·5 | −2888 ± 668 -2888±668 | 0·244 ± 0·430 0·244±0·430 |
| WA (c) WA(c) | 9·6 | 34/40 | 85·0 | 63 ± 64 63±64 | 845 ± 486 845±486 | 8·4 ± 5·2 8·4±5·2 | −2386 ± 1409 -2386±1409 | 0·244 ± 0·139 0·244±0·139 |
| WA (k) WA(k) | 9·5 | 11/12 | 91·7 | 39 ± 27 39±27 | 782 ± 620 782±620 | 7·4 ± 4·5 7·4±4·5 | −3161 ± 1220 -3161±1220 | 0·240 ± 0·140 0·240±0·140 |
| WCP (c) 世界气候方案(c) | 1·2 | 6/6 | 100 | 97 ± 73 97±73 | 1935 ± 952 1935年±952 | 17·2 ± 8·4 17·2±8·4 | −3297 ± 2311 -3297±2311 | 0·341 ± 0·125 0·341±0·125 |
| YNA (a) YNA(a) | 2·4 | 22/26 | 84·6 | 121 ± 78 121±78 | 1513 ± 522 1513±522 | 15·5 ± 1·1 15·5±1·1 | −4446 ± 535 -4446±535 | 0·240 ± 0·063 0·240±0·063 |
Results given by FPT analysis for representative individual trips of a given species for large Procellariiforms in the South Indian Ocean during the incubation period. (a) Variance in log(FPT) in function of spatial scale. A peak of variance reveals an increase in search effort at the corresponding scale. (b) The foraging trips (grey, thin lines) and ARS zones (where individuals increased their search effort, in black, large lines) of the same individuals as revealed by FPT analysis. Colonies are indicated by white stars. LMA, light-mantled albatross; SA, sooty albatross; WAc, wandering albatross from Crozet and WAk from Kerguelen; YNA, Indian yellow-nosed albatross; BBA, black-browed albatross.
结果给予FPT分析为代表个人旅行一定的物种对于大Procellariiforms在南印度洋期间的潜伏期。 (a)差异在日志(FPT)在功能的空间尺度。 一个峰值的差异，揭示了增加的搜索努力在相应的规模。 (b)觅食的旅行(灰色的细线)和索马里再次解放联盟区域(个人增加他们的搜索努力，在黑色、大行)的同一个人所显示的FPT分析。 殖民地指出，由白色的星星。 LMA，光满天天翁；SA，乌黑的天翁;WAc，漂泊信天翁从Crozet和WAk从凯尔盖朗;YNA、印度黄鼻子信天翁;工商管理学士学位，黑白眉信天翁。
distribution of search effort according to habitat
分发的搜索努力根据人居署
FPT analysis allowed us to study the search effort distribution of each species (Fig. 3). YNA and AA concentrated their search effort mainly in the subtropical habitats, while SA, LMA and BBA foraged mainly in the sub-Antarctic habitat. A last group of species (WA, WCP) was able to spread their search effort from sub-Antarctic to subtropical habitats, from Antarctica to South Africa coasts.
FPT分析使我们得以研究搜索的努力分发各种(图。 3款)。 YNA和AA浓缩自己的搜索工作主要是在亚热带生境，同时SA，LMA和工商管理学士觅食，主要是在亚南极的生境。 最后一组的物种(华盛顿，世界气候方案)能够扩展他们的搜索努力从亚南极到亚热带栖息地，从南极到南部非洲海岸。
Search effort for seven Procellariiform species, with intensity of search effort as defined by FPT analysis given for each population in each oceanographic habitat (from left to right: habitats from south to north). Bar width is proportional to surface habitat availability. Diamonds indicate colony position in oceanographic habitats. For WA from Crozet and Kerguelen, black bars represent males and white bars represent females. See Materials and methods for acronyms.
From the discriminant analysis, Axis 1 and 2 (eigenvalues of 1·82 and 0·16, respectively) explained 94·9% of the cumulative proportion of total variance. The distribution of ARS zone characteristics between populations was not random (Wilks’λ = 0·273, P < 0·001), meaning significant specific differences in environmental characteristics on ARS zones, even for different species breeding on the same island (for example YNA and AA for Amsterdam Island, Fig. 4). Segregation in ARS zones between populations occurred mainly according to a gradient of surface temperature (highest F-value, indicating that SST had the highest contribution in discrimination), and secondarily according to the Chl-a concentration and bathymetry. SST and Chl-a concentration were negatively correlated. Thus, despite a wide spatial overlap in at-sea occurrence observed at a large scale (see 1, 3), populations intensified their search effort on areas with different environmental characteristics with relatively little overlap at this fine scale. The apparent overlap in environmental characteristics in ARS zones of WCP, WAc and WAk is due to their wide at-sea distribution, reflecting a large interindividual difference in ARS characteristics, which was not present in the other species (AA, YNA, LMA, BBA and SA). It is interesting to note that, despite breeding 1200 km away and thus experiencing different water masses, the two WA populations from Crozet and Kerguelen present ARS zones with similar environmental characteristics (the smallest between group F3,483 = 4·286, see Fig. 3, and also Supplementary materials S3 for detailed values), indicating that they exploit habitats with the same environmental characteristics.
Results of the discriminant analysis (factors 1 and 2 are represented by axis 1 and 2, respectively) on ARS zones according to populations, as a function of Bathymetry, SST and Chl-a concentration on these ARS zones. Ellipses represent the dispersion of ARS zones with a 95% interval. AA, Amsterdam albatross; LMA, light-mantled albatross; SA, sooty albatross; WAc, wandering albatross from Crozet and WAk, from Kerguelen; WCP, white-chinned petrel; YNA, Indian yellow-nosed albatross; BBA, black-browed albatross.
Sexual differences were observed in the characteristic of main ARS zones for the two WA populations (see 3, 5). Males of both populations (n = 17 for Crozet and 5 for Kerguelen) increased their search effort in colder waters compared with females (F1,41 = 15·92, P < 0·001), with no effect of the breeding population site alone (F1,41 = 2·02, P = 0·14) nor of the interaction population × sex (F1,41 = 0·36, P = 0·58). Otherwise, no significant difference occurred in SST on ARS zones between males and females in YNA (F1,13 = 1·28, P = 0·28).
Distribution of ARS zones of wandering albatross (WA) from Crozet and Kerguelen, and Amsterdam Albatross. Lines indicate 95 and 50% contour of Utilization Distribution (UD) from Kernel analysis on all Argos locations. Fixes represent individual ARS zones, with darker grey for females, paler for males.
variations in area-restricted search scale in relation to species and environment
No significant difference was found in ARS scale between WA from Crozet and Kerguelen (log-transformation, F1,43 = 0·931, P = 0·34), so these two populations were grouped in Linear Model analysis. Two models presented close AIC, the model with Species and the model with the additive terms Species and Habitat (Table 2). This last model was selected because, despite an important increase in the number of parameters, its AIC was lower than the first one and also because of a larger amount of variance explained (r2 = 0·24 against r2 = 0·14 for the model with Species alone, see Table 2). According to this model, the difference in ARS scale was first explained by differences between species (with minimum value of ARS scale for BBA, and maximum scale for YNA), but also by use of different oceanographic habitats, with particularly small ARS scale values for the south subtropical front and the sub-Antarctic front (Fig. 6). According to our results, this indicates that predators of different species foraging in the same habitat tend to adopt the same scale-dependent movement adjustment.
| Models | Adjusted r2 | Deviance | No. of parameters | AIC | ΔAIC |
|---|---|---|---|---|---|
| Constant | – | 283·16 | 2 | 287·16 | 11·55 |
| S | 0·143 | 260·94 | 8 | 276·94 | 1·33 |
| S + H | 0·235 | 235·60 | 20 | 275·61 | 0 |
| S + H + S × H | 0·226 | 222·29 | 31 | 284·29 | 8·68 |
| S + C | 0·136 | 260·75 | 9 | 278·76 | 3·15 |
| S + C + S × C | 0·097 | 258·53 | 15 | 288·53 | 12·92 |
| S + H + C | 0·226 | 235·53 | 21 | 277·53 | 1·92 |
| Complete | 0·260 | 190·32 | 48 | 286·32 | 10·71 |
Estimates (± standard error) of the selected type I linear model (see Table 2). log(ARS scale) = SP + HAB, as revealed by FPT analysis for 103 foraging trips of seven Procellariiform species. AA, Amsterdam albatross; LMA, light-mantled albatross; SA, sooty albatross; WA, wandering albatross; WCP, white-chinned petrel; YNA, Indian yellow-nosed albatross; BBA, black-browed albatross. See Materials and methods for habitat acronyms.
