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# Data from: Crop and landscape heterogeneity increase biodiversity in agricultural landscapes: A global review and meta-analysis **Journal:** Ecology Letters **Authors:** Tharaka S. Priyadarshana (ORCID: 0000-0003-3962-5465), Emily A. Martin (0000-0001-5785-9105), Clélia Sirami (0000-0003-1741-3082), Ben A. Woodcock (0000-0003-0300-9951), Eben Goodale (0000-0003-3403-2847), Carlos Martínez-Núñez (0000-0001-7814-4985), Myung-Bok Lee (0000-0003-2680-5707), Emilio Pagani-Núñez (0000-0001-8839-4005), Chloé A. Raderschall (0000-0003-2005-1705), Lluís Brotons (0000-0002-4826-4457), Anushka Rege (0000-0002-8383-0258), Annie Ouin (0000-0001-7046-2719), Teja Tscharntke (0000-0002-4482-3178), Eleanor M. Slade (0000-0002-6108-1196) **Contact:** Tharaka S. Priyadarshana; [tharakas001@e.ntu.edu.sg](mailto:tharakas001@e.ntu.edu.sg); [tharakas.priyadarshana@gmail.com](mailto:tharakas.priyadarshana@gmail.com) **Data citation:** Priyadarshana, T.S., Martin, E.A., Sirami, C., Woodcock, B.A., Goodale, E., Martínez-Núñez, C., et al. (2024). Crop and landscape heterogeneity increase biodiversity in agricultural landscapes: A global review and meta-analysis [dataset]. Dryad Digital Repository, [https://doi.org/10.5061/dryad.dbrv15f7j](https://doi.org/10.5061/dryad.dbrv15f7j). --- ## Specific data files There are eight data files containing the following metadata. To replicate our results, please run each data file with the corresponding R scripts. * **dat.csv**: This data file contains all the extracted data and should be used in analyses related to invertebrates, vertebrates, animals (invertebrates and vertebrates together), plants, and climatic regions. * **datPollinators.csv; datPredators.csv; and datPests.csv**: These three data files are derived from the *dat.csv* data file and should be used in analyses related to pollinators, predators and pests, respectively. * **datCropArea.csv; datSemiNaturalArea.csv; and datOtherArea.csv:** These three data files are derived from the *dat.csv* data file and should be used in sensitivity analyses testing potential confounding effects on our results that may be caused by the proportion of cropped, semi-natural, and other anthropogenic land-cover types within the landscapes, respectively. * **datCroppingSystems.csv:** This data file is derived from the *dat.csv* data file and should be used in analyses related to cropping systems. ## Metadata * **Author:** Citation for each study * **Study_ID:** Identifier for each study * **Effect_Size_ID:** Identifier for effect size * **Study_Country:** Study country * **Study_Region:** Study region * **Biome:** Tropical/sub-tropical vs temperate farmlands * **Study_System:** Dominant crops * **Verts_Inverts_Plants:** Taxa categorisation into invertebrates, vertebrates, and plants * **Verts_Inverts_Plants_Without_Pests:** Taxa categorisation into invertebrates, vertebrates, and plants, but excluding pest invertebrates and vertebrates * **Animals_Plants:** Taxa categorisation into animals (i.e. invertebrates and vertebrates without pests) and plants * **Functional_Groups:** Functional groups * **Orders:** Taxonomic orders, including birds * **Taxa_Category_2:** More details about the study taxa * **Biodiversity_Category:** Biodiversity metrics (i.e. abundance, richness and Shannon diversity) * **Heterogeneity_Measure:** Spatial heterogeneity components * **Heterogeneity_Mesure_Fine:** Computation of the spatial heterogeneity components * **Heterogeneity_Type:** Spatial heterogeneity component (i.e. crop compositional heterogeneity, crop configurational heterogeneity, landscape compositional heterogeneity, and landscape configurational heterogeneity) * **Heterogeneity:** Spatial heterogeneity types (i.e. compositional heterogeneity = crop and landscape compositional heterogeneity; configurational heterogeneity = crop and landscape configurational heterogeneity ) * **Heterogeneity_Type_L_C:** Land-use type (i.e. crop heterogeneity = crop compositional and configurational heterogeneity; landscape heterogeneity = landscape compositional and configurational heterogeneity) * **Pearson_Correlation:** Effect size (i.e. Pearson correlation coefficient) * **Sample_Size:** Sample size * **Scale:** Spatial scales that the heterogeneity components are defined at * **Scale_Cat:** Spatial scale (A_LocalScale [i.e. < 0.5 km radius area], LandscapeScale_1 [i.e. ≥ 0.5 km, but < 1 km], and LandscapeScale_2 [i.e. ≥ 1 km radius