BUSCADOR RECOLECTA

(A) Schematic drawing of an early S9 egg chamber illustrating the BCs (yellow), NCs (gray), FCs (purple), BM (green), and the position where AFM measurements were taken (arrow). (B) Comparison of the apparent elastic modulus K in egg chambers of the designated genotypes. (C) Schematic drawing of a middle S9 egg chamber illustrating the BCs (yellow), NCs (gray), FCs (purple), BM (green), the mirror region (pink square) and the positions where AFM measurements were taken, anterior to the mirr region (A, black arrow) and in the mirr region (C, pink arrow). (D, E) Comparison of the apparent elastic modulus K in the mirr region (C) and anterior to the mirr region (E), in 4 control mirrGal4 (D) and mirr>EHBP1mCh egg chambers. A minimum of 4 different readings (circles)/egg chamber were taken. Horizontal lines in B, D, and E represent mean values. The statistical significance of differences was assessed with a t test, * P value < 0.05, ** P value < 0.01, and *** P value < 0.001. Horizontal and vertical lines indicate mean and SD, respectively. The raw data underlying panels B, D, and E are available in S1 Data., Peer reviewed

(A) S10 control tjGal4 and (B) tj>LanB1RNAi (B) egg chambers stained with anti-LanB1 antibody (green), the DNA marker Hoechst (blue) and the F-actin marker Rhodamine-Phalloidin (F-actin, red). (C) Quantification of the LanB1 levels in egg chambers of the specified genotypes. The statistical significance of differences was assessed with a t test, *** P value < 0.001. Horizontal and vertical lines indicate mean and SD, respectively. Scale bars in A and B, 20 μm. The raw data underlying panel C are available in S1 Data., Peer reviewed

This dataset includes very high spatial resolution observations at 1 m spatial resolution observations of the snow depth, the NDVI and the melt-out date (DOY of year) acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees, the Izas Experimental Catchment. During two snow seasons (2019-2020 and 2020-2021), 14 NDVI and 17 snow depth distributions were acquired over 48ha. From the snow depth observations the melt-out dates have been derived. Also information on the main topographic variables (elevation, aspect and slope) is included, with same spatial resolution, in this dataset., Peer reviewed

Supplementary material: Table S1. Summary of UAV acquisition during the study period. DOY: Julian Day of the Year.-- Table S2: Plant species observed on each survey plot.-- Figure S1: Upper panel shows, mean, minimum and maximum daily temperatures (5-day moving average). The lower panel depicts the temporal evolution of the snow depth and the total precipitation observed at the automatic weather station.-- Figure S2: Box plots depicting the temporal evolution of the NDVI for the UAV acquisition dates in the plants survey plots in 2019-2020 (upper panel) and 2020-2021 (bottom panel). The boxes show first and third quantiles and the horizontal line inside each box the second percentile (median). Whiskers of each plot include maximum and minimum values for each plot and day.-- Figure S3: Box plot (upper panel) of ASD spectrometer and Sequoia camera mounted on the UAV for the four bands and the NDVI computed from the near infrared and red bands. Linear adjustment (bottom panel) between the NDVI observations of the UAV and the ASD., Peer reviewed

BC migration in tslGFP; mirrGal4 and tslGFP; mirr> AbiRNAi egg chambers, related to Fig 6. Scale bar, 20 μm., Peer reviewed

