Resultados de investigación

[Description of the data and file structure] Files and variables File: dat_biomass.csv Description: Field biomass survey datasets of supratidal forest sites Variables Record: Data entry number (unique value) Plot_ID: Field plot ID Site: Study side ID (see dat_sites.csv for full details) Species: species of measured plant Dominant_genus: dominant canopy species of the plot Quadrat: quadrat ID within site Tree: Tree number within plot. n/a = not available (see Stem number instead) Stem: Stem number within plot. n/a = not available (see Tree number instead) Status: Tree health status (L = Live; D = Dead; S = Stressed) Height_m: Tree height. Unit = m. n/a = not recorded D10_cm: Trunk/stem diameter at 10cm above ground surface. Unit = cm. n/a = not recorded DBH_cm: Trunk/stem diameter at Breast Height (137cm above ground surface). Unit = cm. n/a = not recorded AGB_kg: Tree aboveground biomass estimate based on scaling with allometric equation. Unit = kg File: dat_cores.csv Description: Supratidal forest sediment sample dataset for the calculation of soil organic matter, soil organic carbon and related parameters n/a = not available Variables Record: Data entry number (unique value) Site: Study side ID (see dat_sites.csv for full details) Core_ID: sediment core ID Dominant_genus: dominant canopy species of the plot. Depth_upper_cm: sample section upper depth. Unit = cm. Depth_lower_cm: sample section lower depth. Unit = cm. Depth_mid_cm: sample section mid depth. Unit = cm. DBD_gcm-3: sample dry bulk density. Unit = g cm-3. OM_percent: sample percent organic matter. Unit = %. Corg_percent: sample percent organic carbon. Unit = %.. Corg_density: sample density of organic carbon. Unit = g C cm-3. File: dat_decomposition_BC.csv Description: Dataset of controlled decomposition experiments conducted at Berowra Creek (BC) study site across mangrove, saltmarsh and supratidal forest ecosystem types. n/a = not available Variables site: Study side ID (see dat_sites.csv for full details) time_days: time of sample collection since deployment. Unit = days Mangrove_green: Proportion of original mass remaining at time of sample collection. Ecosystem = mangrove; Material = green tea Mangrove_rooibos: Proportion of original mass remaining at time of sample collection. Ecosystem = mangrove; Material = rooibos tea Saltmarsh_green: Proportion of original mass remaining at time of sample collection. Ecosystem = saltmarsh; Material = green tea Saltmarsh_rooibos: Proportion of original mass remaining at time of sample collection. Ecosystem = saltmarsh; Material = rooibos tea Supratidal_forest_green: Proportion of original mass remaining at time of sample collection. Ecosystem = supratidal forest; Material = green tea Supratidal_forest_rooibos: Proportion of original mass remaining at time of sample collection. Ecosystem = supratidal forest; Material = rooibos tea File: dat_decomposition_TP.csv Description: Dataset of controlled decomposition experiments conducted at Towra Point (TP) study site across mangrove, saltmarsh and supratidal forest ecosystem types. n/a = not available Variables site: Study side ID (see dat_sites.csv for full details) time_days: time of sample collection since deployment. Unit = days Mangrove_green: Proportion of original mass remaining at time of sample collection. Ecosystem = mangrove; Material = green tea Mangrove_rooibos: Proportion of original mass remaining at time of sample collection. Ecosystem = mangrove; Material = rooibos tea Saltmarsh_green: Proportion of original mass remaining at time of sample collection. Ecosystem = saltmarsh; Material = green tea Saltmarsh_rooibos: Proportion of original mass remaining at time of sample collection. Ecosystem = saltmarsh; Material = rooibos tea Supratidal_forest_green: Proportion of original mass remaining at time of sample collection. Ecosystem = supratidal forest; Material = green tea Supratidal_forest_rooibos: Proportion of original mass remaining at time of sample collection. Ecosystem = supratidal forest; Material = rooibos tea File: dat_sites.csv Description: Summary of all field study sites used across various aspects of this study. Includes names, ID, location and geomorphic and vegetation attributes. Note that not all measurements/experiments were undertaken at all sites. Variables Site name: full site name Site ID: Study side ID Latitude: Approximate latitude of study site (degrees S) Longitude: Approximate latitude of study site (degrees) Estuary type: Estuary geomorphic type Entrance opening conditions: description of whether estuary mouth is permanently, or