area]) * **Seminatural_Cover_Mean:** Mean semi-natural area (%) * **Crop_Cover_Mean:** Mean cultivated area (%) * **Other_Cover_Mean:** Mean other land-use area (%) * **Annual_Perennial:** Annual vs perennial crops * **Article_Tittle:** Article tittle * **Notes:** Additional notes * **NAs:** Not available ## Specific R scripts * **1 Publication_Bias.R:** A R script testing for publication bias, outliers, and influential studies; this script should be run with *dat.csv* data file. * **2 Effects_Of_Semi-Natural_Area.R:** A R script testing for the influence of mean semi-natural area on the estimated average relationships between spatial heterogeneity and biodiversity; this script should be run with *datSemiNaturalArea.csv* data file. * **3 Effects_Of_Crop_Area.R:** A R script testing for the influence of mean cultivated area on the estimated average relationships between spatial heterogeneity and biodiversity; this script should be run with *datCropArea.csv* data file. * **4 Effects_Of_Other_Land_Use_Area.R:** A R script testing for the influence of mean other land-use area on the estimated average relationships between spatial heterogeneity and biodiversity; this script should be run with *datOtherArea.csv* data file. * **5 Invertebrate_Abundance.R:** A R script testing for the effect of spatial heterogeneity on invertebrate abundance; this script should be run with *dat.csv* data file. * **6 Invertebrate_Richness.R:** A R script testing for the effect of spatial heterogeneity on invertebrate richness; this script should be run with *dat.csv* data file. * **7 Invertebrate_Diversity.R:** A R script testing for the effect of spatial heterogeneity on invertebrate Shannon diversity; this script should be run with *dat.csv* data file. * **8 Vertebrate_Abundance.R:** A R script testing for the effect of spatial heterogeneity on vertebrate abundance; this script should be run with *dat.csv* data file. * **9 Vertebrate_Richness.R:** A R script testing for the effect of spatial heterogeneity on vertebrate richness; this script should be run with *dat.csv* data file. * **10 Vertebrate_Diversity.R:** A R script testing for the effect of spatial heterogeneity on vertebrate Shannon diversity; this script should be run with *dat.csv* data file. * **11 Plant_Abundance.R:** A R script testing for the effect of spatial heterogeneity on plant abundance; this script should be run with *dat.csv* data file. * **12 Plant_Richness.R:** A R script testing for the effect of spatial heterogeneity on plant richness; this script should be run with *dat.csv* data file. * **13 Plant_Diversity.R:** A R script testing for the effect of spatial heterogeneity on plant Shannon diversity; this script should be run with *dat.csv* data file. * **14 Pollinator_Abundance.R:** A R script testing for the effect of spatial heterogeneity on pollinator abundance; this script should be run with *datPollinators.csv* data file. * **15 Pollinator_Richness.R:** A R script testing for the effect of spatial heterogeneity on pollinator richness; this script should be run with *datPollinators.csv* data file. * **16 Pollinator_Diversity.R:** A R script testing for the effect of spatial heterogeneity on pollinator Shannon diversity; this script should be run with *datPollinators.csv* data file. * **17 Predator_Abundance.R:** A R script testing for the effects of spatial heterogeneity on predator abundance; this script should be run with *datPredators.csv* data file. * **18 Predator_Richness.R:** A R script testing for the effect of spatial heterogeneity on predator richness; this script should be run with *datPredators.csv* data file. * **19 Predator_Diversity.R:** A R script testing for the effect of spatial heterogeneity on predator Shannon diversity; this script should be run with *datPredators.csv* data file. * **20 Pest_Abundance.R:** A R script testing for the effect of spatial heterogeneity on pest abundance; this script should be run with *datPests.csv* data file. * **21 Pest_Richness.R:** A R script testing for the effect of spatial heterogeneity on pest richness; this script should be run with *datPests.csv* data file. * **22 Araneae_Abundance.R:** A R script testing for the effect of spatial heterogeneity on Araneae abundance; this script should be run with *dat.csv* data file. * **23 Araneae_Richness.R:** A R script testing for the effect of spatial heterogeneity on Araneae richness; this script should be run with *dat.csv* data file. * **24 Araneae_Diversity.R:** A R script testing for