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Traditional orchards are distinctive features of cultural landscapes in Central Europe. Despite their high level of ecological importance, they are in decline, and comprehensive spatial data over broad extents, which could enable a trend analysis, are lacking. We analysed traditional orchard maps from 1952 to 1967 and a map from 2010, generated via aerial image interpretation, for the state of Hesse (ca. 21,115 km2), which has the second largest share of traditional orchards in Germany. We aimed to (1) quantify long-term orchard dynamics, (2) compare orchard characteristics in terms of topographical, ecological, and socioeconomic factors, and (3) identify key drivers of orchard loss. We found that the number and area of orchards have clearly decreased across Hesse, with varying local and regional patterns. Further, historically old orchards tended to have a larger area, higher shape complexity, and were located closer to settlements, highways, and neighbouring orchards. In contrast, newly established orchards were often found at higher elevations and on steeper slopes. Finally, the three historical orchard hotspots also experienced the most notable losses driven by different factors, namely the expansion of Artificial Surfaces, Residential Buildings, and Agricultural Land. We highlight the importance of such multitemporal spatial data for a wide range of ecological applications, and we encourage the use of novel technologies to support geospatial analyses in the future. [ABSTRACT FROM AUTHOR]

Invasive species can alter the structure and functioning of the invaded ecosystem, but predictions of the impact of invasive species on ecosystem functioning are weak. Invasion is determined by the interplay of invasive species traits, the recipient community, and the environmental context. However, efficient approaches to assess the spatial dimension of functional changes in heterogeneous environments and altered plant-plant interactions are lacking. Based on recent technological progress, we posit a way forward to i) quantify the fine-scale heterogeneity of the environmental context, ii) map the structure and function of the invaded system, iii) trace changes induced by the invader with functional tracers, and iv) integrate the different spatio-temporal information from different scales using (artificial intelligence-based) modelling approaches to better predict invasion impacts. An animated 3-D model visualisation demonstrates how maps of functional tracers reveal spatio-temporal dynamics of invader impacts. Merging fine- to coarse-scale spatially explicit information of functional changes with remotely sensed metrics will open new avenues for detecting invader impacts on ecosystem functioning. [ABSTRACT FROM AUTHOR]

The intensification of food production systems has resulted in landscape simplification, with trees and hedges disappearing from agricultural land, principally in industrialized countries. However, more recently, the potential of agroforestry systems and small woody landscape features (SWFs), e.g., hedgerows, woodlots, and scattered groups of trees, to sequester carbon was highlighted as one of the strategies to combat global climate change. Our study was aimed to assess the extent of SWFs embedded within agricultural landscapes in Germany, estimate their carbon stocks, and investigate the potential for increasing agroforestry cover to offset agricultural greenhouse gas (GHG) emissions. We analyzed open-source geospatial datasets and identified over 900,000 hectares of SWFs on agricultural land, equivalent to 4.6% of the total farmland. The carbon storage of SWFs was estimated at 111 ± 52 SD teragrams of carbon (Tg C), which was previously unaccounted for in GHG inventories and could play a role in mitigating the emissions. Furthermore, we found cropland to have the lowest SWF density and thus the highest potential to benefit from the implementation of agroforestry, which could sequester between 0.2 and 2 Tg of carbon per year. Our study highlights that country-specific data are urgently needed to refine C stock estimates, improve GHG inventories and inform the large-scale implementation of agroforestry in Germany. [ABSTRACT FROM AUTHOR]

Linking remote sensing methodology to stable isotope ecology provides a promising approach to study ecological processes from small to large spatial scales. Here, we show that δ15N can be detected in fresh leaf reflectance spectra of field samples along a spatial gradient of increasing nitrogen input from an N2-fixing invasive species. However, in field data it is unclear whether δ15N directly influences leaf reflectance spectra or if the relationship is based on covariation between δ15N and foliar nitrogen content or other leaf properties. Using a 15N-labeling approach, we experimentally varied δ15N independently of any other leaf properties in three plant species across different leaf developmental and physiological states. δ15N could successfully be modeled by means of partial least squares (PLSs) regressions, using leaf reflectance spectra as predictor variables. PLS models explained 53–73% of the variation in δ15N within species. Several wavelength regions important for predicting δ15N were consistent across species and could furthermore be related to known absorption features of N-containing molecular bonds. By eliminating covariation with other leaf properties as an explanation for the relationship between reflectance and δ15N, our results demonstrate that 15N itself has an inherent effect on leaf reflectance spectra. Thus, our study substantiates the use of spectroscopic measurements to retrieve isotopic signatures for ecological studies and encourages future development. Furthermore, our results highlight the great potential of optical measurements for up-scaling isotope ecology to larger spatial scales. [ABSTRACT FROM AUTHOR]