intermittently open. Tidal range: categorical classification of site tidal range Mean annual rainfall (mm): mean annual rainfall as indicated by nearest Bureau of Meteorology weather station. Unit = mm per year Dominant woody species: list of the most dominant supratidal forest species within the study site File: dat_groundwater_CI_saltmarsh.csv Description: water level dataset collected within a groundwater standpipe located within central zone of saltmarsh at Corner Inlet (CI) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm temp_SALTMARSH: logged temperature. Units = degrees Celsius level_surface_SALTMARSH: water level relative to soil surface at location of logger. Unit = m level_mAHD_SALTMARSH: water level relative to Australian Height Datum (AHD). Unit = m File: dat_groundwater_CI_supratidal_forest.csv Description: water level and salinity dataset collected within a groundwater standpipe located within seaward fringe and interior zones of supratidal forest at Corner Inlet (CI) study site n/a = not available Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm level_surfaceFRINGE*:* water level relative to soil surface at location of logger. Unit = m level_mAHD_FRINGE: water level relative to Australian Height Datum (AHD). Unit = m temp_FRINGE: logged temperature. Units = degrees Celsius EC_FRINGE: electrical conductivity (EC) of porewater. Units = µS/cm SC_FRINGE: specific conductance (SC) of porewater. Units = µS/cm at 25 degrees C salinity_FRINGE: salinity of porewater. Units = PSU level_surface_INTERIOR: water level relative to soil surface at location of logger. Unit = m level_mAHD_INTERIOR: water level relative to Australian Height Datum (AHD). Unit = m temp_INTERIOR: logged temperature. Units = degrees Celsius EC_INTERIOR: electrical conductivity (EC) of porewater. Units = µS/cm SC_INTERIOR: specific conductance (SC) of porewater. Units = µS/cm at 25 degrees C salinity_INTERIOR: salinity of porewater. Units = PSU File: dat_groundwater_CI_mangrove.csv Description: water level dataset collected within a groundwater standpipe located within central zone of mangrove at Corner Inlet (CI) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm temp_MANGROVE: logged temperature. Units = degrees Celsius level_surface_MANGROVE: water level relative to soil surface at location of logger. Unit = m level_mAHD_MANGROVE: water level relative to Australian Height Datum (AHD). Unit = m File: dat_surface_BC_mangrove.csv Description: water level dataset collected on surface within central zone of mangrove at Berowra Creek (BC) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Temp: logged temperature. Units = degrees Celsius Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_surface_BC_supratidal.csv Description: water level dataset collected on surface within fringe zone of supratidal forest at Berowra Creek (BC) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Temp: logged temperature. Units = degrees Celsius Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_surface_MINN_interior.csv Description: water level dataset collected on surface within interior zone of supratidal forest at Minnamurra River (MINN-FP) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Temp: logged temperature. Units = degrees Celsius Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_surface_BC_saltmarsh.csv Description: water level dataset collected on surface within central zone of saltmarsh at Berowra Creek (BC) study site n/a = not available Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Temp: logged temperature. Units = degrees Celsius Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_surface_MINN_fringe.csv Description: water level dataset collected on surface within fringe zone of supratidal forest at Minnamurra River Rocklow Creek (MINN-RC) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Temp: logged temperature. Units = degrees Celsius Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_surface_MINN_mangrove.csv Description: water level dataset collected on surface within central zone of mangrove at Minnamurra River Rocklow Creek (MINN-RC) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Temp: logged temperature. Units = degrees Celsius Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_surface_MINN_saltmarsh.csv Description: water level dataset collected on surface within central zone of saltmarsh at Minnamurra River Rocklow Creek (MINN-RC) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Temp: logged temperature. Units = degrees Celsius Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_surface_SWAN-I.csv Description: water level dataset