the effect of spatial heterogeneity on Araneae Shannon diversity; this script should be run with *dat.csv* data file. * **25 Bird_Abundance.R:** A R script testing for the effect of spatial heterogeneity on bird abundance; this script should be run with *dat.csv* data file. * **26 Bird_Richness.R:** A R script testing for the effect of spatial heterogeneity on bird richness; this script should be run with *dat.csv* data file. * **27 Bird_Diversity.R:** A R script testing for the effect of spatial heterogeneity on bird Shannon diversity; this script should be run with *dat.csv* data file. * **28 Coleoptera_Abundance.R:** A R script testing for the effect of spatial heterogeneity on Coleoptera abundance; this script should be run with *dat.csv* data file. * **29 Coleoptera_Richness.R:** A R script testing for the effect of spatial heterogeneity on Coleoptera richness; this script should be run with *dat.csv* data file. * **30 Coleoptera_Diversity.R:** A R script testing for the effect of spatial heterogeneity on Coleoptera Shannon diversity; this script should be run with *dat.csv* data file. * **31 Diptera_Abundance.R:** A R script testing for the effect of spatial heterogeneity on Diptera abundance; this script should be run with *dat.csv* data file. * **32 Diptera_Richness.R:** A R script testing for the effect of spatial heterogeneity on Diptera richness; this script should be run with *dat.csv* data file. * **33 Diptera_Diversity.R:** A R script testing for the effect of spatial heterogeneity on Diptera Shannon diversity; this script should be run with dat.csv data file. * **34 Hymenoptera_Abundance.R:** A R script testing for the effect of spatial heterogeneity on Hymenoptera abundance; this script should be run with *dat.csv* data file. * **35 Hymenoptera_Richness.R:** A R script testing for the effect of spatial heterogeneity on Hymenoptera richness; this script should be run with *dat.csv* data file. * **36 Hymenoptera_Diversity.R:** A R script testing for the effect of spatial heterogeneity on Hymenoptera Shannon diversity; this script should be run with *dat.csv* data file. * **37 Lepidoptera_Abundance.R:** A R script testing for the effect of spatial heterogeneity on Lepidoptera abundance; this script should be run with *dat.csv* data file. * **38 Lepidoptera_Richness.R:** A R script testing for the effect of spatial heterogeneity on Lepidoptera richness; this script should be run with *dat.csv* data file. * **39 Lepidoptera_Diversity.R:** A R script testing for the effect of spatial heterogeneity on Lepidoptera Shannon diversity; this script should be run with *dat.csv* data file. * **40 Animal_Abundance.R:** A R script testing for the effect of spatial heterogeneity on animal [i.e. invertebrate and vertebrate without pests] abundance; this script should be run with *dat.csv* data file. * **41 Animal_Richness.R:** A R script testing for the effect of spatial heterogeneity on animal [i.e. invertebrate and vertebrate without pests] richness; this script should be run with *dat.csv* data file. * **42 Animal_Diversity.R:** A R script testing for the effects of spatial heterogeneity metrics on animal [i.e. invertebrate and vertebrate without pests] Shannon diversity; this script should be run with *dat.csv* data file. * **43 Tropical_vs_Temperate_Farmlands.R:** A R script testing for the variabilities of the effect of spatial heterogeneity on animals [i.e. invertebrate and vertebrate without pests] in tropical an temperate farmlands; this script should be run with *dat.csv* data file. * **44 Annual_vs_Perennial_Crops.R:** A R script testing for the variabilities of the effect of spatial heterogeneity on animals [i.e. invertebrate and vertebrate without pests] in annual and perennial farmlands; this script should be run with *datCroppingSystems.csv* data file. * **45 Invertebrate_Scale.R:** A R script testing for the variabilities of the effect of spatial heterogeneity on invertebrate in local-level and landscape-level scales; this script should be run with *dat.csv* data file. * **46 Vertebrate_Scale.R:** A R script testing for the variabilities of the effect of spatial heterogeneity on vertebrate in local-level and landscape-level scales; this script should be run with *dat.csv* data file. * **47 Animal_Scale.R:** A R script testing for the variabilities of the effect of spatial heterogeneity on animals [i.e. invertebrate and vertebrate without pests] in local-level and landscape-level scales; this script should be run with *dat.csv* data file. * **48 Pollinator_Scale.R:** A R script testing for the variabilities of the effect of spatial heterogeneity on pollinators in local-level and landscape-level scales; this script should be run with *datPollinators.csv* data file. * **49 Predator_Scale.R:** A R script testing for the variabilities of the effect of spatial heterogeneity on predators in local-level and landscape-level scales; this script should be run with *datPredators.csv* data file. Agricultural intensification increases food production but also drives widespread biodiversity decline. Increasing landscape heterogeneity has been suggested to increase biodiversity across habitats, while increasing crop heterogeneity may support biodiversity within agroecosystems. These spatial heterogeneity effects can be partitioned into compositional (land-cover type diversity) and configurational heterogeneity (land-cover type arrangement), measured either for the crop mosaic or across the landscape for both crops and semi-natural habitats. However, studies have reported mixed responses of biodiversity to increases in these heterogeneity components across taxa and contexts. Our meta-analysis covering 6,397 fields across 122 studies conducted in Asia, Europe, and North and South America reveals consistently positive effects of crop and landscape heterogeneity, as well as compositional and configurational heterogeneity for plant, invertebrate, vertebrate, pollinator, and predator biodiversity. Vertebrates and plants benefit more from landscape heterogeneity, while invertebrates derive similar benefits from both crop and landscape heterogeneity. Pollinators benefit more from configurational heterogeneity, but predators favour compositional heterogeneity. These positive effects are consistent for invertebrates and vertebrates in both tropical/subtropical and temperate agroecosystems, and in annual and perennial cropping systems, and at small to large spatial scales. Our results suggest that promoting increased landscape heterogeneity by diversifying crops and semi-natural habitats, as suggested in the current UN Decade on Ecosystem Restoration, is key for restoring biodiversity in agricultural landscapes. We screened English Language papers published up to March 2023 from the ‘Web of Science’ (apps.webofknowledge.com/) and ‘Scopus’ (www.scopus.com/) using the following search strings. TS=("landscape heterogeneity" OR "landscape diversity" OR "landscape complexity" OR "crop heterogeneity" OR "crop diversity" OR "farmland heterogeneity" OR "farmland diversity" OR "compositional heterogeneity" OR "configurational heterogeneity") AND TS=("diversity" OR "biodiversity" OR "richness" OR "evenness" OR "abundance").

Offshore freshened groundwater (OFG) and submarine groundwater discharge (SGD) are important components of coastal hydrologic systems. A lack of understanding of offshore groundwater systems and their interactions with onshore systems along the majority of global coastlines still exists due to a general paucity of field data. Recently, controlled-source electromagnetic (CSEM) techniques have emerged as a promising noninvasive method for identifying and characterizing OFG and SGD. Unfortunately, only a few systems are available in academic and research institutions worldwide, and applications are limited to specific regions. These systems are often limited by relatively high deployment costs, slow data acquisition rates, logistical complexity, and lack of modification options. A relatively inexpensive and user-friendly CSEM system is needed to overcome these limitations. We present the initial theoretical and practical developments of SWAN — a low-cost, modular, surface-towed hybrid time-frequency domain CSEM system capable of detecting OFG and SGD to water depths of 100 m. A field test of the system was carried out in the central Adriatic Sea at water depths between several tens to approximately 160 m to illustrate its capabilities. Through its ability to facilitate continuous measurements in both the time and frequency domain, the system has demonstrated its effectiveness in acquiring high-quality data while operating at towing speeds ranging from 2.5 to 3 kn. The resulting data coverage enables the system to detect variations in subsurface resistivity to depths of approximately 150–200 m below seafloor. With its modular, user-friendly design, SWAN provides an accessible, cost-efficient means to investigate the hydrogeology of shallow offshore environments.

