Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The numbers used in the climate zone map don't have a title or legend. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.This is explained in the Title 24 energy efficiency standards glossary section:"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year.""The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone.

The Energy Commission has developed this app to quickly and accurately show addresses and locations to determine California’s climate regions. We invite builders and building officials to use this app to determine the climate zones applicable to building projects.Please note:Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.This is explained in the Title 24 energy efficiency standards glossary section:"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year.""The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone."Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is:CZ 1: ArcataCZ 2: Santa RosaCZ 3: OaklandCZ 4: San Jose-ReidCZ 5: Santa MariaCZ 6: TorranceCZ 7: San Diego-LindberghCZ 8: FullertonCZ 9: Burbank-GlendaleCZ10: RiversideCZ11: Red BluffCZ12: SacramentoCZ13: FresnoCZ14: PalmdaleCZ15: Palm Spring-IntlCZ16: Blue CanyonThe original detailed survey definitions of the 16 Climate Zones are found in the 1995 publication, "California Climate Zone Descriptions for New Buildings."

Analysis of ‘California Building Climate Zones’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4c0d938e-1d8f-432c-b84d-334c796aa6bb on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone.

The numbers used in the climate zone map don't have a title or legend. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.

This is explained in the Title 24 energy efficiency standards glossary section:

"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year."

"The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone.

"Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is:

• CZ 1: Arcata
• CZ 2: Santa Rosa
• CZ 3: Oakland
• CZ 4: San Jose-Reid
• CZ 5: Santa Maria
• CZ 6: Torrance
• CZ 7: San Diego-Lindbergh
• CZ 8: Fullerton
• CZ 9: Burbank-Glendale
• CZ10: Riverside
• CZ11: Red Bluff
• CZ12: Sacramento
• CZ13: Fresno
• CZ14: Palmdale
• CZ15: Palm Spring-Intl
• CZ16: Blue Canyon

The original detailed survey definitions of the 16 Climate Zones are found in the 1995 publication, "California Climate Zone Descriptions for New Buildings."

--- Original source retains full ownership of the source dataset ---

Please note:Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.

Contained within 3rd Edition (1957) of the Atlas of Canada is a map that shows the division of Canada into climatic regions according to the classification of the climates of the world developed by W. Koppen. Koppen first divided the world into five major divisions to which he assigned the letters A, B, C, D, and E. The letters represent the range of divisions from tropical climate (A) to polar climate (E). There are no A climates in Canada. The descriptions of the four remaining major divisions are given in the map legend. Koppen then divided the large divisions into a number of climatic types in accordance with temperature differences and variations in the amounts and distribution of precipitation, on the basis of which he added certain letters to the initial letter denoting the major division. The definitions of the additional letters which apply in Canada are also given when they first appear in the map legend. Thus b is defined under Csb and the definition is, therefore, not repeated under Cfb, Dfb or Dsb. For this map, the temperature and precipitation criteria established by Koppen have been applied to Canadian data for a standard thirty year period (1921 to 1950 inclusive).

description: The California Basin Characterization Model (CA-BCM 2014) dataset provides historical and projected climate and hydrologic surfaces for the region that encompasses the state of California and all the streams that flow into it (California hydrologic region ). The CA-BCM 2014 applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid. The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region. The historical data is based on 800m PRISM data spatially downscaled to 270 m using the gradient-inverse distance squared approach (GIDS), and the projected climate surfaces include five CMIP-3 (GFDL, PCM, MIROC3_2, CSIRO, GISS_AOM) and nine CMIP-5 (MIROC5, MIROC , GISS, MRI, MPI, CCSM4, IPSL, CNRM, FGOALS) General Circulation Models under a range of emission scenarios or representative concentration pathways (RCPs) for a total of 18 futures that have been statistically downscaled using BCSD to 800 m and further downscaled using GIDS to 270 m for model application. The BCM approach uses a regional water balance model based on this high resolution precipitation and temperature as well as elevation, geology, and soils to produce surfaces for the following variables: precipitation, air temperature, recharge, runoff, potential evapotranspiration (PET), actual evapotranspiration, and climatic water deficit, a parameter that is calculated as PET minus actual evapotranspiration. The following data are available in this archive: Raw, monthly model output for historical and future periods. Projected data is available for the following GCM and emission scenario or RCP combinations: GFDL-B1, GFDL-A2 PCM-B1, PCM-A2 MIROC3_2-A2 CSIRO-A1B GISS_AOM-A1B, MIROC5-RCP2.6, MIROC-RCP4.5, MIROC-RCP6.0, MIROC-RCP8.5 GISS-RCP2.6, MRI-RCP2.6, MPI- RCP4.5, CCSM4-RCP8.5, IPSL-RCP8.5, CNRM-RCP8.5, FGOALS-RCP8.5. Data variables: Actual evapotranspiration - water available between wilting point and field capacity, mm (aet); Climatic water deficit - Potential minus actual evapotranspiration, mm (cwd); Maximum monthly temperature, degrees C - (tmx); Minimum monthly temperature, degrees C - (tmn); Potential evapotranspiration - Water that could evaporate or transpire from plants if available, mm (pet); Recharge - Amount of water that penetrates below the root zone, mm (rch); Runoff - Amount of water that becomes stream flow, mm (run); Precipitation, mm - (ppt). Note that another archive, hosted by the California Climate Commons contains various climatological summaries of these data. That archive can be found at: http://climate.calcommons.org/; abstract: The California Basin Characterization Model (CA-BCM 2014) dataset provides historical and projected climate and hydrologic surfaces for the region that encompasses the state of California and all the streams that flow into it (California hydrologic region ). The CA-BCM 2014 applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid. The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region. The historical data is based on 800m PRISM data spatially downscaled to 270 m using the gradient-inverse distance squared approach (GIDS), and the projected climate surfaces include five CMIP-3 (GFDL, PCM, MIROC3_2, CSIRO, GISS_AOM) and nine CMIP-5 (MIROC5, MIROC , GISS, MRI, MPI, CCSM4, IPSL, CNRM, FGOALS) General Circulation Models under a range of emission scenarios or representative concentration pathways (RCPs) for a total of 18 futures that have been statistically downscaled using BCSD to 800 m and further downscaled using GIDS to 270 m for model application. The BCM approach uses a regional water balance model based on this high resolution precipitation and temperature as well as elevation, geology, and soils to produce surfaces for the following variables: precipitation, air temperature, recharge, runoff, potential evapotranspiration (PET), actual evapotranspiration, and climatic water deficit, a parameter that is calculated as PET minus actual evapotranspiration. The following data are available in this archive: Raw, monthly model output for historical and future periods. Projected data is available for the following GCM and emission scenario or RCP combinations: GFDL-B1, GFDL-A2 PCM-B1, PCM-A2 MIROC3_2-A2 CSIRO-A1B GISS_AOM-A1B, MIROC5-RCP2.6, MIROC-RCP4.5, MIROC-RCP6.0, MIROC-RCP8.5 GISS-RCP2.6, MRI-RCP2.6, MPI- RCP4.5, CCSM4-RCP8.5, IPSL-RCP8.5, CNRM-RCP8.5, FGOALS-RCP8.5. Data variables: Actual evapotranspiration - water available between wilting point and field capacity, mm (aet); Climatic water deficit - Potential minus actual evapotranspiration, mm (cwd); Maximum monthly temperature, degrees C - (tmx); Minimum monthly temperature, degrees C - (tmn); Potential evapotranspiration - Water that could evaporate or transpire from plants if available, mm (pet); Recharge - Amount of water that penetrates below the root zone, mm (rch); Runoff - Amount of water that becomes stream flow, mm (run); Precipitation, mm - (ppt). Note that another archive, hosted by the California Climate Commons contains various climatological summaries of these data. That archive can be found at: http://climate.calcommons.org/