The excel file uploaded here contains data collected from livestock farmers across the Chitwan-Annapurna Landscape in Central Nepal. The file includes raw data for farmer\'92s perception rankings, traits, and knowledge corresponding to the questions asked within questionnaires that were conducted as part of the study methodology. The file also includes a legend with definitions for each column in the file., [Methods] Between 2018 and 2019, we conducted 141 interviews with livestock farmers across the Chitwan-Annapurna Landscaoe of Central Nepal. In each of the three study areas, we selected 16-24 villages according to their accessibility, and based on communications with local governmental officials and community leaders. At each village, we approached 1-6 individuals that identified as keeping livestock by a combination method of random and snowball sampling (Cortés-Avizanda et al. 2018, García-Alfonso et al. 2019). All data was collected by hand in the field, and then the corresponding author manually digitized all responses into corresponding entries within Microsoft Excel. Our fieldwork, including survey design and methodology, was conducted with the approval of The City University of New York’s Human Research Protection Program (HRRP) under the category of Human Subject Research (IRB File #2019-0413). In addition, we also received approval for our survey methodology and fieldwork from Nepal’s Department of National Parks and Wildlife Conservation. We sought verbal informed consent before proceeding with the survey, rather than written consent, due to variability in literacy rates, and farmers’ comfort with reading written documents and ability to sign. In accordance with the guidelines of our institution’s HRRP and Institution Review Board, we first read a pre-approved oral consent script aloud to participants that explained the purpose of our study, our local collaborators, and the nature of questions. Participants were assured that their identities would remain anonymous, and no personal identifiers would be recorded from the information collected., [Usage notes] Analyses were conducted using R software 3.3.1 (R Core Team 2016) with ‘glm’ from the stats package for “univariate” (with fixed factor) models, and ‘glmulti’ from the glmulti package (version 1.0.7.1) for multivariate model selection (Calcagno and de Mazancourt 2010). Missing values (e.g., farmer was not asked about species as it was not included in the survey for a given survey area, farmer did not respond to the specific question) are designated as "NA". All analyses omitted NAs, unless otherwise specified in the manuscript., 1. There is a long-standing relationship between humans and vertebrate scavengers, as scavengers’ contributions take on regulating (e.g. nutrient recycling, disease control), material (e.g. competition, livestock depredation) and non-material (e.g. sky burials, ecotourism) roles in society. A social-ecological approach to studying biodiversity is increasingly needed, since the inclusion of local perceptions and knowledge has proven critical for effective conservation programs and ecosystem management., 2. We examine livestock farmers’ perceptions and knowledge related to vertebrate scavengers in the highly diverse Chitwan-Annapurna Landscape (Nepal), and assess the sociodemographic traits that influence their perceived value of scavengers’ ecosystem services provisioning (ESP), and function via scavenging services (SS)., 3. Farmers’ perceptions of functional importance (SS) showed species-specific gradation, unlike ESP, where only avian scavengers were perceived as beneficial. Our results show that the perception of scavenging as a beneficial ecosystem service and its importance as a biological function are decoupled for facultative scavengers, and coupled for obligate scavengers. Relatedly, we identify that affluence-related traits drove positive perceptions of ESP, and local ecological knowledge-based traits were linked to increased knowledge of function via SS., 4. Thus, this increased awareness of functional importance based on close contact with nature does not guarantee positive valuations of scavengers’ contributions, whereas formal education did influence positive perceptions despite reduced awareness of function. Additionally, our findings suggest that existing environmental education measures are targeting the right groups, as these respondents coincide with lower favorability of scavengers’ ecosystem services, but may be unable to overcome existing human-wildlife conflict., 5. For the first time in South Asia, we survey relevant community stakeholder’s attitudes towards an entire scavenging guild and their associated benefits, detriments, and functional importance. Our study illustrates the varied perceptions that exist for different scavenger species, and closely examines a wide-ranging set of sociodemographic traits that show disparate influences on farmers’ knowledge of ecological function and perceived ecosystem service benefits. Crucially, these findings can guide conservation and management priorities by considering the differences in public perception and awareness of scavenging, as well as the interpretation of nature’s contribution to people., Peer reviewed

BC migration in tslGFP; tjGal4 and tslGFP; tj>AbiRNAi egg chambers. Scale bar, 20 μm., Peer reviewed

Figure S1. Correlation chart of environmental variables used for microclimatic models in the PNOMP.-- Table S1. Intercept and significant coefficients of environmental variables included in “microclimatic” Generalized Linear Models, after model selection by Akaike Information Criteria (stepAIC). All models were statistically significant (p<0.001).-- Table S2. Intercept and significant coefficients of environmental variables included in “refugial capacity” Generalized Linear Models, after model selection by Akaike Information Criteria (stepAIC). All models were statistically significant (p<0.001)., Peer reviewed