Discussion
Our FPT analysis has demonstrated that the large majority of individuals of all albatross and petrels studied adopted ARS at spatial scales varying from 30 to 120 km, these values depend on both forager species and environment characteristics. Using this approach, one can identify and understand foraging adjustments of these predators at different scales.
Resources in the marine environment present a heterogeneous and hierarchical distribution (Fauchald 1999). Such patchy resource distribution can lead to a scale-dependent pattern of foraging movement adjustment observed in marine predators such as wandering albatross (Viswanathan et al. 1996; Fritz et al. 2003), Antarctic petrel (Fauchald & Tveraa 2003), yellow-nosed albatross (Pinaud & Weimerskirch 2005) or bottle-nosed dolphin (Bailey & Thompson 2006). According to our results, ARS seems to be widely represented in marine top predators such as seabirds, as a behavioural adaptation to forage in a patchy environment (Benhamou 1992). Although at-sea distribution or trip length can change between years due to interannual environmental variability (Pinaud, Cherel & Weimerskirch 2005), we detected no significant interannual difference in ARS scale according to our data set. Therefore, the observed variations in foraging movement adjustments should be mainly related to species or habitat use.
spatial distribution and segregation of search effort
By describing foraging distribution at the spatial scale at which the individuals increased their search effort, we can assess interspecific and intraspecific differences, not only in at-sea distribution of these sympatric predators, but also in search effort distribution at a smaller scale. The overall foraging distribution studied by satellite telemetry of these seven species during breeding is well-known at a regional scale (1000s km) in the Indian Ocean (see review in Weimerskirch 1998; also Weimerskirch 1998; Weimerskirch et al. 1999; Pinaud & Weimerskirch 2002, 2005; Waugh & Weimerskirch 2003) and suggests a large spatial overlap. Our study revealed that, although a spatial overlap exists at a regional scale, a reduced overlap occurred in habitat characteristics at a finer scale (where search effort is increased). In fact, to reach a distant foraging zone, these long-range foragers need to cross several habitats, resulting in an overlap in at-sea distribution when movement behaviour is not taken into account (Brown 2000). The apparent overlap in environmental characteristics in ARS zones of WCP, WAc and WAk is due to their wide at-sea distribution that built a large confidence interval (ellipse) in Fig. 4, reflecting rather a large interindividual difference in ARS characteristics. This very long-range foraging strategy is not present in the other species (AA, YNA, LMA, BBA and SA) where we found little overlap at a fine scale. This could be related to a specialization in foraging niche when exploiting the marine habitat. In fact, this specialization is expected at a fine scale, allowing a coexistence by reducing competition (see review in Brown 2000). Another strategy for these seabirds seems to be exploiting habitats far from the breeding islands (such as WCP and WA), where the inter- and intraspecific competition is expected to be less intense (Ashmole 1971).
Intraspecific and intersite comparison of the two populations of WA shows that difference in foraging distribution appears mainly at a regional scale (1000s km), where the Kerguelen individuals foraged at higher latitudes compared with Crozet population (Fig. 5). In addition, males in both populations increased their foraging effort southward compared with females (3, 5; Prince et al. 1992; Weimerskirch et al. 1993). At the scale where search effort increased (c.100 km), ARS zones of these two populations cannot be separated according to environmental characteristics. Birds appeared to concentrate their search effort on similar patch characteristics, despite the difference in location observed at a regional scale between the two populations. This distribution at a large scale could be the result of competition for the same resources or more intense interference between the two colonies.
movement adjustments in relation to species and environment
Based on the demonstration of the existence of scale-dependent hierarchical adjustments of wandering albatross movement patterns, Fritz et al. (2003) predicted differences in scale response for different groups of predators, according to differences in their prey distribution and environmental constraints. In our study, the majority of individuals for the seven species adopted an ARS behaviour, with variations explained due to species and also habitat, with no interaction between the two factors. Effect of species can be explained by differences in prey targeted (see Cherel & Klages 1998 for a review for these species) or foraging strategies (‘searching loop’ vs. ‘commuting’; Weimerskirch 1998 Weimerskirch et al. 2005). For example, the WA searches mainly on adult squid whose distribution is scattered over extensive areas using a low cost strategy (‘loops’) to cover extensive surface (Weimerskirch et al. 2005). In the other side, YNA, as well as breeding grey-headed albatrosses Thalassarche chrysostoma (Foster) from Marion Island, were found to adopt a ‘commuting’ strategy to reach spatially predicted mesoscale eddies where a higher prey density is expected (Nel et al. 2001; Pinaud & Weimerskirch 2005). Therefore, one might expect that this affects the decision to increase search effort and the tendency to leave a patch. In addition, morphometrics and wing loads influence flying abilities and energy expenditure in albatrosses (Shaffer, Costa & Weimerskirch 2001) and might therefore affect their searching decisions.
The additive effect of oceanographic habitats on ARS scale variations (independent of species) could be interpreted as a convergence in foraging movement adjustments for these predators according to the structure and prey availability of a particular habitat, rather than responding to a physical parameter. In fact, the ARS radius of the predator could be linked to the prey encounter rate (Kareiva & Odell 1987; Fauchald 1999). In oceans, prey distributions over such large distances are difficult to assess but some studies showed that prey distribution could be linked to heterogeneity in physical and biological characteristics of oceanographic habitats (i.e. Froneman & Pakhomov 2000; Read, Pollard & Bathmann 2002). This can result in higher prey densities in some habitats. For example, YNA from Amsterdam Island exploit the turbulent zone under the influence of the Agulhas Return Current, with the presence of gyres (mean diameter of 250 km) which influence the spatial distribution of primary productivity (Froneman & Pakhomov 2000). In relation to this predictable environmental structure, this predator adopts a ‘commuting’ strategy and adjusts its searching behaviour inside this system at an average scale of 130 km, in accordance to the expected prey distribution (Pinaud & Weimerskirch 2005). Alternatively, black-browed albatrosses are known to commute mainly over neritic waters, exploiting the peri-insular slopes on the Kerguelen plateau where their preferred prey are more available (Cherel, Weimerskirch & Trouvé 2000) and distributed in small patches of higher density (see Weimerskirch et al. 2005). This could explain the smaller ARS scales observed for this species compared with the others. Our results indicate also that the south subtropical and the sub-Antarctic fronts are exploited with a lower search radius, which is consistent with the higher biological production and prey availability found in these frontal regions (see Hunt et al. 1999). It suggests also that, when searching in these productive regions, these predators encounter relatively more prey and behave in the same way in movement adjustments with a smaller search radius. Therefore, the scale-dependent response of an animal to environmental heterogeneity is a complex phenomena, depending on the forager's characteristics as well as on habitat features.
Environmental heterogeneity is considered to affect animal movements at a large range of scales, in terrestrial (Bergman, Schaefer & Luttich 2000; Johnson et al. 2002) as well as in marine ecosystems (Fritz et al. 2003; Pinaud & Weimerskirch 2005). Our results suggest that, as habitats differ in structure and prey availability at various spatial scales, long-ranging predators such as albatrosses could respond to this environmental heterogeneity from the individual to the population level, by adjusting their searching movements through different habitats. In these adjustments, observed differences are species specific but these predators seem to perceive common ecological characteristics, leading to a similar behaviour in similar habitats. We can speculate that, in this case, immediate prey encounter rate plays a major role for these predators when adjusting their searching movements at small spatial scales, whereas past experience and orientation are expected to act mainly in large-scale movements at a scale where prey distribution is more predictable (Hunt et al. 1999; Pinaud & Weimerskirch 2005). Studying movements at a smaller scale in relation to resource distribution is therefore crucial to better understand scale-dependent adjustments and finally foraging distribution of animals.
Acknowledgements
This study was funded by Institut Paul-Emile Victor (program 109) and the Centre National de la Recherche Scientifique. D P was supported by a grant from ‘Ecole Doctorale Sciences de la Vie et de la Santé’ from the Strasbourg University. Protocols and procedures were approved by the Ethical Committee of the IPEV. We would like to thank all people involved in fieldwork and PTT deployments, and in spatial analysis and programming: L. Dubroca, T. Cornulier, and C. Calenge. We thank D. Hyrenbach for help on an early draft, G. Hemson, H. Bailey and 2 anonymous referees for constructive comments. The authors declare that the experiments comply with the current laws of the country in which they were performed.