collected on surface within fringe zone of supratidal forest at Swan River Intermediate (SWAN-I) study site Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm Depth_m: water depth above logger positioned on soil surface. Unit = m File: dat_groundwater_MINN-FP.csv Description: water level and salinity dataset collected within a groundwater standpipe located within seaward fringe (_FRINGE) and interior (_INTERIOR) zones of supratidal forest at Minnamurra River Floodplain (MINN-FP) study site n/a = not available Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm temp_FRINGE: logged temperature. Units = degrees Celsius EC_FRINGE: electrical conductivity (EC) of porewater. Units = mS/cm SC_FRINGE: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_FRINGE: salinity of porewater. Units = PSU level_mAHD_FRINGE: water level relative to Australian Height Datum (AHD). Unit = m level_surface_FRINGE: water level relative to soil surface at location of logger. Unit = m temp_INTERIOR: logged temperature. Units = degrees Celsius EC_INTERIOR: electrical conductivity (EC) of porewater. Units = mS/cm SC_INTERIOR: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_INTERIOR: salinity of porewater. Units = PSU level_mAHD_INTERIOR: water level relative to Australian Height Datum (AHD). Unit = m level_surface_INTERIOR: water level relative to soil surface at location of logger. Unit = m File: dat_groundwater_TIL-F.csv Description: water level and salinity dataset collected within a groundwater standpipe located within central zones of (1) saltmarsh of Salicornia quinqueflora (SMSAL); (2) saltmarsh dominated by Juncus kraussii (SMJUNC); (3) saltmarsh dominated by Phragmites australis (SMPHRAG); and (4) supratidal forest dominated by Melaleuca ericifolia at Tilba Tilba Lake Fluvial (TIL-F) study site n/a = not available Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm temp_SMSAL: logged temperature. Units = degrees Celsius EC_SMSAL: electrical conductivity (EC) of porewater. Units = mS/cm SC_SMSAL: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_SMSAL: salinity of porewater. Units = PSU level_mAHD_SMSAL: water level relative to Australian Height Datum (AHD). Unit = m level_surface_SMSAL: water level relative to soil surface at location of logger. Unit = m temp_SMJUNC: logged temperature. Units = degrees Celsius EC_SMJUNC: electrical conductivity (EC) of porewater. Units = mS/cm SC_SMJUNC: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_SMJUNC: salinity of porewater. Units = PSU level_mAHD_SMJUNC: water level relative to Australian Height Datum (AHD). Unit = m level_surface_SMJUNC: water level relative to soil surface at location of logger. Unit = m temp_SMPHRAG: logged temperature. Units = degrees Celsius EC_SMPHRAG: electrical conductivity (EC) of porewater. Units = mS/cm SC_SMPHRAG: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_SMPHRAG: salinity of porewater. Units = PSU level_mAHD_SMPHRAG: water level relative to Australian Height Datum (AHD). Unit = m level_surface_SMPHRAG: water level relative to soil surface at location of logger. Unit = m temp_SUPRATIDAL_FOREST: logged temperature. Units = degrees Celsius EC_SUPRATIDAL_FOREST: electrical conductivity (EC) of porewater. Units = mS/cm SC_SUPRATIDAL_FOREST: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_SUPRATIDAL_FOREST: salinity of porewater. Units = PSU File: dat_groundwater_TP.csv Description: water level and salinity dataset collected within groundwater standpipes located within central zones of (1) mangrove of *Avicennia marina *(MANGROVE); (2) saltmarsh dominated by Salicornia quinqueflora and Sporobolus virginicus (SMSASP); (3)saltmarsh dominated by Juncus kraussii (SMJUNC); and (4) supratidal forest dominated by Casuarina glauca at Towra Point (TP) study site n/a = not available Variables Date_Time: Date and time of record in local time zone (no daylight savings). Date Format = dd/mm/yyyy. Time format = hh:mm level_surface_MANGROVE: water level relative to soil surface at location of logger. Unit = m level_mAHD_MANGROVE: water level relative to Australian Height Datum (AHD). Unit = m temp_MANGROVE: logged temperature. Units = degrees Celsius SC_MANGROVE: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_MANGROVE: salinity of porewater. Units = PSU level_surface_SMSASP: water level relative to soil surface at location of logger. Unit = m level_mAHD_SMSASP: water level relative to Australian Height Datum (AHD). Unit = m temp_SMSASP: logged temperature. Units = degrees Celsius SC_SMSASP: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_SMSASP: salinity of porewater. Units = PSU level_surface_SMJUNC: water level relative to soil surface at location of logger. Unit = m level_mAHD_SMJUNC: water level relative to Australian Height Datum (AHD). Unit = m temp_SMJUNC: logged temperature. Units = degrees Celsius SC_SMJUNC: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_SMJUNC: salinity of porewater. Units = PSU level_surface_SUPRATIDAL_FOREST: water level relative to soil surface at location of logger. Unit = m level_mAHD_SUPRATIDAL_FOREST: water level relative to Australian Height Datum (AHD). Unit = m temp_SUPRATIDAL_FOREST: logged temperature. Units = degrees Celsius SC_SUPRATIDAL_FOREST: specific conductance (SC) of porewater. Units = mS/cm at 25 degrees C salinity_SUPRATIDAL_FOREST: salinity of porewater. Units = PSU, [Code/software] datasets can be analysed in any package or software which supports .csv files Access information Other publicly accessible locations of the data: NIL Data was derived from the following sources: NIL, [Methods] This study collates datasets collected between 2017 and 2023 from 18 study sites spanning a diversity of settings along these coastlines. Within each setting, sites were prioritised where there was adjoining saltmarsh and/or mangrove communities, to allow comparative analyses with either existing or new datasets of those communities. Therefore, our collation of study sites may be under representative of supratidal forests occurring in isolation from intertidal mangrove and saltmarsh, and should not be considered a systematic or comprehensive coverage of forest settings. The measured parameters, measurement frequency and sampling designs varied among study locations according to their specific data collection objectives., [Water level and salinity] Groundwater standpipes were installed opportunistically across five sites (TP, MIN-FP, TIL-F, CI and ONK) to enable continuous logging of water level and/or salinity concentrations and explore the role of tidal inundation and rainfall on these parameters across diverse geomorphic settings. Standpipes were constructed from PVC tubes (1.5 m long; 50mm diameter) perforated with small (2mm) holes and covered with a nylon stocking to minimise infilling by sediments. Standpipes were installed into an augured hole (~50mm diameter with any gaps around the pipes backfilled using extracted sediments) to a depth of approximately 120 cm, enabling data-logging sensors to be suspended at a depth of 100 cm below the wetland surface. Exceptions to this included the mangrove standpipe at TP (sensors at 50cm depth below surface) and the supratidal forest standpipe at ONK (salinity sensor at 87 cm; water level sensor at 69 cm). In all cases, standpipe perforations were limited to sections below the wetland surface. Water table level and groundwater salinity measurements were taken at 15-minute intervals using either integrated loggers with pressure, temperature and electrical conductivity sensors (SOLINST LTC Edge), or via the simultaneous deployment of individual pressure (HOBO U20-001-04) and temperature / electrical conductivity sensors (HOBO U24-002-C). Surface water-level loggers were deployed at BC, MIN-RC and MIN-FP sites to capture variations in surface inundation across adjacent mangrove, saltmarsh and supratidal forest communities at the scale of individual spring tide cycles. A surface logger was deployed on the surface of fringing supratidal forest at SWAN-I to capture inundation associated with either tides and/or seasonal rainfall at this location. At each site, an additional pressure logger (HOBO U20-001-04) was deployed in a tree well above the inundation limit to enable correction for atmospheric pressure and determination of water depths. Trimble R8s and R10 real-time kinematic global positioning systems (horizontal error < 8 mm; vertical error < 15 mm) was used to survey the location and elevation of each water level measurement location. Where canopy coverage hindered the use of these instruments (i.e. supratidal forests at TP and MIN-FP) elevation was estimated via manual survey from an adjacent open area using a Leica Sprinter 50 Digital Level. In both instances, elevation is reported in the Australian Height Datum (AHD), where 0 m AHD corresponds to an estimate of mean sea level across Australian coastal waters., [Biomass survey] Field vegetation surveys were carried out at 11 sites to assess variations in forest structure and aboveground biomass across study settings. Replicate survey plots were measured within each site, with plot sizes ranging from 12.5 to 400 m2 depending on the density and homogeneity of vegetation at the site. Within