The California desert occupies the southeastern 27 percent of California (11,028,300 ha, 110,283 km2 or 27,251,610 ac). It includes two ecoregional provinces comprised of five desert regions ("ecological sections"; Miles and Goudy 1997). The American Semi-Desert and Desert Province (warm deserts) includes the Mojave Desert, Sonoran Desert, and Colorado Desert sections in the southern 83 percent of the California desert. The Intermountain Semi-Desert Province (cold deserts) includes the Southeastern Great Basin and Mono sections in the northern 17 percent of the region. Previous analyses of fire patterns across the California desert have used point occurrence data. Point occurrence data can have limitations because they can: (1) represent the containment area rather than actual fire area; (2) extend to include unburned areas as contiguous within the fire boundary; (3) be incomplete and estimated before the end of burning; and (4) be reported only in public agency boundaries. Point data also often contain errors associated with the initial recording, or subsequent transcription from paper to electronic records, of the point of origin of a fire. Point datasets also can contain redundancies, such as the same fire being reported by multiple responding agencies that can affect derived statistics such as fire area. Additionally, because points are one dimensional, the area they conceptually represent cannot be readily parsed using other spatial data (e.g. by desert regions and/or ecological zones). More accurate, detailed, and spatially-explicit fire data are available using Landsat satellite imagery from the Monitoring Trends in Burn Severity (MTBS) program. We used these data to precisely document fire area (area within fire perimeters) for fires greater than or equal to 405 ha (1,000 ac) between 1984 and 2013 in the California desert (www.mtbs.gov; accessed 6/30/2015). Previous fire analyses have also stratified analyses by ecological zones derived from 4 Kuchler potential vegetation types (barren, desert shrub, juniper-pinyon, sagebrush). That approach does not distinguish how the relative proportions of vegetation types comprising each ecological zone varies among California desert regions, or explain how the ecotones between the zones shift upslope with decreasing latitude moving from the cold deserts in the north to the warm deserts in the south. These limitations hinder their application to specific areas within the desert bioregion. We derived ecological zones derived from 43 LANDFIRE vegetation biophysical setting types, plus various non-wildland (e.g. developed urban/agriculture/roads) and non-burnable (e.g. open water/barren) areas (Rollins 2009). We also omitted from analyses non-wildland and non-burnable areas (2,003,148 ha [4,949,887 ac]), and focused instead on the remaining burnable wildland areas (9,025,152 ha [22,301,636 ac]). The 43 biophysical setting types were grouped into 13 general vegetation types, which were further grouped into four elevation-based ecological zones plus one riparian zone according to their constituent plant associations. The resulting 5 ecological zones were then intersected with the boundaries of the 5 desert regions of the California to create a map and associated burnable wildland area statistics. A diagram was also created illustrating the relative elevational positions of each ecological zone and vegetation type along a latitudinal gradient from cold deserts to warm deserts. These data were developed to assess the distribution of wildfire regimes across California deserts for the chapter "Southeast Deserts Bioregion" in the book "Fire in California's Ecosystems, Second Edition" published by University of California Press. Miles, S. R. and C. B. Goudy. 1997. Ecological subregions of California: section and subsection descriptions. USDA Forest Service, Pacific Southwest Region, R5-EM-TP-005, San Francisco, CA. Rollins Matthew G. (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18, 235-249.

Fisheries Management Zones have replaced former Fishing Divisions and are the geographic basis for managing, monitoring, assessing and regulating recreational fisheries for a majority of lakes and streams in a zone.Each zone is based on angler usage and ecological/geographic patterns such as climate zones, watersheds and road networks.Additional DocumentationFisheries Management Zone - Data Description (PDF)Fisheries Management Zone - Documentation (Word)View - Fisheries Management Zone - Internal Ontario Public Service use only.StatusCompleted: production of the data has been completedMaintenance and Update FrequencyAs needed: data is updated as deemed necessaryContactMae Rannells-Warren, Fisheries Section, Fish and Wildlife Policy Branch, mae.rannells-warren@ontario.ca

This study examined the use of zoned ventilation systems using a coupled CONTAM/EnergyPlus model for new California dwellings. Several smart control strategies were developed with a target of halving ventilation-related energy use, largely through reducing dwelling ventilation rates based on zone occupancy. The controls were evaluated based on the annual energy consumption relative to continuously operating non-zoned, code-compliant mechanical ventilation systems. The systems were also evaluated from an indoor air quality perspective using the equivalency approach, where the annual personal concentration of a contaminant for a control strategy is compared to the personal concentration that would have occurred using a continuously operating, non-zoned system. Individual occupant personal concentrations were calculated for the following contaminants of concern: moisture, CO2, particles and a generic contaminant. Zonal controls that saved energy by reducing outside airflow achieved typical reductions in ventilation-related energy of 10 to 30%, compared to the 7% savings from the unzoned control. However, this was at the expense of increased personal concentrations for some contaminants in most cases. In addition, care is required in the design and evaluation of zonal controls, because control strategies may reduce exposure to some contaminants while increasing exposure to others.

Methods This data file represents summary results for 2,967 annual co-simulations of EnergyPlus and CONTAM at one-minute time-steps, including data outputs addressing zone and whole-dwelling ventilation airflows (fan flows, infiltration, exfiltration), zone and personal contaminant concentrations (PM2.5, CO2, Generic and water vapor), and energy use. Each simulation represents a unique combination of California climate zone (CEC CZ 1, 3, 10 and 16), building prototype (apartment, 1-story and 2-story), envelope air leakage (0.6, 2 and 3 ACH50), ventilation fan type/configuration (multi- and single-point configurations of exhaust, supply and balanced fans), and ventilation control type (10 smart control types and 2 baseline types). Detailed descriptions of the simulation effort can be found in the project final report. Annual, one-minute time-series outputs were recorded from EnergyPlus, and each time-series file was post-processed to produce a one-line summary of the results. All data pre- and post-processing is characterized in detail at the project bitbucket repository available here.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022).

These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data.

Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period.

Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented.

Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region.

One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold).

The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock.

Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

This data publication includes spatial data for 119 contemporary reference sites within the yellow pine and mixed-conifer zone of the western Sierra Nevada ecoregion as of 2018-2020. This ecoregion encompasses eastern and central California. Fire occurrence, fire severity, and management history datasets were used to identify and delineate the contemporary reference site polygons. We provide a set of spatially explicit forest structure metrics derived from high fidelity airborne lidar data for reference sites where concurrent lidar data were available. We also provide a set of forest structure metrics developed by the California Forest Observatory (CFO) to ensure reference data were available for all sites regardless of lidar availability. Vector spatial datasets are provided individually as shapefiles and combined as an OGC geopackage. Raster layers are provided individually as GeoTIFFs. All data are available in an ArcGIS Pro Package file which also includes a file geodatabase of the spatial data and author-defined layers. Also included is a document containing site descriptions (i.e., size, ownership, climatic setting, etc.) and a set of figures showing the distribution of climatic, topographic, and forest structure metrics for all reference sites grouped by dominant climate class as well as for each individual site.Contemporary reference sites in California’s Sierra Nevada represent areas where a low-intensity and frequent fire regime - an integral ecological process in temperate dry forests - has been mostly restored after more than a century of fire suppression. Forest structural patterns in these sites are likely more resilient to future disturbances and climate change since key ecological processes are intact. Forest structure metrics provide descriptions of horizontal and vertical structural patterns that are relevant to managers and ecologists working in the Sierra Nevada ecoregion. Forest structure metrics within reference sites can be used to guide management treatments or support ecological analyses.These data were published on 05/04/2023. On 05/15/2023 we updated the data downloads to include the SNRC_summaries.pdf file. Minor metadata updates, to include reference to newly published articles, were made on 01/17/2024.

• These data are also available as a web map: https://uw.maps.arcgis.com/home/item.html?id=c692d81e11254edc97a0d9d4a81f148e

Stream drying is happening globally, with significant ecological and social consequences. Most examples of stream drying come from systems influenced by dam operations or those with highly exploited aquifers. Stream drying is also thought to be happening due to climate change, but examples are surprisingly limited. We explored flow trends from the five Mediterranean-climate regions with a focus on unregulated streams with long-term gauge records. We found consistent evidence of decreasing discharge trends, increasing zero-flow days, and steeper downward discharge trends in smaller basins. Beyond directional trends, many systems recently shifted flow state, including some streams that shifted from perennial to intermittent flow states. Our analyses provide evidence of stream drying consistent with climate change, but also highlight knowledge gaps and challenges in empirically and statistically documenting flow regime shifts. We discuss the myriad consequences of losing flow and propose strategies for improving detection and adapting to flow change. Methods To document flow change, we compiled gauge records from five Mediterranean-climate regions of the world, including California (U.S.), Chile, South Africa, Spain, and Western Australia. For each gauge, we downloaded daily discharge records from public sources. Next, we limited our analysis to gauges located in Mediterranean-climates zones by retaining the subset of gauges located in Köppen-Geiger climate classes Csa, Csb, Csc (i.e., areas with a dry summer) using maps from Beck et al. 2018. Second, we identified gauges located in minimally disturbed basins. In the US and Australia, we used “reference” gauges identified by the USGS and Bureau of Meteorology, respectively. In South Africa, Chile, and Spain - where reference gauges have not been designated by agencies – we instead used aerial image analysis of upstream watershed conditions to identify basins with no evidence of significant reservoirs or large water infrastructure projects. We note that our determination of “reference-quality” gauges in Spain [excluding Catalonia] is consistent with Messager et al. 2021. Third, we identified gauges with daily data from 1980-2019 (i.e., most recent 40 years in common across the five regions) and no more than one year of missing data. Overall, we identified 158 gauges that met our criteria for inclusion (i.e., Mediterranean-climate, reference-quality, 40 years of data from 1980-2019, and no more than one year of missing data, WebPanel 1, WebFigure1). To reduce noise in zero-flow conditions, we defined “zero flows” as flows < 0.1 cfs. Finally, for our analysis of zero-flow trends, we used a liberal definition of “intermittent” and included the subset of streams with ≥ to 1 day/year of zero-flow on average, i.e., ≥ 40 days across the 40 year study, following Messager et al. 2021. Using the population of gauges that met our criteria for inclusion, we conducted trend analyses on daily discharge (for each gauge in our population) and on the annual number of zero-flow days (for the subset of intermittent gauges) across the time series by means of non-parametric Mann-Kendall tests. We next explored evidence of flow regime shifts. Specifically, we conducted a breakpoint analysis on the zero-flow days per year using the ‘strucchange’ package in R. We constrained the analysis to test for evidence of a maximum of one breakpoint (indicating a state shift).

Climate and land-use change could exhibit concordant effects that favor or disfavor the same species, which would amplify their impacts, or species may respond to each threat in a divergent manner, causing opposing effects that moderate their impacts in isolation. We used early 20th-century surveys of birds conducted by Joseph Grinnell paired with modern resurveys and land-use change reconstructed from historic maps to examine avian change in Los Angeles and California’s Central Valley (and their surrounding foothills). Occupancy and species richness declined greatly in Los Angeles from urbanization, strong warming (+1.8°C) and drying (-77.2 mm), but remained stable in the Central Valley, despite large-scale agricultural development, average warming (+0.9°C), and increased precipitation (+11.2 mm). While climate was the main driver of species distributions a century ago, the combined impacts of land-use and climate change drove temporal changes in occupancy, with similar numbers of species experiencing concordant and opposing effects. Methods We reconstructed an early-20th century ecological baseline using unique historical resources: (i) systematic bird surveys conducted by Joseph Grinnell from 1895–1904 at 71 sites around the Greater Los Angeles metropolitan area where he grew up and in the surrounding foothills (hereafter “Los Angeles”), and in California’s Central Valley and surrounding foothills (hereafter “Central Valley”) with colleagues in the early 1900’s, and (ii) hand-digitized land-use maps from the same period. These were matched with contemporary bird resurveys and measures of land-use and climate change at the same sites. To obtain data on historic localities and bird species occurrences, we reviewed original field notebooks written by Joseph Grinnell and several of his colleagues, which are curated by the Museum of Vertebrate Zoology at UC Berkeley. These field notebooks provide detailed descriptions and maps of survey routes, as well as systematic lists of bird species observed each day. We identified 71 sites (43 Central Valley, 28 Los Angeles) with historic surveys of bird diversity that sampled representative habitats and climates throughout the range of each study region. Bird surveys by Grinnell and colleagues were carefully documented in the form of field notebooks, museum specimens, photographs of sampling sites, and annotated topographic maps. They were occasionally in the form of standardized abundance surveys that were precursors to modern line transects, more often as lists of species encountered each day that provide detection/non-detection data, and rarely as a daily list that identified only species that had not been detected previously. We conducted modern resurveys during the breeding seasons (April through July) of 2015–2017, matching the following characteristics of the historic surveys: geographical location and extent of survey sites, elevational range covered, habitats surveyed within sites, and timing of the survey during the breeding season. We used standardized variable-distance point counts along a 2.25 km transect, with 10 points placed 250 m apart, corresponding as closely as possible to the area and habitats noted by the historic surveyors and indicated by specimen collecting locations. Surveys began at dawn and lasted 2–3 hours. At each point along the transect, we recorded all birds seen or heard during a seven-minute period. Each site was surveyed daily over three consecutive days. Bird counts from modern surveys were collated for each day across all 10 points surveyed along a transect and reduced to detection/nondetection data per day per site for occupancy modeling. We used a dynamic multispecies occupancy model (described in the paper) to estimate the probabilities of occupancy, local colonization, and local persistence between the historic and modern survey periods. R scripts are provided. To characterize climate (long-term average weather conditions often presented as 30-year climate normals), data were obtained from 800 m resolution interpolated maps produced by the PRISM climate group and averaged over 30-year periods corresponding to the historic (1900–1929) and modern (1988–2017) surveys. To quantify land-use change (modern – historic), we created maps of historic land use within 1 km of our bird survey transects by hand‐digitizing historic maps using ArcMap for comparison with modern land-use data obtained from the National Land Cover Database (NLCD) at 100m, 200m, 500m and 1km distances from modern survey points. Water and urban area were hand-digitized from historic USGS topographic maps (ca. 1906–1932). Water bodies were directly outlined as polygons. Urban area was mapped as buildings (area of the building icon on the topographic map plus a buffer of 50 m) and roads (digitized as line features from the topographic map and given a width of 30 m). Historic agriculture was delineated using a series of three maps of irrigated lands in California in 1920.

AbstractNorth America’s coastal mountains are particularly vulnerable to climate change, yet harbour a number of endemic species. With little room “at the top” to track shifting climate envelopes, alpine species may be especially negatively affected by climate-induced habitat fragmentation. We ask how climate change will affect the total amount, mean patch size, and number of patches of suitable habitat for Vancouver Island White-tailed Ptarmigan (Lagopus leucura saxatilis; VIWTP), a threatened, endemic alpine bird. Using a Random Forest model and a unique dataset consisting of citizen science observations combined with field surveys, we predict the distribution and configuration of potential suitable summer habitat for VIWTP under current and future (2020s, 2050s, and 2080s) climates using three general circulation models and two greenhouse gas concentration scenarios. VIWTP summer habitat is predicted to decline by an average of 25%, 44%, and 56% by the 2020s, 2050s, and 2080s, respectively, under the low greenhouse gas scenario and 27%, 59%, and 74% under the high scenario. Habitat patches are predicted to become fragmented into several smaller patches, with a 52-79% reduction in mean patch size. All climate change models and greenhouse gas scenarios depict near total loss of all patches > 1 km2. Most remaining habitat, or climate macro-refugia, will be located in the center of the island. The extent to which ptarmigan will be able to persist in increasingly fragmented habitat is unclear. Much will depend on the ability of ptarmigan to move throughout a more heterogeneous landscape, utilize smaller breeding areas, and survive increasingly variable climate extremes. Our results emphasize the importance of continued monitoring and protection for high elevation specialist species, and suggest that White-tailed Ptarmigan should be considered an indicator species for alpine ecosystems in the face of climate change. Usage notesPredictor variables for all of Vancouver Island (current climate)allVI_fewvars_1980_2010.csv.zip is a compressed grid created for Vancouver Island at 100-m resolution with topographic variables and climate variables downloaded from ClimateBC version 5.03 (http://cfcg.forestry.ubc.ca/projects/climate-data/climatebcwna/#ClimateBC). This file was used to project the Random Forest model for VIWTP habitat suitability across the entire study area of Vancouver Island. The variables are the same as those described for the presabs_both_IndYears_allvars_final.csv file.allvi_fewvars_1980_2010.csv.zipFuture climate variables for all Vancouver IslandallVI_futureclimates.zip is a compressed file including future climate variables (mean summer precipitation, precipitation as snow, and average summer temperature) covering all of Vancouver Island at 100-m resolution (the same resolution as the topographic variables in Rasters.zip). Each file represents future climate variables for one of three general circulation models (Can, CCSM, or GFDL), one of two IPCC emissions scenarios from the AR5 report (RCP 4.5 or RCP 8.5) and one of three future time periods (2020's, 2050's, or 2080's).allVI_futureclimates.zipPresence-absence dataset for VIWTPpresabs_both_IndYears_allvars_final.csv is the final presence-absence dataset for Vancouver Island White-tailed Ptarmigan used in the model. It contains pseudo-absences that were generated using the methods described in the manuscript. It contains topographic and biogeoclimatic zone data from as described in the manuscript and climate variables that were obtained from ClimateBC version 5.03 (http://cfcg.forestry.ubc.ca/projects/climate-data/climatebcwna/#ClimateBC). Description and data format for each variable (column header) is described in the same order as they appear in the presabs_both_IndYears_allvars_final.csv file. (Also see Table 1 in the manuscript for variable descriptions.) [1] FNETID. Unique ID for each observation. [2] pres. Whether the observation is a presence (1) or a pseudo-absence (0). [3] Year. The year the observation was collected. For pseudo-absences, years were assigned randomly based on the prevalence of years in the presence dataset. [4] datatype. Dataset from which the observation originated. PUB = public observation, FS = field survey, NA = pseudo-absence. [5] BCAlbX. BC Environment Albers X coordinate. [6] BCAlbY. BC Environment Albers Y coordinate. [7] BEC. Biogeoclimatic Zone. MH = Mountain Hemlock, AT = Alpine Tundra, CWH = Coastal Western Hemlock. [8] Elevation. Elevation in meters. [9] Aspect. Aspect reclassified according to solar incidence. We transformed aspect as x = -1*cos[Ø(π/180)], where Ø is the aspect measured in degrees. Values ranged from -1 where the angle of solar incidence was lower (north-facing slopes) to 1 where it was higher (south-facing slopes). The importance of aspect depends on slope, so we assigned aspects with slopes of <5° a neutral value of 0. [10] Slope. Slope in degrees. [11] CTI. Compound Topographic...

