Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics

Predictive population models designed to assist managers and policy makers require an explicit treatment of inherent uncertainty and variability. These are particular concerns when modelling non-native and reintroduced species, when data have been collected within one geographical or ecological context but predictions are required for another, or when extending models to predict the consequences of environmental change (e.g., climate or land-use). We present an aspatial, probabilistic framework of hierarchical process models for predicting population growth even when data are sparse or of poor quality. Insight into the factors affecting population dynamics in real landscapes can be provided and Kullback–Leibler distances are used to compare the relative output of models. This flexible yet robust framework gives easily interpretable results, allowing managers as well as modellers to invalidate anomalous models and apply others to real-life scenarios.We illustrate the framework’s power with a meta-analysis of European wild boar (Sus scrofa) data. We test hypotheses about the effect of geographic region, hunting and mast years on wild boar population growth, to build models of wild boar dynamics for the UK. The framework quantifies the importance of hunting pressure as a driver of population growth, and confirms that reproductive success is greatly decreased in poor mast years, suggesting that the key to predicting wild boar dynamics is to ascertain local hunting pressure and to better understand changing food availability. Geography had no significant effect, indicating that it is not a good proxy for modelling the impact of change in climate or land-use on wild boar populations at the European scale. We use the framework to predict population abundance 9 years after an isolated population of wild boar established in the UK; in a comparison with the only field data and two independent modelling exercises, our framework provides the most robust and informative results.     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.     

Global scale emission and distribution of sea-spray aerosol: Sea-salt and organic enrichment

The chemical composition of marine aerosols as a function of their size is an important parameter for the evaluation of their impact on the global climate system. In this work we model fine particle organic matter emitted by sea spray processes and its influence on the aerosol chemical properties at the global scale using the off-line global Chemistry-Transport Model TM5. TM5 is coupled to a microphysical aerosol dynamics model providing size resolved information on particle masses and numbers. The mass of the emitted sea spray particles is partitioned between water insoluble organic matter (WIOM) and sea salt components in the accumulation mode using a function that relates the emitted organic fraction to the surface ocean chlorophyll-a concentrations. The global emission in the sub-micron size range of organic matter by sea spray process is 8.2 Tg yr^?1, compared to 24 Tg fine yr^?1 sea-salt emissions. When the marine sources are included, the concentrations of modelled primary particulate organic matter (POM) increase mainly over the oceans. The model predictions of WIOM and sea salt are evaluated against measurements carried out at Mace Head (Northern Hemisphere) and Amsterdam Island (Southern Hemisphere), showing that in clean marine conditions WIOM marine emissions contribute significantly to POM values.     

Chemical fluxes in time through forest ecosystems in the UK - Soil response to pollution recovery

Long term trend analysis of bulk precipitation, throughfall and soil solution elemental fluxes from 12 years monitoring at 10 ICP Level II forest sites in the UK reveal coherent national chemical trends indicating recovery from sulphur deposition and acidification. Soil solution pH increased and sulphate and aluminium decreased at most sites. Trends in nitrogen were variable and dependant on its form. Dissolved organic nitrogen increased in bulk precipitation, throughfall and soil solution at most sites. Nitrate in soil solution declined at sites receiving high nitrogen deposition. Increase in soil dissolved organic carbon was detected - a response to pollution recovery, changes in soil temperature and/or increased microbial activity. An increase of sodium and chloride was evident - a possible result of more frequent storm events at exposed sites. The intensive and integrated nature of monitoring enables the relationships between climate/pollutant exposure and chemical/biological response in forestry to be explored.     

Invasion versus Isolation: Trade-Offs in Managing Native Salmonids with Barriers to Upstream Movement

Abstract: Conservation biologists often face the trade‐off that increasing connectivity in fragmented landscapes to reduce extinction risk of native species can foster invasion by non‐native species that enter via the corridors created, which can then increase extinction risk. This dilemma is acute for stream fishes, especially native salmonids, because their populations are frequently relegated to fragments of headwater habitat threatened by invasion from downstream by 3 cosmopolitan non‐native salmonids. Managers often block these upstream invasions with movement barriers, but isolation of native salmonids in small headwater streams can increase the threat of local extinction. We propose a conceptual framework to address this worldwide problem that focuses on 4 main questions. First, are populations of conservation value present (considering evolutionary legacies, ecological functions, and socioeconomic benefits as distinct values)? Second, are populations vulnerable to invasion and displacement by non‐native salmonids? Third, would these populations be threatened with local extinction if isolated with barriers? And, fourth, how should management be prioritized among multiple populations? We also developed a conceptual model of the joint trade‐off of invasion and isolation threats that considers the opportunities for managers to make strategic decisions. We illustrated use of this framework in an analysis of the invasion‐isolation trade‐off for native cutthroat trout (Oncorhynchus clarkii) in 2 contrasting basins in western North America where invasion and isolation are either present and strong or farther away and apparently weak. These cases demonstrate that decisions to install or remove barriers to conserve native salmonids are often complex and depend on conservation values, environmental context (which influences the threat of invasion and isolation), and additional socioeconomic factors. Explicit analysis with tools such as those we propose can help managers make sound decisions in such complex circumstances.