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[Description of methods used for collection/generation of data] We deployed 12 trail cameras in Montaña Palentina (Acorn LTL5310 (Shenzhen, China) =9; Bushnell Trophy Cam HD Black Led (Kansas, USA) = 3) and 12 in Alto Sil (Acorn LTL5310 (Shenzhen, China) =9; Bushnell Trophy Cam HD Black Led (Kansas, USA) = 2, and Browning Dark OPS (Herstal, Belgium) = 1), between July and September 2023 (ca. 60 days per camera) aiming at detecting European wildcats. Inside each location, cameras were distributed within an area of roughly 2000 Has at distances of ca. 1500 m from each other, as this procedure is reliable to estimate European wildcat densities (Gil-Sánchez et al., 2015). Cameras were placed 30-50 cm above ground pointing perpendicularly at paths naturally used by wild animals and from which they were 2-3 m away. An open Eppendorf tube filled with Iberian lynx (Lynx pardinus) urine was buried in front of each camera, aiming at causing European wildcats to stop in front of the camera to optimize identification of individuals based on pelage patterns. Cameras were checked ca. every 20 days to download pictures and replace the batteries and urine (i.e. 2 reviews after deployment and finally camera retrieval). All cameras were programmed to take a burst of three pictures per trigger. Sensitivity was set to low to minimize the excess of triggers caused by sun glares or moving branches. Cameras used natural and infrared light during day and night triggers respectively., [Methods for processing the data] Pictures were sorted following the methodology of R package CamtrapR (Niedballa et al. 2016) in order to extract metadata (date and time of the picture). Humans and birds were not included., [EN] This dataset provides the raw results of a mammalian survey campaign using trail cameras conducted between July and September 2023 (1495 trap-nights) simultaneously in Montaña Palentina Natural Park (Palencia, Spain) and Alto Sil (León, Spain). The main objective of the survey was to detect and estimate population numbers for European wildcats (Felis silvestris), but several small and large mammal species (Carnivora, Artiodactyla, Rodentia and Lagomorpha) were detected., [ES] Este conjunto de datos proporciona los resultados brutos de la campaña de fototrampeo desarrollada entre Julio y Septiembre de 2023 (1495 noches-trampa) de forma simultánea en el Parque Natural de Montaña Palentina (MNPN, Palencia, España) y el Alto Sil (León, Spain). El principal objetivo del muestreo era detectar y estimar números poblacionales en el gato montés europeo (Felis silvestris), pero otras especies de mamíferos (Carnivora, Artiodactyla, Rodentia and Lagomorpha) también fueron detectadas., This study was funded by a contract of collaboration between Fundación Reina Sofia and CSIC, the MICINN through the European Regional Development Fund [SUMHAL, LIFEWATCH-2019-09-CSIC-04, POPE 2014-2020], and Land Rover España., Peer reviewed

5 pages. -- Generating the simulated datasets. -- Testing the function. -- Additional tests. -- Fig. S1. Simulated datasets for 10 individuals., With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)., Peer reviewed

24 pages. -- Table S1: R functions developed specifically for this paper to test the effects of individual site fidelity, temporal and spatial variability in tracking data. The use of the functions and their arguments are described in detail. -- Table S2: summary of logger and deployment details for each colony, prior publication of the data used for this study, and permits and licences granted for the corresponding fieldwork. -- Table S3: number of individuals tracked repeatedly for each colony. -- Table S4: number of tracks per year and colony in the dataset used to run the year effect test (no repeated individuals). -- Table S5: population estimates from all breeding colonies of Cory’s, Scopoli’s and Cape Verde shearwaters available in the publisher literature. n refers to the number of tracks obtained from each colony. -- Figure S1: significant relationship between species inclusiveness values and longitude (top) and population size (breeding pairs) in the log scale (bottom) for Scopoli’s shearwater. -- Figure S2: schematic image of the main currents exploited by Cory’s and Scopoli’s shearwaters around the African continent., With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)., Peer reviewed

