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1. For managed temperate forests, conservationists and policymakers favour fine-grained uneven-aged (UEA) management over more traditional coarse-grained even-aged (EA) management, based on the assumption that within-stand habitat heterogeneity enhances biodiversity. There is, however, little empirical evidence to support this assumption. We investigated for the first time how differently grained forest management systems affect the biodiversity of multiple above- and below-ground taxa across spatial scales. 2. We sampled 15 taxa of animals, plants, fungi and bacteria within the largest contiguous beech forest landscape of Germany and classified them into functional groups. Selected forest stands have been managed for more than a century at different spatial grains. The EA (coarse-grained management) and UEA (fine-grained) forests are comparable in spatial arrangement, climate and soil conditions. These were compared to forests of a nearby national park that have been unmanaged for at least 20years. We used diversity accumulation curves to compare -diversity for Hill numbers D-0 (species richness), D-1 (Shannon diversity) and D-2 (Simpson diversity) between the management systems. Beta diversity was quantified as multiple-site dissimilarity. 3. Gamma diversity was higher in EA than in UEA forests for at least one of the three Hill numbers for six taxa (up to 77%), while eight showed no difference. Only bacteria showed the opposite pattern. Higher -diversity in EA forests was also found for forest specialists and saproxylic beetles. 4. Between-stand -diversity was higher in EA than in UEA forests for one-third (all species) and half (forest specialists) of all taxa, driven by environmental heterogeneity between age-classes, while -diversity showed no directional response across taxa or for forest specialists. 5. Synthesis and applications. Comparing EA and uneven-aged forest management in Central European beech forests, our results show that a mosaic of different age-classes is more important for regional biodiversity than high within-stand heterogeneity. We suggest reconsidering the current trend of replacing even-aged management in temperate forests. Instead, the variability of stages and stand structures should be increased to promote landscape-scale biodiversity.
Understanding animal performance in heterogeneous or variable environments is a central question in ecology. We combine modelling and experiments to test how temperature and food availability variance jointly affect life-history traits of ectotherms. The model predicts that as mean temperatures move away from the ectotherm's thermal optimum, the effect size of joint thermal and food variance should become increasingly sensitive to their covariance. Below the thermal optimum, performance should be positively correlated with food–temperature covariance and the opposite is predicted above it. At lower temperatures, covariance should determine whether food and temperature variance increases or decreases performance compared to constant conditions. Somewhat stronger than predicted, the covariance effect below the thermal optimum was confirmed experimentally on an aquatic ectotherm (Daphnia magna) exposed to diurnal food and temperature variance with different amounts of covariance. Our findings have important implications for understanding ectotherm responses to climate-driven alterations of thermal mean and variance.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.