Sea level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats is not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modelling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of Calidris canutus roselaari (Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for C. c. roselaari. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of C. c. roselaari are predicted to lose — on average — 17.8% of their highly suitable habitat areas, with two sites completely losing all their highly suitable habitat. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of C. c. roselaari (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR. Methods Study area and model species C. c. roselaari are long-distance migratory shorebirds that breed in Alaska (USA) and on Wrangel Island (Russia) during the boreal summer and migrate along the Pacific coast to spend their nonbreeding season in NW Mexico (Carmona et al., 2013). Their nonbreeding habitats are restricted to beaches, coastal lagoons, and deltas spanning 32 - 21° N, although incidental records have been recorded further south. The subspecies has an estimated population size of just 21,700 individuals (Lyons et al., 2016) and is thought to be declining, leading them to be listed as threatened and endangered in Canada and Mexico, respectively (COSEWIC, 2007; SEMARNAT, 2010). Due to their small population size, restricted range, high site fidelity, and specialist habitat requirements, C. c. roselaari are expected to be highly vulnerable to SLR and can serve as an umbrella species for other shorebirds that also spend the nonbreeding season in NW Mexico (Muñoz-Salas et al., 2023). Our study area extended from 32 - 21° N and included the states of Sonora, Sinaloa, and Nayarit, which we have termed the “mainland region,” as well as the Baja California Peninsula, which we have called the “peninsular region.” C. c. roselaari nonbreeding distribution is divided into three separate climatic zones: Mediterranean, in the northwestern tip of Baja California; arid, along the rest of Baja California and in western Sonora; and humid/dry tropical throughout the rest of the study area (Vidal-Zepeda, 2005). Except for Nayarit and parts of Sinaloa, coastal wetlands have a discontinuous distribution along the coastline, with discrete, well-defined wetlands separated by large expanses of arid-xeric ecosystems. Species Data Collection We assembled a dataset to model the current potential distribution of C. c. roselaari using ‘presence’ locations collected from 2000 – 2020 in NW Mexico from three different sources. (1) We gathered C. c. roselaari sightings from within this time period using the eBird Basic Dataset (eBird, 2020). (2) We tracked the annual movements of 58 C. c. roselaari using 3-g GPS satellite transmitters (PinPoint 75 Argos; Lotek Inc.) attached to the backs of adults with cyanoacrolyte glue at Grays Harbor, WA, during April–May 2017 and 2018. Tags recorded locations accurate to ±10 m every two days during the post-breeding period until early to mid-October when they fell off of molting birds or their batteries were depleted. (3) We carried out on-the-ground surveys from 7-15 December 2019 and 2-12 January 2020 along the coastlines of Sonora, Sinaloa, and Nayarit. We chose 110 survey locations following a stratified random sampling design that included sites within 4 km of the shoreline and the nearest road access. Sites were chosen using the land use and vegetation dataset for Mexico (INEGI, 2019), and consisted of habitats types that are known to be used by shorebirds. We conducted 5-minute point counts within 3 hours of high tide during which we counted every shorebird within a 400-m radius of a random site. Field surveys ensured that the C. c. roselaari nonbreeding distribution was well represented, helped reduce biases associated with eBird observations concentrated in popular birding areas, and added areas that could have been visited by tagged C. c. roselaari but not recorded after October — the point at which C. c. roselaari shed their satellite transmitters. Presence data preparation Records from eBird were manually inspected for positional uncertainty and only those that presented enough detail to ensure an accurate location were kept (n = 1,100). We filtered the transmitter locations by location class and kept only those in classes 3D and A3 (10- and <250-m accuracy, respectively; n = 980). Field surveys revealed 10 sites in NW Mexico where C. c. roselaari presence could be detected; each site was treated as an individual presence record. To account for the autocorrelation typical of transmitter datasets, and to reduce records close to each other, we pooled together the transmitter, eBird, and survey locations and, then, rarified the dataset by eliminating points closer together than 2 km. This procedure reduced our final dataset to a sample size of 112 presences. Environmental data preparation We selected 10 environmental predictor variables related to climatic conditions and the static physical environment that are thought to define the ecological niche of C. c. roselaari (Table 1). Climatic variables vary seasonally; we therefore selected mean values (e.g., mean temperature) from Dec and Jan, the months during which C. c. roselaari populations are thought to be most stable in their distribution across their non-breeding range (Carmona et al., 2013). In contrast, we included variables related to the static physical environment (e.g., elevation and distance to the nearest wetland) collected at any time of year within the past 10 years. All environmental predictors were transformed, cropped, and resampled to match their extents, projections, and resolutions (~0.8 x 0.8 km), respectively. To reduce multicollinearity, layers with variance inflation factors (USDM package; Naimi et al., 2013) >12 were excluded (Table 1). For instance, we included three metrics of the distance to the nearest wetland (Euclidean distance to intertidal habitat, Euclidean distance to the coastline, and Euclidean distance to the polygon of each coastal wetland) but only distance to the polygon of each coastal wetland was retained as all metrics were highly correlated and the latter provided the best results (Table 1). Table 1. Environmental predictor variables used to develop species distribution models for C. c. roselaari in NW Mexico

