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ABSTRACT: Temporal variation in the detectability of a species can bias estimates of relative abundance if not handled correctly. For example, when effort varies in space and/or time it becomes necessary to take variation in detectability into account when data are analyzed. We demonstrate the importance of incorporating seasonality into the analysis of data with unequal sample sizes due to lost traps at a particular density of a species. A case study of count data was simulated using a spring-active carabid beetle. Traps were 'lost' randomly during high beetle activity in high abundance sites and during low beetle activity in low abundance sites. Five different models were fitted to datasets with different levels of loss. If sample sizes were unequal and a seasonality variable was not included in models that assumed the number of individuals was log-normally distributed, the models severely under- or overestimated the true effect size. Results did not improve when seasonality and number of trapping days were included in these models as offset terms, but only performed well when the response variable was specified as following a negative binomial distribution. Finally, if seasonal variation of a species is unknown, which is often the case, seasonality can be added as a free factor, resulting in well-performing negative binomial models. Based on these results we recommend (a) add sampling effort (number of trapping days in our example) to the models as an offset term, (b) if precise information is available on seasonal variation in detectability of a study object, add seasonality to the models as an offset term; (c) if information on seasonal variation in detectability is inadequate, add seasonality as a free factor; and (d) specify the response variable of count data as following a negative binomial or over-dispersed Poisson distribution.
PLoS ONE 01/2012; 7(7):e40923. · 4.09 Impact Factor
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ABSTRACT: 1. In the prevailing context of concerns over climate change and its potential impacts on ecosystems, evaluating ecological consequences of climatic forcing has become a critical issue. 2. Historical data on the abundance of organisms have been extensively used to characterize the ecological effects of climatic forcing through specific weather and/or climatic variables, with most of the studies confined to single population models. 3. However, population responses to environmental fluctuations typically depend upon positive and negative feedbacks induced by interactions with other species. It is therefore important to integrate the insights gained from single population approaches into a multispecies perspective. 4. Here we combine the hierarchical Bayesian modelling approach with the state-space formulation to extend the scope of previously proposed models of population dynamics under climatic forcing to multi-species systems. 5. We use our model to analyse long-term macro-moth (Lepidoptera) community data from the Rothamsted Insect Survey network in the UK, using winter rainfall and winter temperature as environmental covariates. 6. The effects of the two weather variables were consistent across species, being negative for winter rainfall and positive for winter temperature. The two weather variables jointly explained 15-40% of the total environmental variation affecting the dynamics of individual species, and could explain up to 90% of covariances in species dynamics. 7. The contribution of interspecific interactions to community-level variation was found to be weak compared to the contributions of environmental forcing and intraspecific interactions.
Journal of Animal Ecology 01/2011; 80(1):101-7. · 4.94 Impact Factor
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ABSTRACT: Elucidating the mechanisms underlying the assembly and dynamics of ecological communities is a fundamental goal of ecology. Two conceptual approaches have emerged in this respect: the niche-assembly view and the neutral perspective. The debate as to which approach best explains the biodiversity patterns observed in nature is becoming outdated, as ecologists increasingly agree on the existence of a niche-neutral continuum of community dynamical behaviors. However, attempts to make the continuum idea operational and measurable remain sparse. Here, we propose a model-based approach to achieving this. The proposed methodology consists of separating out fluctuations in species abundances into niche-mediated and stochastic factors, linking the niche configuration to community dynamics through competition, and adding demographic stochasticity. This results in a comprehensive framework including neutrality and strict niche segregation as extreme cases. We develop an index of departure from neutral drift as a surrogate for community position on the niche-neutral continuum. We evaluate the performance of our modeling approach with simulated data, and subsequently use the model to analyze rodent web-trapping data from a real-world system. The model fitting is carried out with a Bayesian approach using Markov chain Monte Carlo simulation methods.
