A. Cormont

Improved testing of species traits--environment relationships in the fourth-corner problem.

The fourth-corner problem entails estimation and statistical testing of the relationship between species traits and environmental variables from the analysis of three data tables. In a 2008 paper, S. Dray and P. Legendre proposed and evaluated five permutation methods for statistical significance testing, including a new two-step testing procedure. However, none of these attained the correct type I error in all cases of interest. We solve this problem by showing that a small modification of their two-step procedure controls the type I error in all cases. The modification consists of adjusting the significance level from mean square root of alpha to alpha or, equivalently, of reporting the maximum of the individual P. values as the final one. The test is also applicable to the three-table ordination method RLQ.

Effect of local weather on butterfly flight behaviour, movement, and colonization: significance for dispersal under climate change

Recent climate change is recognized as a main cause of shifts in geographical distributions of species. The impacts of climate change may be aggravated by habitat fragmentation, causing regional or large scale extinctions. However, we propose that climate change also may diminish the effects of fragmentation by enhancing flight behaviour and dispersal of ectothermic species like butterflies. We show that under weather conditions associated with anticipated climate change, behavioural components of dispersal of butterflies are enhanced, and colonization frequencies increase. In a field study, we recorded flight behaviour and mobility of four butterfly species: two habitat generalists (Coenonympha pamphilus; Maniola jurtina) and two specialists (Melitaea athalia; Plebejus argus), under different weather conditions. Flying bout duration generally increased with temperature and decreased with cloudiness. Proportion of time spent flying decreased with cloudiness. Net displacement generally increased with temperature. When butterflies fly longer, start flying more readily and fly over longer distances, we expect dispersal propensity to increase. Monitoring data showed that colonization frequencies moreover increased with temperature and radiation and decreased with cloudiness. Increased dispersal propensity at local scale might therefore lower the impact of habitat fragmentation on the distribution at a regional scale. Synergetic effects of climate change and habitat fragmentation on population dynamics and species distributions might therefore appear to be more complex than previously assumed.

Population dynamics under increasing environmental variability: implications of climate change for ecological network design criteria

There is growing evidence that climate change causes an increase in variation in conditions for plant and animal populations. This increase in variation, e.g. amplified inter-annual variability in temperature and rainfall has population dynamical consequences because it raises the variation in vital demographic rates (survival, reproduction) in these populations. In turn, this amplified environmental variability enlarges population extinction risk. This paper demonstrates that currently used nature conservation policies, principles, and generic and specific design criteria have to be adapted to these new insights. A simulation shows that an increase in variation in vital demographic rates can be compensated for by increasing patch size. A small, short-lived bird species like a warbler that is highly sensitive to environmental fluctuations needs more area for compensation than a large, long-lived bird species like a Bittern. We explore the conservation problems that would arise if patches or reserve sizes would need to be increased, e.g. doubled, in order to compensate for increase in environmental variability. This issue has serious consequences for nature policy when targets are not met, and asks for new design criteria.

Synthesis of ecosystem vulnerability to climate change in the Netherlands shows the need to consider environmental fluctuations in adaptation measures

Climate change impacts on individual species are various and range from shifts in phenology and functional properties to changes in productivity and dispersal. The combination of impacts determines future biodiversity and species composition, but is difficult to evaluate with a single method. Instead, a comparison of mutually independent approaches provides information and confidence in patterns observed beyond what may be achieved in individual approaches. Here, we carried out such comparison to assess which ecosystem types in the Netherlands appear most vulnerable to climate change impacts, as arising from changes in hydrology, nutrient conditions and dispersal limitations. We thus combined meta-analyses of species range shifts with species distribution modelling and ecohydrological modelling with expert knowledge in two respective impact studies. Both impact studies showed that nutrient-poor ecosystems and ecosystem types with fluctuating water tables—like hay meadows, moist heathlands and moorlands—seem to be most at risk upon climate change. A subsequent meta-analysis of species–environmental stress relations indicated that particularly endangered species are adversely affected by the combination of drought and oxygen stress, caused by fluctuating moisture conditions. This implies that adaptation measures should not only aim to optimise mean environmental conditions but should also buffer environmental extremes. Major uncertainties in the assessment included the quantitative impacts of vegetation-hydrology feedbacks, vegetation adaptation and interactions between dispersal capacity and traits linked to environmental selection. Once such quantifications become feasible, adaptation measures may be tailor-made and optimised to conserve vulnerable ecosystem types.