Environmental predictor

Description

Data Source

Elevation

Global elevation in meters

WorldClim (Fick and Hijmans, 2017)

Temperature

Mean December and January temperature

WorldClim (Fick and Hijmans, 2017)

Distance to wetland

Euclidean distance to the nearest polygon of coastal wetlands

Own data

Distance to urban areas

Euclidean distance to the nearest polygon of urban areas

Own data based on Inegi (2019)

Land cover and vegetation

MCD12Q1 MODIS/Terra+Aqua Land Cover Global 500m

Friedl & Sulla-Menashe (2019) https://lpdaac.usgs.gov/products/mcd12q1v006/

Nightlights

Proxy for disturbance and development intensity

NOAA (2020); https://ngdc.noaa.gov/eog/download.html

Silt

Global soil silt content 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Clay

Global soil clay content 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Soil Ph

Global soil pH x 10 in H20 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Soil Coarseness

Global coarse fragments 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Sea level rise scenario for 2050 To assess the inundation risk in NW Mexico in 2050, we used a static ‘bathtub’ model obtained from Climate Central (2020). The model is based on global-scale datasets for elevation, tide, and coastal flooding likelihoods for the year 2050. The model parameters included the CoastalDEM elevation dataset (v. 1.1; Kulp & Strauss, 2018), an RCP scenario of 4.5, and medium ‘luck’ based on mid-range results from the sea-level projection range (50th percentile). The output of this product is a spatial layer with areas identified as vulnerable to permanent SLR alone and to minor floods that may rise and fall slowly. While static models can overestimate the extent of inundation and ignore bio-geomorphological feedbacks known to buffer the effects of SLR (e.g., marsh migration; Klingbeil et al., 2021), they are better suited for large-scale inundation assessments where a lack of high-resolution data — such as in the Global South — and high computational costs limit the use of dynamic models. As such, the static models used here should be considered a “worst-case”

