Latitudinal gradients of biodiversity and ecosystem services in protected and non-protected oak forest areas can inform climate smart conservation

Adaptive governance of areas set aside for future protection of biodiversity,sustainable production,and recreation requires knowledge about whether and how effects of area protection are modulated by climate change and redistribution of species.To investigate this,we compare biodiversity of plants(assessed using vegetation plots)and arthropods(collected with Malaise traps,analyzed using metabarcoding)and productivity(tree growth,determined using dendrochronology)in protected and non-protected oak(Quercus spp.)forests along a latitudinal gradient(55.6°N–60.8°N)in Sweden.We also compare historical,recent and projected future climate in the region.In contrast to established global latitudinal diversity gradients,species richness of plants and arthropods increased northwards,possibly reflecting recent climate-induced community redistributions,but neither was higher in protected than in non-protected areas,nor associated with contemporary ground temperature.Species composition of arthropods also did not differ between protected and non-protected areas.Arthropod biomass increased with latitude,suggesting that the magnitude of cascading effects mediated via their roles as pollinators,herbivores,and prey for other trophic levels,varies geographically and will change with a moving climate.Annual growth rate of oaks(an ecosystem service in the form of biomass increase and carbon sequestration)was independent of latitude and did not differ between protected and non-protected areas.Our findings question the efficacy of contemporary designation and management of protected oak forests,and emphasize that development and implementation of modified climate smart conservation strategies is needed to safeguard ecosystem functioning,biodiversity,and recreational values of protected forest areas against future challenges.

Deep learning-based detection of indicator species for monitoring biodiversity in semi-natural grasslands

Deep learning(DL)has huge potential to provide valuable insights into biodiversity changes in speciesrich agricultural ecosystems such as semi-natural grasslands,helping to prioritize and plan conservation efforts.However,DL has been underexplored in grassland conservation efforts,hindered by data scarcity,intricate ecosystem interactions,and limited economic incentives.Here,we developed a DL-based object-detection model to identify indicator species,a group of vascular plant species that serve as surrogates for biodiversity assessment in high nature value(HNV)grasslands.We selected indicator species Armeria maritima,Campanula patula,Cirsium oleraceum,and Daucus carota.To overcome the hurdle of limited data,we grew indicator plants under controlled greenhouse conditions,generating a sufficient dataset for DL model training.The model was initially trained on this greenhouse dataset.Then,smaller datasets derived from an experimental grassland plot and natural grasslands were added to the training to facilitate the transition from greenhouse to field conditions.Our optimized model achieved remarkable average precision(AP)on test datasets,with 98.6 AP50 on greenhouse data,98.2 AP50 on experimental grassland data,and 96.5 AP50 on semi-natural grassland data.Our findings highlight the innovative application of greenhouse-grown specimens for the in-situ identification of plants,bolstering biodiversity monitoring in grassland ecosystems.Furthermore,the study illuminates the promising role of DL techniques in conservation programs,particularly as a monitoring tool to support result-based agrienvironment schemes.

The resilience of pollination interactions: importance of temporal phases

Aims The loss of species that engage in close ecological interactions,such as pollination,has been shown to lead to secondary extinctions,ultimately threatening the overall ecosystem stability and function-ing.Pollination studies are currently flourishing at all possible levels of interaction organization(i.e.,species,guild,group and network),and different methodological protocols aimed to define the resil-ience of pollination interactions have been proposed.However,the temporal dimension of the resilience of pollination interactions has been often overlooked.In the light of these considerations,we addressed the following questions:does a temporal approach help to reveal critical moments during the flowering season,when polli-nation interactions are less resilient to perturbations?Do pollination interactions evaluated at species,guild,group and network level show different patterns when assessed through time?Methods We monitored contacts between plant and pollinator species in dry grassland communities every 15 days during the overall community flowering season(12 surveys).For each survey,we built a quantita-tive plant-pollinator interaction matrix and we calculated two sets of metrics characterizing,respectively,the diversity and the distribution of interactions across hierarchical levels.To describe the diversity of interactions,we calculated partner diversity(PD)at the species level,vulnerability/generality(V/G)at the guild level,and interaction diver-sity and evenness at the network level.The distribution of interactions was characterized by calculating selectiveness at the species and the network level,and modularity at the group level.We assessed the temporal variation of PD,V/G at the level of plants and pollinators,and species selectiveness,by means of Linear Mixed Models(LMMs).To investigate the temporal variation of indexes calculated at group and network level,we applied simple linear and quadratic regres-sions after checking for temporal autocorrelation in residuals.Important Findings When taking into account the temporal dimension of interactions,the diversity of interactions showed different patterns at different levels of organization.At the species level,no relationship was dis-closed between PD and time,when assessing the temporal trend of V/G separately for the guild of plants and pollinators we observed an asymmetric structure of interactions.Pollination interactions showed to be asymmetric throughout the flowering season;how-ever,evenness of interactions and network selectiveness showed significant positive relationships with time,revealing a poorer net-work of interactions during the end of the flowering season.The temporal analysis of pollination interactions revealed a stronger risk of secondary extinctions at the end of the flowering season,due to a lower degree of redundancy and thus of resilience of the overall network of interactions.