each quadrat all trees > 1.3 m height were measured for diameter at breast height (DBH; Casuarinas) or diameter at 10 cm (D10; Melaleucas) to enable biomass estimation following genus-specific allometric equations created by Paul et al. (2013). The inclusion of such low-statured individuals is consistent with the definition of Australia’s forests as vegetation with mature or potentially mature stand height exceeding 2 metres (ABARES 2023). Biomass values for BC, BUTCK, LES and TP are updated estimates, including new additional plots, of those reported in Kelleway et al (2021). Biomass values are presented as dry weight estimates (Mg DW ha^-1^) and were converted to aboveground carbon stock estimates using a conversion factor of 0.488 (Kelleway et al. 2021) to enable calculation of total ecosystem carbon stocks., [Belowground carbon stocks] Soil cores were collected from one or more supratidal forest plots at 12 sites to assess variations in belowground carbon stocks among settings. Core barrels of either aluminium (74 mm internal diameter) or PVC (82 cm internal diameter) were manually hammered into the ground with care taken to minimise compaction. Compaction was estimated by measuring the difference in elevation of the soil surface within the core barrel and the outer soil surface. A linear compaction correction factor was later applied along the length of each core based on these measures. In the laboratory, soil cores were sub-sampled at compaction-corrected depth intervals of ≤ 5 cm along the entire length of the core. Bulk soil was oven dried at 60°C until constant mass and weighed to determine dry bulk density and then homogenized and ground into a fine powder. Samples from BC, TP, KWP, QI, RHYLL, OYST-M, WIL-F and NORN-F were assessed for organic carbon (%Corg) via an elemental analyser, following removal of carbonates with HCl digestion, where required. Due to resource constraints, all other samples were assessed via loss-on-ignition (LOI) at 550oC for 4 h (Heiri et al., 2001). For these samples %Corg estimates were derived from organic matter concentrations using a previously developed empirical relationship for Casuarina samples (Kelleway et al. 2021), or a new empirical relationship developed for Melaleuca sites from samples subjected to both LOI and elemental analyses (Appendix S1: Figure S1). Soil carbon stocks were estimated for 0-30 cm, 0-100 cm and 100-200 cm depth ranges where core depths allowed. Stock estimates for BC, BUTCK, CRB, LES and TP were previously reported in Kelleway et al. (2021)., [Belowground decomposition] We used a modified tea bag index protocol as part of the global TeaComposition H2O program (Trevathan-Tackett et al. 2021) at two study locations (BC and TP) to compare long-term belowground organic matter decomposition in supratidal forest relative to adjacent intertidal mangrove and saltmarsh ecosystems. We monitored the biomass loss of standardised litters: green tea (Lipton; EAN 87 22700 05552 5) and rooibos tea (Lipton; EAN 87 22700 18843 8), which are proxies for more labile and more recalcitrant plant organic matter types, respectively (Trevathan-Tackett et al. 2024). The green tea contains a higher proportion of water soluble compounds (simple sugars and phenolics), while the rooibos tea consists of a higher proportion of acid insoluble compounds (e.g. lignin; Keuskamp et al. 2013). At T0 (December 2017), all pre-weighed tea bags were buried at approximately 15 cm depth. Four tea bags per plot were manually retrieved (2x green; 2x rooibos) from each site, at each of 3, 12, 24, 36 month intervals after initial deployment, though two of the 64 samples could not be found. Tea bags were rinsed in distilled water to remove attached soil, and any visible root in-growth was carefully removed prior to drying of samples (60 °C until constant mass), and re-weighing., In this study we report on new datasets of vegetation structure, carbon cycling parameters, inundation and salinity patterns across 18 sites spanning more than 4,000 km of Australia’s temperate coastlines. We report site-specific ecosystem carbon stocks ranging from 169 to 635 Mg Corg ha-1, with mean aboveground biomass (134 ± 63 Mg DW ha-1) and belowground carbon stocks to 1 m soil depth (193 ± 98 Mg Corg ha-1), which are within the range of national estimates for mangrove and saltmarsh ecosystems. While there are variations in vegetation structure between sites dominated by the genera Melaleuca and Casuarina, this does not lead to discernible differences in above- or belowground carbon stocks. Organic matter decomposition trends within supratidal forest