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The predicted climate change causes deep concerns on the effects of increasing temperatures and changing precipitation patterns on species viability and, in turn, on biodiversity. Models of Population Viability Analysis (PVA) provide a powerful tool to assess the risk of species extinction. However, most PVA models do not take into account the potential effects of behavioural adaptations. Organisms might adapt to new environmental situations and thereby mitigate negative effects of climate change. To demonstrate such mitigation effects, we use an existing PVA model describing a population of the tawny eagle (Aquila rapax) in the southern Kalahari. This model does not include behavioural adaptations. We develop a new model by assuming that the birds enlarge their average territory size to compensate for lower amounts of precipitation. Here, we found the predicted increase in risk of extinction due to climate change to be much lower than in the original model. However, this "buffering" of climate change by behavioural adaptation is not very effective in coping with increasing interannual variances. We refer to further examples of ecological "buffering mechanisms" from the literature and argue that possible buffering mechanisms should be given due consideration when the effects of climate change on biodiversity are to be predicted. (c) 2004 Elsevier B.V. All rights reserved
The Southern Ocean ecosystem is characterized by extreme seasonal changes in environmental factors such as day length, sea ice extent and food availability. The key species Antarctic krill (Euphausia superba) has evolved metabolic and behavioural seasonal rhythms to cope with these seasonal changes. We investigate the switch between a physiological less active and active period for adult krill, a rhythm which seems to be controlled by internal biological clocks. These biological clocks can be synchronized by environmental triggers such as day length and food availability. They have evolved for particular environmental regimes to synchronize predictable seasonal environmental changes with important life cycle functions of the species. In a changing environment the time when krill is metabolically active and the time of peak food availability may not overlap if krill's seasonal activity is solely determined by photoperiod (day length). This is especially true for the Atlantic sector of the Southern Ocean where the spatio-temporal ice cover dynamics are changing substantially with rising average temperatures. We developed an individual-based model for krill to explore the impact of photoperiod and food availability on the growth and demographics of krill. We simulated dynamics of local krill populations (with no movement of krill assumed) along a south-north gradient for different triggers of metabolic activity and different levels of food availability below the ice. We also observed the fate of larval krill which cannot switch to low metabolism and therefore are likely to overwinter under ice. Krill could only occupy the southern end of the gradient, where algae bloom only lasts for a short time, when alternative food supply under the ice was high and metabolic activity was triggered by photoperiod. The northern distribution was limited by lack of overwintering habitat for krill larvae due to short duration of sea ice cover even for high food content under the ice. The variability of the krill's length-frequency distributions varied for different triggers of metabolic activity, but did not depend on the sea ice extent. Our findings suggest a southward shift of krill populations due to reduction in the spatial sea ice extent, which is consistent with field observations. Overall, our results highlight the importance of the explicit consideration of spatio-temporal sea ice dynamics especially for larval krill together with temporal synchronization through internal clocks, triggered by environmental factors (photoperiod and food) in adult krill for the population modelling of krill. (C) 2015 Elsevier B.V. All rights reserved.
Current chemical risk assessment procedures may result in imprecise estimates of risk due to sometimes arbitrary simplifying assumptions. As a way to incorporate ecological complexity and improve risk estimates, mechanistic effect models have been recommended. However, effect modeling has not yet been extensively used for regulatory purposes, one of the main reasons being uncertainty about which model type to use to answer specific regulatory questions. We took an individual-based model (IBM), which was developed for risk assessment of soil invertebrates and includes avoidance of highly contaminated areas, and contrasted it with a simpler, more standardized model, based on the generic metapopulation matrix model RAMAS. In the latter the individuals within a sub-population are not treated as separate entities anymore and the spatial resolution is lower. We explored consequences of model aggregation in terms of assessing population-level effects for different spatial distributions of a toxic chemical. For homogeneous contamination of the soil, we found good agreement between the two models, whereas for heterogeneous contamination, at different concentrations and percentages of contaminated area, RAMAS results were alternatively similar to IBM results with and without avoidance, and different food levels. This inconsistency is explained on the basis of behavioral responses that are included in the IBM but not in RAMAS. Overall, RAMAS was less sensitive than the IBM in detecting population-level effects of different spatial patterns of exposure. We conclude that choosing the right model type for risk assessment of chemicals depends on whether or not population-level effects of small-scale heterogeneity in exposure need to be detected. We recommend that if in doubt, both model types should be used and compared. Describing both models following the same standard format, the ODD protocol, makes them equally transparent and understandable. The simpler model helps to build up trust for the more complex model and can be used for more homogeneous exposure patterns. The more complex model helps detecting and understanding the limitations of the simpler model and is needed to ensure ecological realism for more complex exposure scenarios. (C) 2013 Elsevier B.V. All rights reserved.
The importance of a careful choice of the appropriate scale for studying ecological phenomena has been stressed repeatedly. However, issues of spatial scale in metapopulation dynamics received much more attention compared to temporal scale. Moreover, multiple calls were made to carefully choose the appropriate model structure for Population Viability Analysis (PVA). We assessed the effect of using coarser resolution in time and model structure on population dynamics. For this purpose, we compared outcomes of two PVA models differing in their time step: daily individual-based model (dIBM) and yearly stage-based model (ySBM), loaded with empirical data on a well-known metapopulation of the butterfly Boloria eunomia. Both models included the same environmental drivers of population dynamics that were previously identified as being the most important for this species. Under temperature change scenarios, both models yielded the same qualitative scenario ranking, but they quite substantially differed quantitatively with dIBM being more pessimistic in absolute viability measures. We showed that these differences stemmed from inter-individual heterogeneity in dIBM allowing for phenological shifts of individual appearance. We conclude that a finer temporal resolution and an individual-based model structure allow capturing the essential mechanisms necessary to go beyond mere PVA scenario ranking. We encourage researchers to carefully chose the temporal resolution and structure of their model aiming at (1) depicting the processes important for (meta)population dynamics of the species and (2) implementing the environmental change scenarios expected for their study system in the future, using the temporal resolution at which such changes are predicted to operate.