Climate plays a crucial role in shaping species distribution and evolution over time. Dr. Elisabeth Vrba’s Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These results provide empirical support for the role of historical climate fragmentation and physiography in shaping the distribution and evolution of life on Earth. Methods Climate Data and Classification In this study, we employed the Köppen-Geiger climate classification to categorize climate zones. This system relies on climatic parameters, specifically monthly mean temperature (ºC) and total precipitation (mm), to define climate types (Beck et al., 2018; Köppen, 1884). