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.
Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers – a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation – which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models.
Viability analyses of large metapopulations are often hampered by difficulties in the parameter estimation. This leads to high uncertainty in parameter values and model outputs and complicates the formulation of clear recommendations for conservation management. We present a comprehensive procedure that is able to process spatiotemporal patterns of metapopulation occupancy to rank management scenarios. The first step of the procedure involves the formulation of the stochastic metapopulation model and the estimation of parameter values with a Bayesian approach, using a Markov chain Monte Carlo algorithm. In the second step, the model is used to predict the effects of different management actions, taking into account the uncertainty in the parameter estimates. Finally, in the third step, decision analysis is used to evaluate and aggregate the results of the previous step into a simple rank order of management scenarios. The procedure was applied to a metapopulation of the Glanville fritillary, Melitaea cinxia. Although the amount of available occupancy data was considerable, the uncertainty in the estimated model parameter values was so large that a precise estimate of the extinction risk of the metapopulation could not be made. However, the procedure was able to produce a rank order of management scenarios that was extraordinarily robust to the uncertainty. Application of the procedure to two other case studies revealed that, even though robust rankings cannot always be obtained, the results of the procedure are helpful in assessing the degree of uncertainty in the ranking and pointing to those factors most responsible for the lack of robustness. The results of this paper demonstrate very clearly, by way of example, both the limitations and the possibilities of model-based metapopulation viability analysis.
There is an increased interest in the effects of hunting on the genetic structure of populations, and the distribution of important phenotypes, such as size and horn growth (e.g. [Coltman et al. 2003][1]; [Kuparinen & Merila 2007][2]). This raises questions about how to maintain hunted populations
Background: Dramatic reductions in early-spring Calluna vulgaris moisture content have been linked to extreme fire hazard and plant die-back. Aims: To investigate spatial and temporal variation in the fuel moisture content of Calluna vulgaris. Methods: Calluna vulgaris plants were sampled in different sites and seasons to examine vertical profiles in moisture content. Live moisture content was monitored throughout autumn 2003 and spring 2004. Changes were compared to trends in temperature, soil resistance and rainfall. The effect of exposure was examined by comparing shoot moisture content in sheltered and exposed locations. Results: Significant spatial and temporal variation in moisture content was observed. In spring rapid fluctuations in moisture coincided with periods of dry weather, low temperatures and frozen ground. Shoots from exposed locations had significantly lower moisture content when the ground was frozen. Conclusions: Significant declines in the live fuel moisture content of Calluna vulgaris are associated with physiological drought caused by cold, clear conditions and frozen ground. Over-winter damage to leaf cuticles reduces the ability of the plant to regulate water loss. Changes in moisture content can be rapid, and managers should be aware of the potential for extreme fire behaviour.
Citizen science and automated collection methods increasingly depend on image recognition to provide the amounts of observational data research and management needs. Recognition models, meanwhile, also require large amounts of data from these sources, creating a feedback loop between the methods and tools. Species that are harder to recognize, both for humans and machine learning algorithms, are likely to be under-reported, and thus be less prevalent in the training data. As a result, the feedback loop may hamper training mostly for species that already pose the greatest challenge. In this study, we trained recognition models for various taxa, and found evidence for a 'recognizability bias', where species that are more readily identified by humans and recognition models alike are more prevalent in the available image data. This pattern is present across multiple taxa, and does not appear to relate to differences in picture quality, biological traits or data collection metrics other than recognizability. This has implications for the expected performance of future models trained with more data, including such challenging species.
Abstract Dynamic vegetation models provide process‐based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.
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