Oecologia 11/2010; 166(1):241-51. · 3.41 Impact Factor
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ABSTRACT: We investigated the effects of human trampling on boreal forest understory vegetation on, and off paths from suburban forest edges towards the interiors and on the likelihood of trampling-aided dispersal into the forests for three years by carrying out a trampling experiment. We showed that the vegetation was highly sensitive to trampling. Even low levels of trampling considerably decreased covers of the most abundant species on the paths. Cover decreased between 10 and 30% on paths which had been trampled 35 times, and at least by 50% on those trampled 70-270 times. On-path vegetation cover decreased similarly at forest edges and in the interiors. However, some open habitat plant species that occurred outside the forest patches and at forest edges dispersed into the forests, possibly through the action of trampling. A higher cover percentage of an open habitat species at the forest edge line increased its probability to disperse into the forest interior. The vegetation community on, next to, and away from lightly trampled paths remained the same throughout the trampling experiment. For heavily trampled paths, the community changed drastically on the paths, but stayed relatively similar next to and away from the paths. As boreal vegetation is highly sensitive to the effects of trampling, overall ease of access throughout the forest floor should be restricted to avoid the excessive creation of spontaneous paths. To minimize the effects of trampling, recreational use could be guided to the maintained path network in heavily used areas.
Journal of Environmental Management 04/2010; 91(9):1811-20. · 3.24 Impact Factor
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ABSTRACT: Environmental noise is ubiquitous in population growth processes, with a well acknowledged potential to affect populations regardless of their sizes. It therefore deserves consideration in population dynamics modelling. The usual approach to incorporating noise into population dynamical models is to make some model parameter(s) (typically the growth rate, the carrying capacity, or both) stochastic and responsive to environment fluctuations. It is however still unclear whether including noise in one or/and another parameter makes a difference to the model performance. Here we investigated this issue with a focus on model fit and predictive accuracy. To do this, we developed three population dynamical models of the Ricker type with the noise included in the growth rate (Model 1), in the carrying capacity (Model 2), and in both (Model 3). We generated several population time series under each model, and used a Bayesian approach to fit the three models to the simulated data. We then compared the model performances in fitting to the data and in forecasting future observations.
When the mean intrinsic growth rate, r, in the data was low, the three models had roughly comparable performances, irrespective of the true model and the level of noise. As r increased, Models 1 performed best on data generated from it, and Model 3 tended to perform best on data generated from either Models 2 or Model 3. Model 2 was uniformly outcompeted by the other two models, regardless of the true model and the level of noise. The correlation between the deviance information criterion (DIC) and the mean square error (MSE) used respectively as measure of fit and predictive accuracy was broadly positive.
Our results suggested that the way environmental noise is incorporated into a population dynamical model may profoundly affect its performance. Overall, we found that including noise in one or/and another parameter does not matter as long as the mean intrinsic growth rate, r, is low. As r increased, however, the three models performed differently. Models 1 and 3 broadly outperformed Model 2, the first having the advantage of being simple and more computationally tractable. A comforting result emerging from our analysis is the broad positive correlation between MSEs and DICs, suggesting that the latter may also be informative about the predictive performance of a model.
BMC Ecology 03/2010; 10:7.
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ABSTRACT: Recent studies, which have found evidence for kin-biased egg donation, have sparked interest in re-assessing the parasitic nature of conspecific brood parasitism (CBP). Since host-parasite kinship is essential for mutual benefits to arise from CBP, we explored the role of relatedness in determining the behaviour of conspecific nest parasites and their hosts in nesting female Barrow's goldeneyes (Bucephala islandica), a duck in which CBP is common. The results revealed that the amount of parasitism increased with host-parasite relatedness, the effect of which was independent of geographical proximity of host and parasite nests. Proximity per se was also positively associated with the amount of parasitism. Furthermore, while hosts appeared to reduce their clutch size as a response to the presence of parasitic eggs, the magnitude of host clutch reduction also tended to increase with increasing relatedness to the parasite. Hence, our results indicate that both relatedness and spatial proximity are important determinants of CBP, and that host clutch reduction may be an adaptation to nest parasitism, modulated by host-parasite relatedness. Taken together, the results provide a demonstration that relatedness influences host and parasite behaviour in Barrow's goldeneyes, resulting in kin-biased egg donation.
