Dr Dave Hodgson in the lab

Dr Dave Hodgson in the lab

Dr Dave Hodgson
Associate Professor of Ecology/Director of Education

Key publications | Publications by category | Publications by year

Key publications



Leggett, H.C., Benmayor, R., Hodgson, D.J., Buckling, A. (2013). Experimental evolution of adaptive phenotypic plasticity in a parasite. Current Biology, 23(2), 139-142.
Stott, I., Townley, S., Hodgson, D.J. (2011). A framework for studying transient dynamics of population projection matrix models. Ecol Lett, 14(9), 959-970.

Abstract:
A framework for studying transient dynamics of population projection matrix models.

Empirical models are central to effective conservation and population management, and should be predictive of real-world dynamics. Available modelling methods are diverse, but analysis usually focuses on long-term dynamics that are unable to describe the complicated short-term time series that can arise even from simple models following ecological disturbances or perturbations. Recent interest in such transient dynamics has led to diverse methodologies for their quantification in density-independent, time-invariant population projection matrix (PPM) models, but the fragmented nature of this literature has stifled the widespread analysis of transients. We review the literature on transient analyses of linear PPM models and synthesise a coherent framework. We promote the use of standardised indices, and categorise indices according to their focus on either convergence times or transient population density, and on either transient bounds or case-specific transient dynamics. We use a large database of empirical PPM models to explore relationships between indices of transient dynamics. This analysis promotes the use of population inertia as a simple, versatile and informative predictor of transient population density, but criticises the utility of established indices of convergence times. Our findings should guide further development of analyses of transient population dynamics using PPMs or other empirical modelling techniques.
 Abstract.  Author URL
Stott, I., Franco, M., Carslake, D., Townley, S., Hodgson, D. (2010). Boom or bust? a comparative analysis of transient population dynamics in plants. Journal of Ecology, 98(2), 302-311.
Stott, I., Townley, S., Carslake, D., Hodgson, D.J. (2010). On reducibility and ergodicity of population projection matrix models. Methods in Ecology and Evolution, 1, 242-252.
Carslake, D., Townley, S., Hodgson, D.J. (2009). Patterns and rules for sensitivity and elasticity in population projection matrices. Ecology, 90(11), 3258-3267.

Abstract:
Patterns and rules for sensitivity and elasticity in population projection matrices.

Sensitivity and elasticity analysis of population projection matrices (PPMs) are established tools in the analysis of structured populations, allowing comparison of the contributions made by different demographic rates to population growth. In some commonly used structures of PPM, however, there are mathematically inevitable patterns in the relative sensitivity and elasticity of certain demographic rates. We take a simulation approach to investigate these mathematical constraints for a range of PPM models. Our results challenge some previously proposed constraints on sensitivity and elasticity. We also identify constraints beyond those that have already been proven mathematically and promote them as candidates for future mathematical proof. A general theme among these rules is that changes to the demographic rates of older or larger individuals have less impact on population growth than do equivalent changes among younger or smaller individuals. However, the validity of these rules in each case depends on the choice between sensitivity and elasticity, the growth rate of the population, and the PPM structure used. If the structured population conforms perfectly to the assumptions of the PPM used to model it, the rules we describe represent biological reality, allowing us to prioritize management strategies in the absence of detailed demographic data. Conversely, if the model is a poor fit to the population (specifically, if demographic rates within stages are heterogeneous), such analyses could lead to inappropriate management prescriptions. Our results emphasize the importance of choosing a structured population model that fits the demographics of the population.
 Abstract.  Author URL
Carslake, D., Townley, S., Hodgson, D.J. (2009). Predicting the impact of stage-specific harvesting on population dynamics. J Anim Ecol, 78(5), 1076-1085.

Abstract:
Predicting the impact of stage-specific harvesting on population dynamics.

1. Perturbation analyses of population projection matrices predict the response of a population's growth rate to changes in lifestage-specific vital rates. Such predictions have been widely used in population management but their reliability remains hotly debated. 2. We grew replicate populations of the water flea Daphnia magna in controlled, density-independent conditions and subjected treatment populations to harvesting of the largest lifestage. We predicted the growth rate of treatment populations using sensitivity analysis (a linear approximation), and transfer function analysis (TFA; which captures nonlinear responses) applied to projection matrix models parameterized from the control populations. 3. When perturbation analyses considered only the direct effect of harvesting on adult survival, the growth rate of harvested populations (averaging 0.051) was significantly overestimated (average of 0.112) by TFA and non-significantly underestimated (average of 0.012) by sensitivity. 4. When the indirect effects of harvesting on other vital rates were accounted for in a structured perturbation, TFA gave accurate predictions (average growth rate of 0.068), while sensitivity gave significant underestimates (average of -0.043). 5. Our results demonstrate two crucial sources of error that may influence predictions of the impacts of demographic perturbations on population dynamics. First, impacts of stage-specific harvesting are inherently nonlinear, hence predictions based on sensitivity must be treated with caution. Second, stage-specific perturbations can change non-target demographic rates, even in the absence of adaptation. 6. Population managers should consider both nonlinear and indirect effects of perturbations when designing management interventions. We encourage the development of methods to assess the robustness of predictions to unforeseen perturbation structures and indirect harvesting impacts.
 Abstract.  Author URL

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