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Deep learning (DL) has huge potential to provide valuable insights into biodiversity changes in species-rich 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 AP 50 on greenhouse data, 98.2 AP 50 on experimental grassland data, and 96.5 AP 50 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 agri-environment schemes., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

We explore here our mechanistic understanding of the environmental and physiological processes that determine the oxygen isotope composition of leaf cellulose (δ 18 O cellulose ) in a drought-prone, temperate grassland ecosystem. A new allocation-and-growth model was designed and added to an 18 O-enabled soil-vegetation-atmosphere transfer model (MuSICA) to predict seasonal (April-October) and multi-annual (2007-2012) variation of δ 18 O cellulose and 18 O-enrichment of leaf cellulose (Δ 18 O cellulose ) based on the Barbour-Farquhar model. Modelled δ 18 O cellulose agreed best with observations when integrated over c. 400 growing-degree-days, similar to the average leaf lifespan observed at the site. Over the integration time, air temperature ranged from 7 to 22°C and midday relative humidity from 47 to 73%. Model agreement with observations of δ 18 O cellulose (R 2  = 0.57) and Δ 18 O cellulose (R 2  = 0.74), and their negative relationship with canopy conductance, was improved significantly when both the biochemical 18 O-fractionation between water and substrate for cellulose synthesis (ε bio , range 26-30‰) was temperature-sensitive, as previously reported for aquatic plants and heterotrophically grown wheat seedlings, and the proportion of oxygen in cellulose reflecting leaf water 18 O-enrichment (1 - p ex p x , range 0.23-0.63) was dependent on air relative humidity, as observed in independent controlled experiments with grasses. Understanding physiological information in δ 18 O cellulose requires quantitative knowledge of climatic effects on p ex p x and ε bio ., (© 2020 The Authors New Phytologist © 2020 New Phytologist Trust.)

Targeted Selective Treatment (TST) is one approach to slow down the development of anthelmintic resistance. Its success is closely linked to the correct identification of animals in need of treatment. In dairy goats it has been proposed to use milk yield as TST indicator and to focus treatments on high yielding dairy goats. In dairy sheep the relationship between milk performance and infection with gastrointestinal nematodes (GIN) is not well known. The aim of this study was to investigate the relationship between milk yield and GIN infection in dairy sheep and based on this, to evaluate milk performance data as a potential TST indicator. Overall 1159 Lacaune ewes of 15 dairy sheep farms in Switzerland were included in the study. The ewes were phenotyped once between August and December 2019, when they were at least 70 days in milk (DIM). Individual faecal samples were taken from every ewe to determine the nematode egg concentration per gram faeces (EPG). In addition, the clinical parameters FAMACHA score and packed cell volume (PCV) were measured. Linear mixed models were used to analyse the effects of the collected parameters on EPG. EPG increased significantly with increasing test day milk yields (P = 0.002), indicating high yielding ewes to be less resistant to GIN infections than low yielding ewes. The effect was most pronounced in earlier lactation but remained within a moderate range. Overall, our results indicated the potential of using milk yield data of rather early lactation as TST indicator in dairy sheep. On farms with predominantly H. contortus the combination with FAMACHA might improve the correct identification of highly infected ewes, as FAMACHA was correlated with EPG (r = 0.37, P < 0.001)., (© 2020 Published by Elsevier B.V.)

Understanding the role of individual organisms in whole-ecosystem carbon (C) fluxes is probably the biggest current challenge in C cycle research. Thus, it is unknown whether different plant community members share the same or different residence times in metabolic (τmetab ) and nonmetabolic (i.e. structural) (τnonmetab ) C pools of aboveground biomass and the fraction of fixed C allocated to aboveground nonmetabolic biomass (Anonmetab ). We assessed τmetab , τnonmetab and Anonmetab of co-dominant species from different functional groups (two bunchgrasses, a stoloniferous legume and a rosette dicot) in a temperate grassland community. Continuous, 14-16-d-long (13) C-labeling experiments were performed in September 2006, May 2007 and September 2007. A two-pool compartmental system, with a well-mixed metabolic and a nonmixed nonmetabolic pool, was the simplest biologically meaningful model that fitted the (13) C tracer kinetics in the whole-shoot biomass of all species. In all experimental periods, the species had similar τmetab (5-8 d), whereas τnonmetab ranged from 20 to 58 d (except for one outlier) and Anonmetab from 7 to 45%. Variations in τnonmetab and Anonmetab were not systematically associated with species or experimental periods, but exhibited relationships with leaf life span, particularly in the grasses. Similar pool kinetics of species suggested similar kinetics at the community level., (© 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.)