Changes in climate and land use are major threats to pollinating insects, an essential functional group. Here, we unravel the largely unknown interactive effects of both threats on seven pollinator taxa using a multiscale space-for-time approach across large climate and land-use gradients in a temperate region. Pollinator community composition, regional gamma diversity, and community dissimilarity (beta diversity) of pollinator taxa were shaped by climate-land-use interactions, while local alpha diversity was solely explained by their additive effects. Pollinator diversity increased with reduced land-use intensity (forest < grassland < arable land < urban) and high flowering-plant diversity at different spatial scales, and higher temperatures homogenized pollinator communities across regions. Our study reveals declines in pollinator diversity with land-use intensity at multiple spatial scales and regional community homogenization in warmer and drier climates. Management options at several scales are highlighted to mitigate impacts of climate change on pollinators and their ecosystem services.

Contemporary climate change leads to earlier spring phenological events in Europe. In forests, in which overstory strongly regulates the microclimate beneath, it is not clear if further change equally shifts the timing of leaf unfolding for the over- and understory of main deciduous forest species, such as Fagus sylvatica L. (European beech). Furthermore, it is not known yet how this vertical phenological (mis)match—the phenological difference between overstory and understory—affects the remotely sensed satellite signal. To investigate this, we disentangled the start of season (SOS) of overstory F.sylvatica foliage from understory F. sylvatica foliage in forests, within nine quadrants of 5.8 × 5.8 km, stratified over a temperature gradient of 2.5 °C in Bavaria, southeast Germany, in the spring seasons of 2019 and 2020 using time lapse cameras and visual ground observations. We explained SOS dates and vertical phenological (mis)match by canopy temperature and compared these to Sentinel-2 derived SOS in response to canopy temperature. We found that overstory SOS advanced with higher mean April canopy temperature (visual ground observations: −2.86 days per °C; cameras: −2.57 days per °C). However, understory SOS was not significantly affected by canopy temperature. This led to an increase of vertical phenological mismatch with increased canopy temperature (visual ground observations: +3.90 days per °C; cameras: +2.52 days per °C). These results matched Sentinel-2-derived SOS responses, as pixels of higher canopy height advanced more by increased canopy temperature than pixels of lower canopy height. The results may indicate that, with further climate change, spring phenology of F. sylvatica overstory will advance more than F. sylvatica understory, leading to increased vertical phenological mismatch in temperate deciduous forests. This may have major ecological effects, but also methodological consequences for the field of remote sensing, as what the signal senses highly depends on the pixel mean canopy height and the vertical (mis)match.

European roe deer (Capreolus capreolus L.) are important given their economic, recreational and ecological value. However, uncontrolled roe deer numbers can result in negative impacts on forest regeneration and agricultural crops, disease transmission and occurrences of deer-vehicle collisions. Information on the abundance and distribution is needed for effective management. We combined distance sampling (DS) of roe deer dung pellet groups with multiple variables to develop a density surface model (DSM) in the federal state of Bavaria in south-eastern Germany. We used the estimates of pellet group density as a proxy for roe deer relative abundance. We extrapolated our best DSM, conducted a quantitative evaluation and contrasted relative abundance along climate and land-use gradients. Relative abundance of roe deer was influenced by a combination of habitat type, climate and wildlife management variables, which differed between seasons and which reflected changes in food and shelter availability. At the landscape scale, the highest abundance was observed in agriculture-dominated areas and the lowest in urban areas. Higher abundance was also observed in areas with intermediate temperatures compared to the warmest areas. Our results provide information on possible future changes in the distribution of relative abundance due to changes in climate and land-use.