Modelling shifts between mono- and multifunctional farming systems: the importance of social and economic drivers

ContextIn Europe, policy measures are starting to emerge that promote multifunctional farming systems and delivery of ecosystem services besides food production. Effectiveness of these policy instruments have to deal with ecological, economic and social complexities and with complexities in individual decisions of local actors leading to system shifts.ObjectiveThe objective of this paper is to discover the most important social and/or economic drivers that cause farm systems to shift between a monofunctional (providing food) and a multifunctional state (providing food and natural pest regulation).MethodsUsing a cellular automata model, we simulated decisions of individual farmers to shift between a mono-and multifunctional state through time, based on their behaviour type and on financial and social consequences. Collaboration of multifunctional farmers at a landscape scale is a precondition to provide a reliable level of natural pest regulation.ResultsCosts of applying green infrastructure was an important driver for the size and the conversion rate of shifts between mono-and multifunctional farming systems. Shifts towards multifunctional farming were enhanced by a higher motivation of farmers to produce sustainably, while shifts (back) to a monofunctional state was enhanced by a low social cohesion between multifunctional farmers.ConclusionsThese results suggest that in order to develop a multifunctional farming system, individual farmers should act counterintuitively to their conventional farming environment. To maintain a multifunctional farming system, social cohesion between multifunctional farmers is most relevant. Financial aspects are important in both shifts.

Population dynamics of Great Bittern (Botaurus stellaris) in the Netherlands: interaction effects of winter weather and habitat fragmentation

The increased variability in weather as a manifestation of climate change is expected to have negative impacts on population survival in wildlife species, because it will likely lead to increased variation in vital demographic rates (mortality and reproduction) in these populations. For the effective protection of biodiversity, adaptation measures are needed to compensate for the expected increase in weather variability and the negative interaction with habitat fragmentation. As a case study, we studied the fluctuations in Great Bittern numbers (Botaurus stellaris) from 28 monitoring plots scattered over the Netherlands to explore the interaction between the effect of weather and possible remediating effects of the landscape structure. Great Bittern habitat surrounding these plots differs with respect to area, quality, and degree of isolation of this habitat. In western Europe, Great Bitterns are found to be susceptible to continuous loss of suitable habitat due to vegetation succession and fragmentation. Moreover, year-to-year fluctuations in local Great Bittern populations can be caused by severe winter weather or other weather extremes. Our results show that severe winter weather has indeed a significant negative impact on Great Bittern population growth rates. Furthermore, we found that an increased carrying capacity and spatial cohesion (i.e. inverse of habitat fragmentation) contribute to an increase in mean growth rates over the years. As growth rates are higher in large, well-connected habitats, we argue that recovery from negative effects of, e.g. severe winters on Great Bittern population numbers is enhanced in these less-fragmented habitats. We derived generic adaptation measures for enhancing the recovery rate of populations of species in general: one should invest in more large, well-connected nature areas, not only to diminish the negative effects of habitat fragmentation on wildlife populations, but additionally to reduce the impacts of climatic variability.

Improving the applicability and transparency of land use change modelling: The iCLUE model

Abstract Human use of land increasingly alters the structure and the functioning of the environment. To ex-ante understand and anticipate these changes there is an increased need for readily available and operational land use change models. One of these models is CLUE, which has been used in many studies all over the world. These studies brought forward operational hurdles, that hamper model application. The overall objective of this paper is to present a new version of the CLUE model, iCLUE, that helps to overcome these hurdles. We describe the technical redevelopment, conceptual innovations, several applications and success factors and critical reflections. iCLUE minimizes manual error-prone actions, enhances ease-of-use, speeds up the operational modelling process and provides data visualisations to empower users to analyse and interpret results.