Fractal landscape method: an alternative approach to measuring area-restricted searching behavior

Tremblay, Yann; Roberts, Antony J.; Costa, Daniel P.

doi:10.1242/jeb.02710

Cited by 67 publications

(51 citation statements)

References 41 publications

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“…By this reasoning the most dangerous competitor firms surrounding a focal firm will be fractally structured. Many studies of competitive biological predator/prey ecosystems (we cite 19 here) show repeatedly that they have fractal structures (McMahon and Bonner, 1983;Schmidt-Nielsen, 1984;Aronson, 1992;Russell et al, 1992;Niklas, 1994;Tsuda, 1995;West et al, 1997;Solé et al, 2001;Cuddington and Yodzis, 2002;Haskell et al, 2002;Shinchi et al, 2002;Xiao et al, 2002;Hoddle, 2003;Liu and Chen, 2003;Ferguson, 2004;Laidre et al, 2004;Phillips et al, 2004;Tremblay et al, 2007;Sims et al, 2008).…”

Section: Requisite Fractal Structure From Underlying Scale-free Theorymentioning

confidence: 96%

When organisations and ecosystems interact: toward a law of requisite fractality in firms

McKelvey¹,

Lichtenstein²,

Andriani³

2012

IJCLM

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Abstract:Complexity science has evolved greatly in the past 30 years, starting from its European roots in Prigogine's dissipative structures model of phase transitions, continuing through the Santa Fe School's focus on self-organised adaptation as explained through computational simulations, and now to it's most recent focus on power laws and their basis in scale-free causes. After briefly reviewing these three approaches to complex systems, we attempt to integrate them into a broad-based model of organisational design and performance. Our model develops the law of 'requisite fractality' -an updated version of Ashby's original law for organisations in dynamic environments. Implications for organisations and managers are discussed.

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Section: Requisite Fractal Structure From Underlying Scale-free Theorymentioning

confidence: 96%

When organisations and ecosystems interact: toward a law of requisite fractality in firms

McKelvey¹,

Lichtenstein²,

Andriani³

2012

IJCLM

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“…In addition to changes in movement patterns, periods of area-restricted search (ARS) are often used to identify foraging behavior and important feeding habitats (Kareiva & Odell 1987, Fauchald & Tveraa 2003, Tremblay et al 2007, Weimerskirch et al 2007). Periods of ARS were determined using the fractal landscape method (Tremblay et al 2007).…”

Section: Animal Handling Research Was Conducted Frommentioning

confidence: 99%

Coupling GPS tracking with dive behavior to examine the relationship between foraging strategy and fine-scale movements of northern fur seals

Kuhn

Tremblay²,

Ream³

et al. 2010

Endang. Species. Res.

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The foraging strategies of diving marine species are often categorized into 3 fundamental groups (epipelagic, mesopelagic, and benthic foraging) based on diving, habitat use, and diet studies. Because these foraging strategies are influenced by the distribution and behavior of the prey being targeted, we would expect search behavior and space use to differ depending on the strategy employed. Since northern fur seals Callorhinus ursinus display both epipelagic and benthic foraging strategies, they were an ideal model to test the hypothesis that fine-scale movement and space-use patterns will vary when animals use markedly different foraging strategies. Dive bouts were characterized into foraging strategies based on numerous dive parameters (depth, duration, etc.). For each strategy, we compared movement patterns (e.g. transit rate and path straightness) and space use (area-restricted search [ARS] zones) around St. Paul Island, Alaska, USA. Nearly all dive parameters were significantly different between foraging strategies (epipelagic vs. benthic). In addition, epipelagic bouts were more sinuous and covered a greater total distance than benthic bouts. However, the greater distances traveled in epipelagic bouts were due to longer bout durations, as transit rates were not different between the 2 strategies. On average, < 2 ARS zones were identified per trip, and the characteristics of epipelagic and benthic ARS zones were not different. By combining dive behavior with precise at-sea locations, this study has provided a greater understanding of the finescale foraging behavior of northern fur seals. Monitoring changes in foraging behavior over time and comparing behavior among populations with differing population trajectories may provide more clues as to why northern fur seal numbers on St. Paul Island continue to decline.

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“…There are a variety of approaches to estimate where and when marine animals are likely to be feeding, ranging from measurements derived from surface tracks [i.e. transit rate first passage time, fractal landscape, state space models (Fauchald and Jonsen et al, 2005;Tremblay et al, 2007;Patterson et al, 2008)] to variations in the diving pattern (Mori, 1998;Bailleul et al, 2008). However, none of these methods provide a measure of food intake and, depending on the animals' foraging strategies and movement patterns, they may miss or over-represent foraging (Robinson et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Northern elephant seals adjust gliding and stroking patterns with changes in buoyancy: validation of at-sea metrics of body density

Aoki

Watanabe

Crocker

et al. 2011

Journal of Experimental Biology

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160

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SUMMARYMany diving animals undergo substantial changes in their body density that are the result of changes in lipid content over their annual fasting cycle. Because the size of the lipid stores reflects an integration of foraging effort (energy expenditure) and foraging success (energy assimilation), measuring body density is a good way to track net resource acquisition of free-ranging animals while at sea. Here, we experimentally altered the body density and mass of three free-ranging elephant seals by remotely detaching weights and floats while monitoring their swimming speed, depth and three-axis acceleration with a high-resolution data logger. Cross-validation of three methods for estimating body density from hydrodynamic gliding performance of freely diving animals showed strong positive correlation with body density estimates obtained from isotope dilution body composition analysis over density ranges of 1015 to 1060kgm -3 . All three hydrodynamic models were within 1% of, but slightly greater than, body density measurements determined by isotope dilution, and therefore have the potential to track changes in body condition of a wide range of freely diving animals. Gliding during ascent and descent clearly increased and stroke rate decreased when buoyancy manipulations aided the direction of vertical transit, but ascent and descent speed were largely unchanged. The seals adjusted stroking intensity to maintain swim speed within a narrow range, despite changes in buoyancy. During active swimming, all three seals increased the amplitude of lateral body accelerations and two of the seals altered stroke frequency in response to the need to produce thrust required to overcome combined drag and buoyancy forces. Supplementary material available online at

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Fractal landscape method: an alternative approach to measuring area-restricted searching behavior

Cited by 67 publications

References 41 publications

When organisations and ecosystems interact: toward a law of requisite fractality in firms

When organisations and ecosystems interact: toward a law of requisite fractality in firms

Coupling GPS tracking with dive behavior to examine the relationship between foraging strategy and fine-scale movements of northern fur seals

Northern elephant seals adjust gliding and stroking patterns with changes in buoyancy: validation of at-sea metrics of body density

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