Urbanization and agricultural intensification are considered the main causes of recent insect decline in temperate Europe, while direct climate warming effects are still ambiguous. Nonetheless, higher temperatures advance spring leaf emergence, which in turn may directly or indirectly affect insects. We therefore investigated how Sentinel-2-derived start of season (SOS) and its spatial variability (SV-SOS) are affected by spring temperature and whether these green-up variables can explain insect biomass and richness across a climate and land-use gradient in southern Germany. We found that the effects of both spring green-up variables on insect biomass and richness differed between land-use types, but were strongest in forests. Here, insect richness and biomass were higher with later green-up (SOS) and higher SV-SOS. In turn, higher spring temperatures advanced SOS, while SV-SOS was lower at warmer sites. We conclude that with a warming climate, insect biomass and richness in forests may be affected negatively due to earlier and more uniform green-up. Promising adaptation strategies should therefore focus on spatial variability in green-up in forests, thus plant species and structural diversity.

Climate and land-use change are major drivers of insect decline, yet their interactive effects on insect richness and abundance, especially across trophic levels, remain poorly understood. Here, we investigate how temperature and land use shape insect communities across spatial scales and trophic levels, from flowering plants and cavity-nesting bees to hunting wasps, their antagonists and parasitism rates. Using trap nests and a space-for-time approach, we surveyed 179 plots spanning four habitat types (forest, grassland, arable land and settlements) across 60 study regions in Germany covering semi-natural, agricultural and urban landscapes. Bee richness and abundance responded to climate–land-use interactions across spatial scales, being higher with warmer local daytime temperatures and overall warmer climates, but only in less intensive land uses. In contrast, elevated night-time temperatures negatively affected bees. Higher trophic levels benefited more consistently from warmer climates than lower trophic levels and were less affected by high local daytime and night-time temperatures. Parasitism rates were lowest in arable land but similar across habitats within semi-natural regions, suggesting that landscape-scale processes buffer local effects. Our findings underscore the importance of considering night-time temperatures for diurnal insects and suggest that rising temperatures may exacerbate the negative impacts of land use on pollinators.

Using RGB camera data (e.g. webcams, wildlife cameras) has great potential to measure forest phenology over climate gradients, because of its very high temporal resolution, while at the same time being more objective and less time consuming than in situ observations. To make images useful for the purpose of measuring phenological events, such as Start of Season (SOS) and End of Season (EOS), there is need to derive Regions of Interest (ROI) objectively and (semi-)automatically. In order to answer this need, Bothmann et al. (2017) proposed a method which randomly sets a number of pinpricks in the image and calculates how greenness over time from all other pixels correlates to these different pinpricks. Subsequently, ROIs are created by discarding the pixels with low correlation, using multiple thresholds. Despite its advantage of being automated and more objective compared to prevailing expert-based ROIs, and therefore its potential applicability for phenological research using a large amount of cameras, the method has not been reproduced for this purpose so far. Therefore, we assess here how well this method is able to separate foliage of different deciduous species from evergreens and phenologically irrelevant components in time-lapse wildlife camera data and in that way how suitable it is in explaining variation in phenology over a temperature gradient. We used 73 Cuddleback wildlife cameras troughout Bavaria which were installed within nine quadrants of 6*6 kilometers spanning a temperature gradient of 2.5°C. Hourly taken images of deciduous forests in spring, summer and autumn 2019 were analysed. Half of them were facing canopy, and half of them were facing understory. We applied the principles of the method from Bothmann et al. (2017) and assigned the best matching ROI to foliage of Fagus sylvatica or other deciduous species. Within this ROI, mean Green Chromatic Coordinate (GCC), a greenness index, over all pixels within the ROI, was derived per time-stamp. Afterwards, a time-series was calculated on these GCC values and with a suitable combination of curve-fitting techniques, SOS and EOS were derived, expressed in Day of Year (DOY). We compared these SOS and EOS dates with weekly in situ observations of spring and autumn phenology, which were taken in the same quadrants. Despite that Bothmann's method was developed on a single tower-mounted scientific webcam which viewed on canopy from above, while we made use of wildlife cameras at 73 different locations facing either understory perpendicular or canopy from below, it was able to distinguish F. sylvatica and other deciduous foliage from phenologically less relevant information. Time-series derived from these ROIs were able to explain variability in phenology between understory and canopy and over the temperature gradient similarly and supplementary to in situ observations.