The Atlantic Zone Monitoring Program (AZMP) was implemented in 1998 with the aim of detecting and tracking climate change and variability in the northwest Atlantic, evaluating changes in the physical, chemical and biological ocean properties and predator-prey dynamics of marine resources, thereby enhancing Canada's ability to understand, describe, and forecast the state of the marine ecosystem. AZMP's sampling scheme in the Maritimes Region involves oceanographic sampling at two high-frequency (biweekly to monthly) fixed coastal stations, biannual sampling at stratified-random locations during DFO-led Ecosystem Trawl Surveys, biannual sampling along fixed cross-shelf sections (Cabot Strait Line, Louisbourg Line, Halifax Line, Browns Bank Line, and the Northeast Channel); the latter of which may also include opportunistic sampling at stations in the Laurentian Channel, Gulf of Maine, and various Marine Protected Areas (MPAs) in support of ancillary research programs and initiatives. AZMP sampling is conducted according to standard procedures (see https://cat.fsl-bsf.scitech.gc.ca/record=b3951030~S6) and includes the collection of CTD profiles and water samples for the determination of physical (e.g., T, S), chemical (e.g., nutrients, oxygen, pH), and biological (chl a) variables and vertical ring net tows. The data collected are synthesized into two annual reports on the physical and chemical/biological changes in the Maritimes Region, and are also used to evaluate changes across the Atlantic zone. _NCProperties=version=2,netcdf=4.7.4,hdf5=1.12.0, cdm_data_type=Profile cdm_profile_variables=platform_name,chief_scientist,cruise_name,cruise_number,geographic_area,id,station,event_number,profile_direction,latitude,longitude,profile_start_time,profile_end_time comments=MP (BIO). Tamara, Magda (DAL). contributor_name=Lindsay Beazley,BIO Data Services,Marine Environmental Data Section (MEDS) contributor_role=custodian,distributor,owner Conventions=CF-1.6,CF-1.7,CF-1.8,ACDD-1.1,ACDD-1.3 country=Canada country_of_origin=Canada description=The Atlantic Zone Monitoring Program (AZMP) was implemented in 1998 with the aim of detecting and tracking climate change and variability in the northwest Atlantic, evaluating changes in the physical, chemical and biological ocean properties and predator-prey dynamics of marine resources, thereby enhancing Canada's ability to understand, describe, and forecast the state of the marine ecosystem. AZMP's sampling scheme in the Maritimes Region involves oceanographic sampling at two high-frequency (biweekly to monthly) fixed coastal stations, biannual sampling at stratified-random locations during DFO-led Ecosystem Trawl Surveys, biannual sampling along fixed cross-shelf sections (Cabot Strait Line, Louisbourg Line, Halifax Line, Browns Bank Line, and the Northeast Channel); the latter of which may also include opportunistic sampling at stations in the Laurentian Channel, Gulf of Maine, and various Marine Protected Areas (MPAs) in support of ancillary research programs and initiatives. AZMP sampling is conducted according to standard procedures (see https://cat.fsl-bsf.scitech.gc.ca/record=b3951030~S6) and includes the collection of CTD profiles and water samples for the determination of physical (e.g., T, S), chemical (e.g., nutrients, oxygen, pH), and biological (chl a) variables and vertical ring net tows. The data collected are synthesized into two annual reports on the physical and chemical/biological changes in the Maritimes Region, and are also used to evaluate changes across the Atlantic zone. description_fr=Le Programme de surveillance de la zone atlantique (PMZA) a été mis en œuvre en 1998 dans le but de détecter et de suivre les changements climatiques et la variabilité dans l'Atlantique Nord-Ouest, d'évaluer les changements dans les propriétés physiques, chimiques et biologiques de l'océan et la dynamique prédateur-proie des ressources marines, améliorant ainsi le Canada. de comprendre, de décrire et de prévoir l'état de l'écosystème marin. Le schéma d'échantillonnage de l'AZMP dans la région des Maritimes comprend un échantillonnage océanographique à deux stations côtières fixes à haute fréquence (bihebdomadaire à mensuelle), un échantillonnage semestriel à des emplacements stratifiés aléatoires pendant les relevés au chalut écosystémique dirigés par le MPO, un échantillonnage biannuel le long de sections transversales fixes (Cabot Strait Line, Louisbourg Line, Halifax Line, Browns Bank Line et Northeast Channel) ; ces derniers peuvent également inclure un échantillonnage opportuniste dans les stations du chenal Laurentien, du golfe du Maine et de diverses aires marines protégées (AMP) à l'appui de programmes et d'initiatives de recherche auxiliaire. L'échantillonnage AZMP est effectué conformément aux procédures normalisées (voir https://cat.fsl-bsf.scitech.gc.ca/record=b3951030~S6) et comprend la collecte de profils CTD et d'échantillons d'eau pour la détermination des variables physiques (p. ex. T, S), chimiques (nutriments, oxygène, pH) et biologiques (chl a) et verticales remorquage de filet d'anneau. Les données recueillies sont synthétisées dans deux rapports annuels sur les changements physiques et chimiques et biologiques dans la région des Maritimes, et sont également utilisées pour évaluer les changements dans la zone atlantique. Easternmost_Easting=-51.204 featureType=Profile geospatial_lat_max=48.0002 geospatial_lat_min=40.6563 geospatial_lat_units=degrees_north geospatial_lon_max=-51.204 geospatial_lon_min=-70.3777 geospatial_lon_units=degrees_east geospatial_vertical_max=4950.692 geospatial_vertical_min=0.4958143 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.dfo-mpo.gc.ca/science/data-donnees/azmp-pmza/index-eng.html institution=DFO BIO instrument_serial_number=251175 instrument_vocabulary=SeaVoX Device Catalogue (SDN:L22) ioc_country_code=18 iso_3166_country_code=CA keywords_fr=changement climatique, salinité, chlorophylle a, température de l'eau, télédétection, fluorescence, densité eau, échantillonnage, missions océanographique, physico-chimie, Oxygène, Pression, Salinité de surface, Température de surface, Densité d'eau de mer, Température potentielle, Densité potentielle, Autre, Salinité sous la surface, Température sous la surface keywords_fra=changement climatique, salinité, chlorophylle a, température de l'eau, télédétection, fluorescence, densité eau, échantillonnage, missions océanographique, physico-chimie, Oxygène, Pression, Salinité de surface, Température de surface, Densité d'eau de mer, Température potentielle, Densité potentielle, Autre, Salinité sous la surface, Température sous la surface keywords_vocabulary=CIOOS Keywords mission=D25667020 naming_authority=CF Standard Name Table v29 Northernmost_Northing=48.0002 odf_data_type=CTD organization=Fisheries and Oceans Canada organization_fr=Pêches et Océans Canada platform=research vessel platform_vocabulary=https://vocab.nerc.ac.uk/collection/L06/current/ program=Atlantic Zone Monitoring Program sdn_institution_urn=SDN:EDMO::1811 source=/srv/data/raw/dfo/bio/ctd/2025_unprocessed/D25667020.ODF sourceUrl=(local files) Southernmost_Northing=40.6563 standard_name_vocabulary=CF Standard Name Table v78 subsetVariables=project,cruise_name,cruise_number,platform_name,platform_id,chief_scientist,geographic_area,station,event_number,profile_direction summary_fr=Le Programme de surveillance de la zone atlantique (PMZA) a été mis en œuvre en 1998 dans le but de détecter et de suivre les changements climatiques et la variabilité dans l'Atlantique Nord-Ouest, d'évaluer les changements dans les propriétés physiques, chimiques et biologiques de l'océan et la dynamique prédateur-proie des ressources marines, améliorant ainsi le Canada. de comprendre, de décrire et de prévoir l'état de l'écosystème marin. Le schéma d'échantillonnage de l'AZMP dans la région des Maritimes comprend un échantillonnage océanographique à deux stations côtières fixes à haute fréquence (bihebdomadaire à mensuelle), un échantillonnage semestriel à des emplacements stratifiés aléatoires pendant les relevés au chalut écosystémique dirigés par le MPO, un échantillonnage biannuel le long de sections transversales fixes (Cabot Strait Line, Louisbourg Line, Halifax Line, Browns Bank Line et Northeast Channel) ; ces derniers peuvent également inclure un échantillonnage opportuniste dans les stations du chenal Laurentien, du golfe du Maine et de diverses aires marines protégées (AMP) à l'appui de programmes et d'initiatives de recherche auxiliaire. L'échantillonnage AZMP est effectué conformément aux procédures normalisées (voir https://cat.fsl-bsf.scitech.gc.ca/record=b3951030~S6) et comprend la collecte de profils CTD et d'échantillons d'eau pour la détermination des variables physiques (p. ex. T, S), chimiques (nutriments, oxygène, pH) et biologiques (chl a) et verticales remorquage de filet d'anneau. Les données recueillies sont synthétisées dans deux rapports annuels sur les changements physiques et chimiques et biologiques dans la région des Maritimes, et sont également utilisées pour évaluer les changements dans la zone atlantique. time_coverage_duration=P0DT0H0M0S time_coverage_end=2025-05-21T11:53:20Z time_coverage_start=1997-04-17T05:58:20Z title_fr=Profils verticaux de rosette du programme de monitorage de la zone Atlantique (PMZA) de la région des maritimes Westernmost_Easting=-70.3777