substrates were similar to adjacent mangrove and saltmarsh, though there were differences among study sites and between labile versus recalcitrant tea litters. Soil-atmospheric flux measurements conducted at one site were also within the range of adjacent blue carbon ecosystems. We hypothesise that the high degree of preservation of belowground carbon and low soil-atmosphere flux of greenhouse gases is driven by a combination of infrequent surface inundation, high water tables and typically saline groundwater in supratidal forests, as measured across multiple settings. Supratidal forests are carbon-rich ecosystems influenced by coastal processes associated with tidal inundation. While further research is required to understand the full distribution, carbon cycling and abiotic drivers of supratidal forests, our findings strongly support their inclusion in blue carbon and other management initiatives that support the response and recovery of these endangered ecological communities in a time of change., Peer reviewed

Peer reviewed

The document, in Excel format, contains: Table S1.1. Phenotypic and genomic drug resistance profiles of MTBC samples collected in the Comunitat Valenciana between 2014 and 2016 Table S1.2. Isoniazid and rifampicin Minimum Inhibitory Concentrations of samples with discrepancies in phenotype-genotype resistance profiles, Supplementary Tables generated for the first chapter of the thesis titled 'Novel whole-genome sequencing approaches to study drug resistance, transmission, and evolution of the Mycobacterium tuberculosis complex'. Author: Ana María García Marín. Insituto de Biomedicina de Valencia (IBV-CSIC) and Universitat de València., Supplementary_Tables_Thesis_AMGM_Chapter1.xlsx, Peer reviewed

The document, in Excel format, contains: Table S2.1. Basic statistics of long- and short-read sequencing of MTBC isolates collected in the Comunitat Valenciana in 2016 Table S2.2. Quality control of individual MTBC complete genomes, Supplementary Tables generated for the second chapter of the thesis titled 'Novel whole-genome sequencing approaches to study drug resistance, transmission, and evolution of the Mycobacterium tuberculosis complex'. Author: Ana María García Marín. Insituto de Biomedicina de Valencia (IBV-CSIC) and Universitat de València., Supplementary_Tables_Thesis_AMGM_Chapter_2.xlsx, Peer reviewed

The widespread use of pesticides is increasing the presence of environmental contaminants with potential impacts on biodiversity, ecosystems, and human health. Although long-term pesticide effects have been previously studied, the long-term impact of an acute pesticide exposure during critical early developmental periods remains poorly understood. Here, we used zebrafish to examine whether acute exposure to a pesticide mixture at 0.5 μg/L (the maximum allowed in drinking water) during the first 5 days post-fertilisation (dpf) of development has lasting effects at 28 dpf. Zebrafish were assessed behaviourally, morphologically, and molecularly both immediately after exposure at 5 dpf and later at 28 dpf. Our results show alterations in stress-response that start to emerge right after the developmental exposure and are associated with a less anxious-like phenotype at juvenile stages. Interestingly, despite the observed behavioural phenotype at 28 dpf, it did not lead to significant molecular changes in the hypothalamic-pituitary-interrenal (HPI) axis at this stage. On the contrary, a positive control group of juvenile fish subjected to a sustained pesticide exposure throughout the 28 dpf showed both reduced anxiety-like behaviour and HPI alterations. Our study suggests that even an acute exposure to a low-concentration of pesticides during critical developmental periods can result in enduring behavioural changes, Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Montpellier Université d'Excellence,MUSE-Pestifish 2017, Nicola Marchi, HORIZON EUROPE Marie Sklodowska-Curie Actions, 101153110, Isabel Forner-Piquer, ANSES-OptoFish, ANSES-OptoFish, Nicola Marchi, National Institute of Health, NIH U01 DA044400-03, Caroline H. Brennan, Ministerio de Ciencia, Innovación y Universidades, RYC2021-034012-I, Jose Vicente Torres-Perez, British Pharmacological Society, 2023 Pickford Award, Jose Vicente Torres-Perez, IFP was supported by MUSE-Pestifish 2017 (Programme for Excellence of the University of Montpellier 2017 to NM), IFP also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (agreement No 101153110). NM is supported by an ANSES-OptoFish. CHB is supported by National Institute of Health (NIH U01 DA044400-03). JVTP is funded by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI/https://doi.org/10.13039/501100011033) and the European Union “NextGenerationEU”/PRTR with a Ramón y Cajal contract (Grant RYC2021-034012-I); JVTP is also supported by the 2023 Pickford Award from the British Pharmacological Society, This work contributes to the Institut de Ciències del Mar "Severo Ochoa Centre of Excellence" accreditation CEX2024-001494-S funded by AEI 10.13039/501100011033 of the Spanish Ministry of Science and Innovation, Peer reviewed