A tabular spreadsheet with the species, colony, unique identifiers for each bird and trip, latitude, longitude, year of tracking (1st year, 2nd year, etc.), and an ordering column that allows the positions to be ordered to form a track., on-breeding locations of Cory’s shearwaters (Calonectris borealis), Scopoli’s shearwaters (C. diomedea), and Cabo Verde shearwaters (C. edwardsii) tracked from the colonies of Berlenga, Chafarinas, Corvo, Faial, Graciosa, Montaña Clara, Pico, Selvagem, Sisargas, Terreros, Timanfaya, Veneguera, and Vila for Cory’s shearwaters; Cala Morell, Chafarinas, Filfla, Frioul, Giraglia, Gozo, Lavezzi, Linosa, Malta, Na Foradada, Na Pobra, Palomas, Pantaleu, Paximada, Porquerolles, Riou, Strofades, Tremiti, and Zembra for Scopoli’s shearwaters; and Curral Velho and Raso for Cabo Verde’s shearwaters. Animals were tracked between the years of 2006 and 2016, and data includes species, colony, unique identifiers for each bird and trip, latitude, longitude, year of tracking (1st year, 2nd year, etc.), and an ordering column that allows the positions to be ordered to form a track., With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)., Peer reviewed

Peer reviewed

This dataset encompasses the input files associated with diverse analytical and numerical models investigating cyclic pressure diffusion within uniform poroelastic materials. The dataset is organized into two primary folders. The "Analytical Solutions" folder comprises three Python libraries containing the material properties and functions for calculating pressure amplitude at various distances. The "Numerical Solutions" folder contains simulations for both Hydraulic ("01_") and Hydro-Mechanical ("HM_") problems. The simulations are conducted for representative values corresponding to Berea sandstone ("BS_"), Opalinus Clay ("OPA_"), and Westerly granite ("WG_"), considering both monodimensional ("1D") and axisymmetric ("Ax") cases. Within each subfolder, files with the suffix "_gen.dat" provide input data for the model, encompassing material properties, initial and boundary conditions, and time intervals. Correspondingly, files concluding with "_gri.dat" contain information of the mesh employed. The "_bfc.dat" file documents the periodic injection pressure applied for each simulation, while the "root.dat" file specifies the model's nomenclature. To execute a model, launching the executable "Cb_v9_1_3par4.exe" within each ".gid" folder is sufficient., European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program through the Starting Grant GEoREST (www.georest.eu), under grant agreement No. 801809. IMEDEA is an accredited "Maria de Maeztu Excellence Unit" (Grant CEX2021-001198, funded by MCIN/AEI/10.13039/501100011033)., With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001198)., File List: README.txt (this file) \Analytical Solutions\CyclicDiffusion.py \Analytical Solutions\CyclicDiffusion_Adimensional.py \Analytical Solutions\Materials.py \Numerical Solutions\01_BS_1D_g0_1yr.gid \Numerical Solutions\01_BS_Ax_g0_1yr.gid \Numerical Solutions\01_OPA_1D_g0_1w.gid \Numerical Solutions\01_OPA_Ax_g0_1w.gid \Numerical Solutions\01_WG_1D.gid \Numerical Solutions\01_WG_Ax.gid \Numerical Solutions\HM_BS_1D_g0_1yr.gid \Numerical Solutions\HM_BS_Ax_g0_1yr.gid \Numerical Solutions\HM_OPA_1D_g0_1w.gid \Numerical Solutions\HM_OPA_Ax_g0_1w.gid \Numerical Solutions\HM_WG_1D.gid \Numerical Solutions\HM_WG_Ax.gid \Numerical Solutions\Cb_v9_1_3par4.exe, Peer reviewed