In this deliverable of the iFishIENCi Project, we aim to provide an overall description of the iBOSS, before presenting specific deployments conducted as part of the piloting activities (WP3). For each selected deployments, recommendations for future developments are discussed based on the lesson learned from the pilots.

This work presents refined, updated subregional and regional non-indigenous species (NIS) inventories for the Mediterranean Sea, validated by national and taxonomic experts, with species records observed until December 2020. These datasets will be used as the baselines for the implementation of the Integrated Monitoring and Assessment Programme for the Mediterranean (IMAP) and the Mediterranean Quality Status Report 2023. In total, 1006 non-indigenous species have been found in Mediterranean marine and brackish waters. The highest numbers of NIS were observed in Israel, Turkiye, Lebanon and Italy. Approximately 45 species were categorized as data deficient, either due to lack of consensus on their alien status or the validity of their identification. Polychaeta, Foraminifera and macroalgae were the groups with the highest numbers of controversial species. There was a general increase in the yearly rate of new NIS introductions after the late 1990s, which appears to be slowing down in the last decade, but this may be confounded by reporting lags and differential research efforts. Between 1970 and 2020 there has been a steep increase in the proportion of shared species present throughout all four Mediterranean subregions, which are predominantly transported via shipping and recreational boating. While Lessepsian species are gradually spreading westwards and northwards, there is still a considerable invasion debt accumulating in the eastern and central Mediterranean. This study was funded by SPA/RAC and the IMAP-MAP project through Contract Ny 11_2021_SPA/RAC IMAP-MAP PROJECT Baseline of non-indigenous species in the Mediterranean. SPA/RAC; IMAP-MAP project [11_2021_SPA/RAC]; SPA/RAC; IMAP-MAP project [11_2021_SPA/RAC]

Volcanoes are sources of numerous threats including lava flows, pyroclastic flows, ash dispersal and landslides or sector collapses. In addition to these commonly known volcanic hazards, volcano-induced tsunamis can occur in the marine environment, introducing a major hazard that can affect populations located far away from the volcanoes. Existing tsunami warning systems generally do not account for volcano-generated tsunamis, due to the multiple source mechanisms that can cause such tsunamis, a limited understanding of precursory signals for these events, and the need for local detection rather than remote sensing. Among these source mechanisms of volcanic tsunamis, sector and lateral collapses are at the high risk-low frequency extreme of risk matrices. Marine volcanoes grow in specific environments, with factors like marine clays, constant full saturation, sediment transport and remobilization, interaction with ocean dynamics, and sea level changes that may impact edifice stability in distinct ways. The majority of historically documented marine volcano collapses occurred at erupting volcanoes, suggesting that eruptions could serve as a remotely detectable warning signal for collapses. However, careful examination of temporal sequences of these examples reveals that collapses do not always follow eruptions. Consequently, there is a need for identifying other, more robust precursors to volcano collapse, in particular in the marine environment, where the consequences of collapses may be widespread.

A new assessment of the global biodiversity of decapod Crustacea (to 31 December 2022) records 17,229 species in 2,550 genera and 203 families. These figures are derived from a well-curated dataset maintained on the online platform DecaNet, a subsidiary of the World Register of Marine Species (WoRMS). Distinct phases are recognised in the discovery process (as measured by species descriptions) corresponding to major historical and geopolitical time periods, with the current rate of species descriptions being more than three times higher than in the Victorian age of global exploration. Future trends are briefly explored, and it is recognised that a large number of species remain to be discovered and described. International audience

Abstract Gas hydrates are one of the largest marine carbon reservoirs on Earth. The conventional understanding of hydrate dynamics assumes that the system converges to a steady-state over geological time-scales, achieving fixed concentrations of gas hydrate and free gas phase. However, using a high-fidelity numerical model and consistently resolving phase states across multiple fluid-fluid and fluid-solid phase boundaries, we have identified well-defined periodic states embedded within hydrate system dynamics. These states lead to cyclic formation and dissolution of massive hydrate layers that is self-sustaining even in the absence of external triggers. This previously unresolved characteristic could manifest as spontaneous gas discharge and pressure release in, supposedly, unperturbed systems. Our findings challenge the foundational principle that the gas hydrate systems have unique steady-state solutions. Instead, existence of periodic states introduces an irreducible uncertainty in gas hydrate dynamics which puts significant error bars on previous hydrate estimates.