Climate change is intensifying extreme weather events, including marine heatwaves, which are prolonged periods of anomalously high sea surface temperature that pose a novel threat to aquatic animals. Tropical animals may be especially vulnerable to marine heatwaves because they are adapted to a narrow temperature range. If these animals cannot acclimate to marine heat waves, the extreme heat could impair their behavior and fitness. Here, we investigated how marine heatwave conditions affected the performance and thermal tolerance of a tropical predatory fish, arceye hawkfish (Paracirrhites arcatus), across two seasons in Mo'orea, French Polynesia. We found that the fish’s daily activities, including recovery from burst swimming and digestion, were more energetically costly in fish exposed to marine heatwave conditions across both seasons, while their aerobic capacity remained the same. Given their constrained energy budget, these rising costs associated with warming may impact how hawkf..., Hawkfish (Paracirrhites arcatus) were collected in Mo'orea, French Polynesia during the Austral winter (2019) and Austral summer (2022). They were acclimated to one of five treatments (27, 28, 29, 31, or 33°C) for one week and tested for metabolic performance metrics, including maximum metabolic rate (MMR), standard metabolic rate (SMR), and costs of digestion (specific dynamic action, SDA) using custom-made respirometry chambers. A subset of fish was also tested for thermal tolerance using an acute cardiac thermal tolerance test. In addition, wild fish were also caught and tested for acute cardiac thermal tolerance. Metabolic data were processed in R and heart rate data were processed in LabChart. Please see the published manuscript for detailed methods. , , # Despite plasticity, heatwaves are costly for a coral reef fish

Description of the Data and file structure

Author Information A. Principal Investigator Contact Information Name: Dr. Erika Eliason or Dr. Deron Burkepile Institution: University of California, Santa Barbara Email: eliason@ucsb.edu, dburkepile@ucsb.edu B. Corresponding Author Contact Information Name: Jacey Van Wert Institution: University of California, Santa Barbara Email: jcvanwert@gmail.com

Date range of data collection (single date, range, approximate date): 2019-2022

Geographic location of data collection: Mo'orea, French Polynesia

File List: VanWert_etal_2023_hawkfish.csv - contains hawkfish collection and morphology data, SMR, MMR, AAS, SDA metrics, and cardiac thermal tolerance test metrics

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation/processing of data: Hawkfish (*Parac...

This layer represents CMIP6 future projections of mean diurnal range. Diurnal range is a measure of daily daytime to nighttime temperature range. However, this layer provides the mean of the monthly temperature ranges (monthly maximum minus monthly minimum). Since the climate data inputs are monthly or averaged months across multiple years, this calculation uses recorded temperature fluctuation within a month to capture diurnal temperature range. Using monthly averages in this manner is mathematically equivalent to calculating the temperature range for each day in a month, and averaging these values for the month.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: deg CCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 BioclimateClimate ScenariosThe CMIP6 climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5.SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP2-4.5intermediate GHG emissions:CO2 emissions around current levels until 2050, then falling but not reaching net zero by 21002.0 °C2.7 °C2.1 – 3.5SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate DataWorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional InformationThe time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality IssuesEach model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica.Related LayersBioclimate 1 Annual Mean TemperatureBioclimate 2 Mean Diurnal RangeBioclimate 3 IsothermalityBioclimate 4 Temperature SeasonalityBioclimate 5 Max Temperature of Warmest MonthBioclimate 6 Min Temperature Of Coldest MonthBioclimate 7 Temperature Annual RangeBioclimate 8 Mean Temperature Of Wettest QuarterBioclimate 9 Mean Temperature Of Driest QuarterBioclimate 10 Mean Temperature Of Warmest QuarterBioclimate 11 Mean Temperature Of Coldest QuarterBioclimate 12 Annual PrecipitationBioclimate 13 Precipitation Of Wettest MonthBioclimate 14 Precipitation Of Driest MonthBioclimate 15 Precipitation SeasonalityBioclimate 16 Precipitation Of Wettest QuarterBioclimate 17 Precipitation Of Driest QuarterBioclimate 18 Precipitation Of Warmest QuarterBioclimate 19 Precipitation Of Coldest QuarterBioclimate Baseline 1970-2000

#Données of the 2022 Hydroclimatic Atlas ## #Description The Hydroclimatic Atlas describes the current and future water regime of southern Quebec in order to support the implementation of water management practices that are resilient to climate change. These data are from the most recent version of the Hydroclimatic Atlas. ## #Nouveautés * Improvement of the spatial resolution of the hydrographic network; * Greater spatial coverage; * Addition of the CliMEX and CORDEX-NA sets, in addition to the scenarios in the CMIP5 set; * Use of six hydrological platforms; * * Addition of indicators, especially annual ones. * Etc. ## #Liste data available * Link to the new Hydroclimatic Atlas website. * Map of the 24,604 river sections of the Hydroclimatic Atlas with their attributes, available in GeoJSON and shapefile format. To facilitate download and display, the map is divided into 11 GeoJSON files: ABIT (Abitibi and Lac Abitibi region), CND west (North Shore A and B regions), CND east (North Shore regions C, D and E), GASP (North Shore regions C, D and E), GASP (Gaspésie), MONT (Gaspesie), MONT (Montégérie), OUTM (Outaouais Upstream), OUTV (Outaouais Downstream), OUTV (Outaouais Downstream), SAGU (Saguenay), SLNO (St-Laurent Nord-Ouest), SLSO (St-Laurent Sud-Ouest), and VAUD (Vaudreuil). * The CSV tables (“Magnitude...”) for each of the 76 hydrological indicators describing the amplement, the direction and the dispersion for RCP 4.5 and RCP8.5, for the three future horizons (see the documentation for details). * The CSV tables (“Projected indicator...”) for each of the 76 hydrological indicators detailing the flow values with their uncertainty for the historical period and the three future horizons (RCP4.5 and 8.5). See the documentation for more details. * A PDF with the metadata and a more detailed description of the data. ## #Note The 2018 version data is archived on Data Quebec for reference, for example for old reports or analyses referring to this version of the data. Any new study or analysis should use the most recent data available below or on the Atlas website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**