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Biology subjects

Lars Dalby

Publications and source records attributed to Lars Dalby.

2 recordsLinked to original sources

Dark diversity illuminates the dim side of conservation and restoration

1 Dark diversity is a promising concept for prioritizing management efforts as it focuses on missing species, i.e., species present in the regional pool, but locally absent despite suitable environmental conditions. 2 We applied the concept to a massive national plant diversity database (236,923 records from 15,160 surveys involving 564 species) to provide the first geographically comprehensive assessment of dark diversity across a large area (43,000 km2), at a spatial scale (~75 m2) relevant for conservation and restoration planning and across multiple terrestrial habitats, thus maximising its practical application potential. The likelihood for a given plant species to belong to the dark diversity pool was computed and logistically regressed against its ecological preferences (nutrient availability, pH etc.), strategies (competitor, stress tolerance, ruderal), mycorrhizal dependence and infection percentage, seed mass and maximum dispersal distance. 3 Forty-six percent of the species were absent in >95 % of the suitable sites, whereas 7 % of the species were absent in less than 60 % of sites that were deemed suitable. 4 Species that were more likely to belong to the dark diversity tended to depend on mycorrhiza, were mostly adapted to low light and nutrient levels, had poor dispersal abilities, were ruderals and had a low stress tolerance. Synthesis and applications Our findings have important implications for the planning and management of natural ecosystems requiring detailed knowledge of what triggers the presence/absence of individual plant species in a seemingly suitable habitat. We conclude that practitioners may need to carefully consider mycorrhizal inoculations with a suitable assemblage of fungi for certain plant species to become established. Also assisted migration might be necessary to help poor dispersers although spatial and temporal processes are also important to have in mind. Finally, it is important to vary nutrient loads making room for plant species to colonise both nutrient-poor and nutrient-rich localities.

Ecology

Landscape structure and management alter the outcome of a pesticide ERA: evaluating impacts of endocrine disruption using the ALMaSS European Brown Hare model

There is a gradual change towards explicitly considering landscapes in regulatory risk assessment. To realise the objective of developing representative scenarios for risk assessment it is necessary to know how detailed a landscape representation is needed to generate a realistic risk assessment, and indeed how to generate such landscapes. This paper evaluates the contribution of landscape and farming components to a model based risk assessment of a fictitious endocrine disruptor on hares. In addition, we present methods and code examples for generation of landscape structures and farming simulation from data collected primarily for EU agricultural subsidy support and GIS map data.\n\nTen different Danish landscapes were generated and the ERA carried out for each landscape using two different assumed toxicities. The results showed negative impacts in all cases, but the extent and form in terms of impacts on abundance or occupancy differed greatly between landscapes. A meta-model was created, predicting impact from landscape and farming characteristics. Scenarios based on all combinations of farming and landscape for five landscapes representing extreme and middle impacts were created. The meta-models developed from the 10 real landscapes failed to predict impacts for these 25 scenarios. Landscape, farming, and the emergent density of hares all influenced the results of the risk assessment considerably.\n\nThe study indicates that prediction of a reasonable worst case scenario is difficult from structural, farming or population metrics; rather the emergent properties generated from interactions between landscape, management and ecology are needed. Meta-modelling may also fail to predict impacts, even when restricting inputs to combinations of those used to create the model. Future ERA may therefore need to make use of multiple scenarios representing a wide range of conditions to avoid locally unacceptable risks. This approach could now be feasible Europe wide given the landscape generation methods presented.

Systems Biology