Dynamical response to fishing varies with compensatory mechanism: An abalone population model

Bardos, David C.; Day, Robert W.; Lawson, Nicholas T.; Linacre, Nicholas A.

doi:10.1016/j.ecolmodel.2005.07.015

Cited by 16 publications

(10 citation statements)

References 45 publications

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“…Fertility and allometric growth rates were the same as those used by Bardos et al . () for a fast‐growing abalone population. We set annual survival in the largest size class (>118 mm) at 0.72 based on locally derived estimates for non‐harvested populations of H. laevigata (Shepherd, ).…”

Section: Methodsmentioning

confidence: 99%

“…We modified survival rates for size classes used by Bardos et al . () proportionally so that the upper limit on annual survival was 0.72 (Appendix S1).…”

Section: Methodsmentioning

confidence: 99%

“…We developed a pre-breeding, stage-structured matrix model (Akc ßakaya & Root, 2005). Matrix elements governed transitions among seven length classes (size classes are provided in Bardos et al, 2006), derived for a time step of 1 year from growth, fertility and mortality data for abalone populations in Australia (Appendix S1). Fertility and allometric growth rates were the same as those used by Bardos et al (2006) for a fast-growing abalone population.…”

Section: Coupled Niche-population Modelmentioning

confidence: 99%

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Population dynamics can be more important than physiological limits for determining range shifts under climate change

Fordham

Mellin

Russell

et al. 2013

Global Change Biology

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Evidence is accumulating that species' responses to climate changes are best predicted by modelling the interaction of physiological limits, biotic processes and the effects of dispersal-limitation. Using commercially harvested blacklip (Haliotis rubra) and greenlip abalone (Haliotis laevigata) as case studies, we determine the relative importance of accounting for interactions among physiology, metapopulation dynamics and exploitation in predictions of range (geographical occupancy) and abundance (spatially explicit density) under various climate change scenarios. Traditional correlative ecological niche models (ENM) predict that climate change will benefit the commercial exploitation of abalone by promoting increased abundances without any reduction in range size. However, models that account simultaneously for demographic processes and physiological responses to climate-related factors result in future (and present) estimates of area of occupancy (AOO) and abundance that differ from those generated by ENMs alone. Range expansion and population growth are unlikely for blacklip abalone because of important interactions between climate-dependent mortality and metapopulation processes; in contrast, greenlip abalone should increase in abundance despite a contraction in AOO. The strongly non-linear relationship between abalone population size and AOO has important ramifications for the use of ENM predictions that rely on metrics describing change in habitat area as proxies for extinction risk. These results show that predicting species' responses to climate change often require physiological information to understand climatic range determinants, and a metapopulation model that can make full use of this data to more realistically account for processes such as local extirpation, demographic rescue, source-sink dynamics and dispersal-limitation.

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Section: Methodsmentioning

confidence: 99%

“…We modified survival rates for size classes used by Bardos et al . () proportionally so that the upper limit on annual survival was 0.72 (Appendix S1).…”

Section: Methodsmentioning

confidence: 99%

Section: Coupled Niche-population Modelmentioning

confidence: 99%

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Population dynamics can be more important than physiological limits for determining range shifts under climate change

Fordham

Mellin

Russell

et al. 2013

Global Change Biology

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“…To date, the stock management of H. rubra and H. laevigata has been built on outputs from non‐spatial stochastic and deterministic population modelling techniques (e.g. Bardos et al. , 2006).…”

Section: Discussionmentioning

confidence: 99%

“…To date, the stock management of H. rubra and H. laevigata has been built on outputs from non-spatial stochastic and deterministic population modelling techniques (e.g. Bardos et al, 2006). Our spatial modelling provides a key understanding of spatial patterns of abalone abundance and their primary drivers.…”

Section: Discussionmentioning

confidence: 99%

Geographic range determinants of two commercially important marine molluscs

Mellin

Russell

Connell

et al. 2011

Diversity and Distributions

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Aim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood‐based generalized linear models and boosted regression trees. For each modelling technique, a two‐step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c. 1‐km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine‐resolution predictive range maps (c. 1 km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established.

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Conservation of Biodiversity

Chadès¹

2013

Markov Decision Processes in Artificial Intelligence

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Dynamical response to fishing varies with compensatory mechanism: An abalone population model

Cited by 16 publications

References 45 publications

Population dynamics can be more important than physiological limits for determining range shifts under climate change

Population dynamics can be more important than physiological limits for determining range shifts under climate change

Geographic range determinants of two commercially important marine molluscs

Conservation of Biodiversity

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