Trophic interactions and range limits: the diverse roles of predation

Holt, Robert D.; Barfield, Michael

doi:10.1098/rspb.2008.1536

Cited by 114 publications

(108 citation statements)

References 51 publications

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“…Lately, however, theoretical models have suggested that biotic interactions may also be important in shaping range limits (Holt and Barfield, 2009a), and recent empirical research has suggested that the distribution of many European bird species may not be as strongly related to climate as previously thought (Beale et al, 2008a). This weaker than expected association with abiotic climate variables may be explained if biotic interactions are more important than previously thought.…”

Section: Application To the European Bird Atlas Datamentioning

confidence: 54%

“…Secondly, there is growing evidence to suggest that the importance of climate and abiotic variables has previously been overstated (e.g. Watts and Worner 2008) largely because processes like the biotic interactions included in our models have previously been neglected (Davis et al, 1998;Beale et al, 2008b;Holt and Barfield, 2009b;La Sorte et al, 2009). It would clearly be valuable to develop the methods further to include both continuous variables and binary variables in the same analyses.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Faisal

Dondelinger

Husmeier

et al. 2010

Ecological Informatics

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The complexity of ecosystems is staggering, with hundreds or thousands of species interacting in a number of ways from competition and predation to facilitation and mutualism. Understanding the networks that form the systems is of growing importance, e.g. to understand how species will respond to climate change, or to predict potential knock-on effects of a biological control agent. In recent years, a variety of summary statistics for characterising the global and local properties of such networks have been derived, which provide a measure for gauging the accuracy of a mathematical model for network formation processes. However, the critical underlying assumption is that the true network is known. This is not a straightforward task to accomplish, and typi- * Corresponding author. Tel: +44 (0) cally requires minute observations and detailed field work. More importantly, knowledge about species interactions is restricted to specific kinds of interactions. For instance, while the interactions between pollinators and their host plants are amenable to direct observation, other types of species interactions, like those mentioned above, are not, and might not even be clearly defined from the outset. To discover information about complex ecological systems efficiently, new tools for inferring the structure of networks from field data are needed. In the present study, we investigate the viability of various statistical and machine learning methods recently applied in molecular systems biology: graphical Gaussian models, L1-regularised regression with least absolute shrinkage and selection operator (LASSO), sparse Bayesian regression and Bayesian networks. We have assessed the performance of these methods on data simulated from food webs of known structure, where we combined a niche model with a stochastic population model in a 2-dimensional lattice.We assessed the network reconstruction accuracy in terms of the area under the receiver operator characteristics (ROC) curve, which was typically in the range between 0.75 and 0.9, corresponding to the recovery of about 60% of the true species interactions at a false prediction rate of 5%. We also applied the models to presence/absence data for 39 European warblers, and found that the inferred species interactions showed a weak yet significant correlation with phylogenetic similarity scores, which tended to weakly increase when including bio-climate covariates and allowing for spatial autocorrelation. Our findings demonstrate that relevant patterns in ecological networks can be identified from large-scale spatial data sets with machine learning methods, and that these methods have the potential to contribute novel important tools for gaining deeper insight into the structure and stability of ecosystems.

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Section: Application To the European Bird Atlas Datamentioning

confidence: 54%

Section: Discussionmentioning

confidence: 99%

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Faisal

Dondelinger

Husmeier

et al. 2010

Ecological Informatics

View full text Add to dashboard Cite

show abstract

“…Conversely, while to some extent previously downplayed, the importance of natural enemies in limiting the geographic ranges of the species that they use is coming increasingly to the fore (Case et al 2005). This latter perspective is developed in this special issue in papers on the roles of predation in range limitation (Holt & Barfield 2009), and more specifically the effects of sterilizing diseases (Antonovics 2009). In both cases, apparently counter-intuitive patterns can result from species interactions, including that predation can under some circumstances permit prey species to have larger ranges than would be the case in the absence of predation.…”

Section: Abstract: Ecology; Evolution; Geographic Rangementioning

confidence: 99%

Geographic range limits of species

2009

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Understanding the forms that the geographic range limits of species take, their causes and their consequences are key issues in ecology and evolutionary biology. They are also topics on which understanding is advancing rapidly. This themed issue of Proc. R. Soc. B focuses on the wide variety of current research perspectives on the nature and determinants of the limits to geographic ranges. The contributions address important themes, including the roles and influences of dispersal limitation, species interactions and physiological limitation, the broad patterns in the structure of geographic ranges, and the fundamental question of why at some point species no longer evolve the ability to overcome the factors constraining their distributions and thus fail to continue to spread. In this introduction, these contributions are placed in the wider context of these broad themes.

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“…A substantial research effort has been devoted to understanding the processes that set species range limits [48 -50], and theory has demonstrated that both the ecological [51,52] and evolutionary [53] structure of a species's range can differ substantially depending upon whether the gradient influences reproduction, survival or amount of habitat available. The few studies that have, to date, considered the dynamics of species shifting their ranges across environmental gradients have incorporated extremely simplified representations of the landscape and of dispersal [46,54,55].…”

Section: Discussionmentioning

confidence: 99%

Risky movement increases the rate of range expansion

et al. 2011

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The movement rules used by an individual determine both its survival and dispersal success. Here, we develop a simple model that links inter-patch movement behaviour with population dynamics in order to explore how individual dispersal behaviour influences not only its dispersal and survival, but also the population's rate of range expansion. Whereas dispersers are most likely to survive when they follow nearly straight lines and rapidly orient movement towards a non-natal patch, the most rapid rates of range expansion are obtained for trajectories in which individuals delay biasing their movement towards a non-natal patch. This result is robust to the spatial structure of the landscape. Importantly, in a set of evolutionary simulations, we also demonstrate that the movement strategy that evolves at an expanding front is much closer to that maximizing the rate of range expansion than that which maximizes the survival of dispersers. Our results suggest that if one of our conservation goals is the facilitation of range-shifting, then current indices of connectivity need to be complemented by the development and utilization of new indices providing a measure of the ease with which a species spreads across a landscape.

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Trophic interactions and range limits: the diverse roles of predation

Cited by 114 publications

References 51 publications

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Geographic range limits of species

Risky movement increases the rate of range expansion

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