En este proyecto se ha trabajado para la investigación del efecto CISS en capas de radicales orgánicos. La dataset proporcionada permite poder graficar todos los espectros y otros gráficos que aparacen tanto en el manuscrito princiapl como en la información suplementaria., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000917-S)., Peer reviewed

[Methods] The study was based on a survey of 79 lakes from mid-July to the end of August 2000 across the entire range of the Pyrenees. During this period, summer stratification occurs in most lakes, and phytoplankton communities can be assumed to be in a similar successional stage. The lakes were selected to cover the main bedrock, elevation, and geographical variation within the range. Only one or, exceptionally, a few lakes were sampled in cirque basins that included many close lakes to reduce potential spatial covariance because of the proximity and also to facilitate covering the entire massif with the available resources. The environmental variables considered (54) were grouped into five categories to evaluate the partial and hierarchical influence on the phytoplankton groups. Water chemistry (15 variables) included nutrients, major ions, and DOC. The physical environment (12) was characterized by considering morphological, thermal, light, and littoral substrate characteristics. The biotic environment (8) included macrophyte and fish presence (assessed using ancillary information and visual inspection during the survey), the organic content of the top sediment (loss on ignition, LOI), and planktonic components (rotifers, macrozooplankton, and bacterial biomass). The description of the catchment (16) included the catchment area and geological characteristics determined using cartographic information (Spanish and French Geological Maps) and GIS techniques, estimates of precipitation and duration of the ice cover inferred by extrapolation methods, and vegetation categories evaluated during the survey. Finally, the geographic setting (3) was defined by coordinates and altitude. Pigments were used for quantifying the relative dominance of high-rank taxonomic phytoplankton groups. The relationship between chlorophyll and phytoplankton biomass may be highly influenced by light conditions. To facilitate the lake comparison, we standardized pigment sampling by collecting water at an iso-irradiance depth in the deepest part of each lake at 1.5 times the Secchi disk depth. At this depth, between 1 and 10% subsurface irradiance penetrates in summer, usually coinciding with a typical deep chlorophyll maximum (DCM). The DCM results from a phytoplankton growth optimum at a depth balancing nutrient and light availability. In shallow lakes, with >10% surface irradiance reaching the bottom and weak stratification, DCM develops near the lake bottom, as early studies have already observed. Therefore, the sample was taken between 1 and 2 m above the surface sediment in 29 lakes where the Secchi depth reached the sediment surface. Water samples were collected using a polyethylene tube connected to a flask and a manual vacuum pump. A volume between 1.5 and 2 L was filtered using Whatman® GF / F filters (Maidstone, UK), which were wrapped in aluminium foil and kept cold before freezing (-20 0C) within 3-6 hours. The pigments were extracted from frozen filters using a probe sonicator (50 W, 2 min) with 90% acetone. The extract was filtered through Whatman Anodisc filters (0.1 µm) and analysed by HPLC. The HPLC system was equipped with a Waters 600E solvent delivery system, a Waters 717 autosampler set at 4 °C, a C18 column (dimensions: 250 x 4.6 mm, particle size: 5µm; Spherisorb-ODS1, Waters Corporation, Milford, US) and a Waters 996 photodiode array detector. The detector was set at 440 and 660 nm to integrate the carotenoid and phorbin peaks, respectively. The pigments were separated based on modifying the method described by Kraay, Zapata and Veldhuis (1992). After injection of the sample (40 µL), pigments were eluted by a linear gradient from 100% solvent A (0.3 M ammonium acetate in methanol: acetonitrile: MilliQ water, 51:36:13 (v/v/v)) to 75% A and 25% B (ethyl acetate: acetonitrile, 70:30, (v/v)) for 5 min followed by 5 min and 20 min, respectively, of isocratic hold at 75% A and 100% solvent B. The flow rate was 1.2 mL min-1. The solvent composition was returned to initial conditions on a 5-minute gradient, followed by 5 minutes of system equilibration before injection of the following