Molecular Ecology 06/2009; 18(12):2713-21. · 5.52 Impact Factor
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ABSTRACT: The search for general mechanisms of community assembly is a major focus of community ecology. The common practice so far has been to examine alternative assembly theories using dichotomist approaches of the form neutrality versus niche, or compensatory dynamics versus environmental forcing. In reality, all these mechanisms will be operating, albeit with different strengths. While there have been different approaches to community structure and dynamics, including neutrality and niche differentiation, less work has gone into separating out the temporal variation in species abundances into relative contributions from different components. Here we use a refined statistical machinery to decompose temporal fluctuations in species abundances into contributions from environmental stochasticity and inter-/intraspecific interactions, to see which ones dominate. We apply the methodology to community data from a range of taxa. Our results show that communities are largely driven by environmental fluctuations, and that member populations are, to different extents, regulated through intraspecific interactions, the effects of interspecific interactions remaining broadly minor. By decomposing the temporal variation in this way, we have been able to show directly what has been previously inferred indirectly: compensatory dynamics are in fact largely outweighed by environmental forcing, and the latter tends to synchronize the population dynamics.
Proceedings of the Royal Society B: Biological Sciences 06/2009; 276(1669):2923-9. · 5.41 Impact Factor
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ABSTRACT: Probabilistic reaction norms (PRNs) are an extension of the concept of reaction norms, developed to account for stochasticity in ontogenetic transitions. However, logistic regression based PRNs are restricted to discrete time intervals, whereas previously proposed models for continuous transitions are demanding in terms of modelling effort and data needed.
Here we introduce two alternative approaches for the probabilistic modelling of continuous ontogenetic transitions. The models are simplified in their description of forces underlying transitions, thus being empirical rather than mechanistic by their nature, but therefore applicable to situations where data and prior knowledge of transitions are limited. The models provide continuous time description of the transition pattern, insights into how it is affected by covariates, at the same time allowing for fine scale transition probability predictions. Performance of the models is demonstrated using empirical data on metamorphosis in common frogs (Rana temporaria) reared in a common garden experiment.
As being user-friendly and methodologically easily accessible, the models introduced in this study aid the concept of probabilistic reaction norms becoming as general and applicable tool in the studies of life-history variation as the deterministic reaction norms are today.
PLoS ONE 02/2008; 3(11):e3677. · 4.09 Impact Factor
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ABSTRACT: A great deal of variation in ecological processes can be generated through variation in environmental conditions. Models describing the underlying processes should be able to account for this variation. However, models may not be flexible enough, so that different realizations of a process may be better described by different models. This may lead to uncertainty in model selection.Here we examine the question of whether two empirical models can provide consistent fits to different realizations of a process affected by environmental variation. We further examine the sensitivity of the model predictions to the amount of data available and the selection of the model. To study this, we simulated pollen dispersal patterns under varying wind conditions and then investigated whether the datasets consistently supported the same model. The role of the model selection and the impact of the spatial range over which the dispersal distances were observed were assessed by comparing model predictions at long dispersal distances.There was no consistent pattern of one model providing a better fit than the other across simulations. The model providing better fit varied depending on the range of distances over which the dispersal patterns were observed, and on the amount of long-distance dispersal. The model predictions were found to be very sensitive to the selection of the model.The variation between datasets produced with the same underlying mechanisms cannot be easily described using one model, which also limits our ability to reliably predict the underlying process. Therefore, the amount of information about a model choice provided by an individual field study may be rather limited. If we are to understand processes that are affected by environmental variation then we have to observe the range of possible outcomes of the processes under varying spatio-temporal conditions.