19 pages. -- 1. Sex ratio analyses. -- 2. Model fits., Peer reviewed

1 table., Despite the dynamic nature of communities, most research typically treated interaction networks as static entities, and only a few analysed the spatial and the temporal scale simultaneously. Here, we used spatial and temporal multilayer networks to explore the persistence of species and interactions in space and time, as well as the variation of species importance according to biotic factors. We further investigated, for the first time, the spatio-temporal variation in multilayer network structure. Species exhibited substantial turnover across time mainly due to fluctuations in phenology. In contrast, interaction turnover was more pronounced across habitats, which seems to be a common strategy of pollinators to adjust interactions spatially to different ecological scenarios. Overall, species were comparatively more important to the cohesiveness of spatial than of temporal networks. The importance of plant species fluctuated temporally due mainly to changes in flower density, whereas that of pollinators fluctuated in space and time according to the abundance. Module composition was highly unstable in time, and species were highly capable of changing partners and module affiliation across both scales. These findings emphasise the importance of avoiding not only habitat loss but also habitat fragmentation to maintain highly connected habitats in terms of species interactions., Peer reviewed

1 table., Despite the dynamic nature of communities, most research typically treated interaction networks as static entities, and only a few analysed the spatial and the temporal scales simultaneously. Here, we used spatial and temporal multilayer networks to explore the persistence of species and interactions in space and time, as well as the variation of species role (centrality) according to biotic factors. We further investigated, for the first time, the spatio-temporal variation in multilayer network structure. Species exhibited substantial turnover across time mainly due to differences in species phenology. In contrast, interaction turnover was more pronounced across habitats, which seems to be a common strategy of pollinators to adjust interactions spatially to different ecological scenarios. Plant species were comparatively more important to the cohesiveness of spatial than temporal networks whereas the centrality of pollinators correlated between scales. The importance of plant species fluctuated temporally due mainly to changes in flower density, whereas that of pollinators fluctuated in space and time according to their relative abundance. Module composition was highly unstable in time. Species were highly capable of changing partners and module affiliation across both scales. We conclude that plant-pollinator interactions are highly dynamic in space and time and that there are differences between plants and pollinators in their use of resources across habitats and subseasons with implications for the understanding of functional connectivity and multilayer structure., This project (Ref. #: Polin_Menorca 096) was funded by the Menorca Biosphere Reserve., Peer reviewed

8 pages. -- Table S1. Origin, team members, geographical location and year of the biometric measurements used in this study. [Origen, equipo, ubicación geográfica y año de las medidas biométricas utilizadas en este estudio.]. -- Table S2. The number of study colonies and adult Yellow-legged gulls measured per colony. [Número de colonias de estudio y gaviotas patiamarillas adultas medidas por colonia.]. -- Table S3. Means and standard deviations (SD) of all study variables for males and females from both regions. CA: Cantabrian-Atlantic; ME: Mediterranean. All measurements in mm. Sample sizes available for each variable in Figure 1. [Media y desviación estándar (DE) de todas las variables de estudio para machos y hembras de ambas regiones. CA: Cantábrico-Atlántico; ME: Mediterráneo. Todas las medidas en mm. Tamaños de muestra disponibles para cada variable en la Figura 1.]. -- Table S4. Results of the analysis of normality (Shapiro tests) for all study variables. CA: Cantabrian-Atlantic; MED: Mediterranean. [Resultados del análisis de normalidad (pruebas de Shapiro) para todas las variables de estudio. CA: Cantábrico-Atlántico; MED: Mediterráneo.]. -- Table S5. Results of the analysis of the homogeneity of the variances for all variables. CA: Cantabrian-Atlantic; MED: Mediterranean. [Resultados del análisis de homogeneidad de las varianzas para todas las variables. CA: Cantábrico-Atlántico; MED: Mediterráneo.]. -- Table S6. Correlation matrices (Pearson’s r) among study variables for males and females from both study regions. X indicates an estimation error in the correlation test due to the lack of sufficient finite observations. The strongest correlations are highlighted in bold. [Matrices de correlación (r de Pearson) entre variables de estudio para machos y hembras de ambas regiones de estudio. X indica un error de estimación en la prueba de correlación debido a la falta de suficientes observaciones finitas. Las correlaciones más fuertes se destacan en negrita.], Peer reviewed