sample. Pigments were identified by comparison with a library of pigment spectra obtained from extracts of pure algae cultures from the Culture Collection of Algae and Protozoa (CCAP, Oban, Scotland, UK). Chl-a, Chl-b, and b,b-carotene standards were obtained from Sigma Chemical Co. Ltd. (UK). The extinction coefficients used for calculations were obtained from the literature (Rowan 1989; Jeffrey, Mantoura & Wright 1997). The contribution of each algal group to phytoplankton biomass was estimated in terms of Chl-a using CHEMTAX (Mackey et al. 1996; Schlüter et al. 2006). The method works by algorithmic iteration and requires a first estimate of the marker pigment to Chl-a molar ratios (initial ratio matrix; H0) appropriate for the algal classes expected in the sample. The matrix of the pigment ratio is varied by a small amount in each iteration, and the class abundance is recalculated. The class sum is checked against the measured total Chl-a. CHEMTAX gives the best fit of contributions of the predefined taxa to total Chl-a. The advantage of this method is that it distinguishes between algal groups with qualitatively identical pigment compositions by differences in pigment ratios. We used between 1 and 4 marker pigments per group, which included chlorophytes, chrysophytes, cryptophytes, diatoms, dinoflagellates, and cyanobacteria. For further details see the related publication., The data file contains the phytoplankton group distribution estimated using pigment-based chemotaxonomy across 82 lakes of the Pyrenees selected to cover the bedrock and elevation gradients., [Description of the data and file structure] The file PGMCHEMTAX-JEcol2023.xlsx contains a first raw with the variable names (19), followed by 79 raws with the data in columns. The variable names are self-descriptive, although we include a more detailed description below. There are no missing values; zeros (0) correspond to values below the detection limit of the method., 1-High mountain lakes are increasingly recognized as sentinel ecosystems of global change. Monitoring phytoplankton changes or reconstructing their composition from sedimentary records can help identify systemic changes in these lakes and their catchments. 2- This study aimed to evaluate the distribution of the major phytoplankton groups in high mountain lakes across environmental gradients and identify tipping points in relative dominance. The phytoplankton groups were estimated using pigment-based chemotaxonomy in 79 lakes in the Pyrenees selected to cover the bedrock and elevation gradients. Fifty-four environment variables were considered, including in-lake and catchment descriptors. 3-Redundancy analyses showed that in-lake descriptors override the explicative capacity of landscape variables. Generalized additive models and multivariate regression trees showed that water hardness, trophic state, and food web descriptors were, in this order, the most influential factors determining phytoplankton group dominance. Calcium concentration of about 200 μeq L-1 defined the threshold between soft waters – with chrysophytes and chlorophytes showing a higher affinity for them – and harder waters that favour diatoms and cyanobacteria. Across the trophic gradient, there was a threshold at ~5 μg L-1 of total phosphorus (TP), chrysophytes being dominant below that TP value and cryptophytes above. The dominance of chlorophytes and cryptophytes increased with the density of macrozooplankton. Chrysophytes were significantly lower and diatoms higher in lakes with fish. 4- Synthesis. The relative abundance of phytoplankton groups in temperate high mountain lakes responds in a nonlinear way to the hardness of the water in the range 20 – 1195 Ca2+ μeq L-1 and the trophic state in the range 0.94 - 19 μg L TP-1. The thresholds across water hardness and trophic state gradients coincide with studies based on other organisms, pointing to a robust typology for mountain lakes that should be considered when selecting global-change sentinel lakes and anticipating abrupt transitions across these thresholds., European Commission, Award: EVK1-CT-1999–00032, EMERGE European Commission, Award: LIFE20 NAT/ES/00347, LIFE RESQUE ALPYR European Commission, Award: BiodivRestor-280, BiodivERsA FISHME Ministerio de Ciencia e Innovación, Award: PID2019-111137GB-C21, ALKALDIA Ministerio de Ciencia e Innovación, Award: RTI2018-096217-B-I00, FUNBIO Organismo Autónomo Parques Nacionales, Award: 2413/2017, BIOOCULT, Peer reviewed