Oikos 05/2007; 116(6):966 - 974. · 3.06 Impact Factor
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ABSTRACT: Mechanistic and phenomenological dispersal modelling of organisms has long been an area of intensive research. Recently, there has been an increased interest in intermediate models between the two. Intermediate models include major mechanisms that affect dispersal, in addition to the dispersal curve of a phenomenological model. Here we review and describe the mathematical and statistical framework for phenomenological dispersal modelling. In the mathematical development we describe modelling of dispersal in two dimensions from a point source, and in one dimension from a line or area source. In the statistical development we describe applicable observation distributions, and the procedures of model fitting, comparison, checking, and prediction. The procedures are also demonstrated using data from dispersal experiments. The data are hierarchically structured, and hence, we fit hierarchical models. The Bayesian modelling approach is applied, which allows us to show the uncertainty in the parameter estimates and in predictions. Finally, we show how to account for the effect of wind speed on the estimates of the dispersal parameters. This serves as an example of how to strengthen the coupling in the modelling between the phenomenon observed in an experiment and the underlying process – something that should be striven for in the statistical modelling of dispersal.
Oikos 05/2007; 116(6):1037 - 1050. · 3.06 Impact Factor
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ABSTRACT: Tools for estimating pollen dispersal and the resulting gene flow are necessary to assess the risk of gene flow from genetically modified (GM) to conventional fields, and to quantify the effectiveness of measures that may prevent such gene flow. A mechanistic simulation model is presented and used to simulate pollen dispersal by wind in different agricultural scenarios over realistic pollination periods. The relative importance of landscape-related variables such as isolation distance, topography, spatial configuration of the fields, GM field size and barrier, and environmental variation are examined in order to find ways to minimize gene flow and to detect possible risk factors. The simulations demonstrated a large variation in pollen dispersal and in the predicted amount of contamination between different pollination periods. This was largely due to variation in vertical wind. As this variation in wind conditions is difficult to control through management measures, it should be carefully considered when estimating the risk of gene flow from GM crops. On average, the predicted level of gene flow decreased with increasing isolation distance and with increasing depth of the conventional field, and increased with increasing GM field size. Therefore, at a national scale and over the long term these landscape properties should be accounted for when setting regulations for controlling gene flow. However, at the level of an individual field the level of gene flow may be dominated by uncontrollable variation. Due to the sensitivity of pollen dispersal to the wind, we conclude that gene flow cannot be summarized only by the mean contamination; information about the frequency of extreme events should also be considered. The modeling approach described in this paper offers a way to predict and compare pollen dispersal and gene flow in varying environmental conditions, and to assess the effectiveness of different management measures.
Ecological Applications 04/2007; 17(2):431-40. · 5.10 Impact Factor
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ABSTRACT: We investigated some of the causes of ground beetle decline using atlas data from Belgium, Denmark and the Netherlands, countries in which natural environments have all but disappeared. We used ordinal regression to identify characteristics that are significantly correlated with the decline of carabid beetle species over the last 50-100 years, using a stepwise selection procedure to select the optimal model according to the Akaike Information Criterion. The results showed that large-bodied carabid populations have declined more than smaller ones, possibly because of their lower reproductive output and lower powers of dispersal. Habitat specialist populations (i.e. species with small niche breadths) have also decreased more than habitat generalist populations. Species with both long- and short-winged individuals have been less prone to decline than those that are exclusively either short-winged or long-winged. Dimorphic species may survive better in highly altered environments because long-winged individuals are good at dispersing between suitable habitats and short-winged individuals are good at surviving and reproducing in these newly colonised habitats. Finally, populations of large carabids associated with coastal, woodland or riparian habitat types were less prone to decline than populations of large carabids associated with various, open or grassland habitat types. The pattern is reversed for carabid species smaller than 8 mm in size. These results are explained in the context of habitat restoration and destruction in these highly modified western European countries.
Oecologia 04/2003; 135(1):138-48. · 3.41 Impact Factor