Database of references used to the summarize of the main physiographic, the Quaternary depositional architecture characteristics and the chronostratigraphic boundaries of the Iberian continental margins, This study was funded by the following projects: The Betic-Alboran-Rif active shear zone and cascading geohazards: faults, folds, seismicity, partially to fully submerged landslides and tsunamis-BARACA (PID2022-136678NB-I00 financed by MICIU/AEI 10.13039/501100011033 and FEDER, UE); and EMODNET Ingestion and Safe-keeping of Marine Data-III EASME/EMFF/2018/1.3.1.8/01/SI2.810021, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

Ministerio de Ciencia, Innovación y Universidades: Cryo-electron microscopy: from the resolution revolution to the new drug design revolution PID2022-137548OB-I00; Ministerio de Ciencia, Innovación y Universidades: Digital image processing in cryomicroscopy: breaking down barriers (DigitalBreak) TED2021-132748B-I00, Peer reviewed

[Aim:] Assess the potential effects of climate change on different forest habitats, and outline a climate-wise conservation network to sustain forest connectivity under current and future climates., [Location:] Mainland Spain., [Time period:] Current and future (2071-2100)., [Major taxa studied:] Forest species associated with different vegetation types and dispersal abilities., [Methods:] We fitted a Random Forest model to predict the distribution of six vegetation types under current and four future climate scenarios. We then assessed forest availability and connectivity for each climate scenario and vegetation type. To define the conservation network we identified the key habitat patches and corridors for dynamic connectivity using multi-temporal habitat availability indices. Finally, we analyzed how much of this conservation network is expected to change due to climate shifts and how this aligns with existing protected areas., [Results:] Forests across all vegetation types exhibited substantial northward, eastward, and upward shifts. Forests adapted to cold or wet conditions (e.g., deciduous, mountain conifers, and high-mountain vegetation) declined in area and connectivity. Warm- and dry-adapted forests (e.g., sclerophyllous, subsclerophyllous, and hyperxerophilous vegetation) increased their ranges and connectivity. The increase in subsclerophyllous area tripled in connectivity gain, whereas other vegetation increased area and connectivity at similar rates (differences below 15%). 53% of the proposed conservation network shifted vegetation types. Current protected areas covered 45% of the conservation patches across Spain but less than 7% within the deciduous vegetation type., [Main conclusions:] Projected shifts highlight the need for dynamic connectivity analyses to guide effective conservation under changing climates. Forest types exhibited distinct trends, underscoring the need for tailored strategies for each type. The proposed conservation network provides guidance for a proactive enhancement of forest species resilience, and serve as reference for other countries with similar conservation targets, such as the international EU Restoration Law and Biodiversity Strategy., Ministerio de Ciencia e Innovación (Agencia Estatal de Investigación) and FEDER Una manera de hacer Europa: PID2021-124187NB-I00 Ministerio de Ciencia e Innovación (Agencia Estatal de Investigación) and Unión Europea NextGenerationEU/PRTR: TED2021-129589B-I00, Peer reviewed

Additional file 3: Table S3. Characteristics and classification of constitutional (germline) POLE and POLD1 exonuclease domain missense variants., Agencia Estatal de Investigación; Instituto de Salud Carlos III; Agència de Gestió d'Ajuts Universitaris i de Recerca Generalitat de Catalunya, Peer reviewed

E.R.E. was supported through NSF award OCE–1326927. L.M.’s PhD scholarship is supported by Geoscience Australia. W.M. is supported by the H2020 MSCA-IF – TURBOMUD project – GA No. 101018321. Support for E. Estes was provided by NSF grants OCE1326927 (IODP JRSO) and OCE2412279 (IODP Closeout), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed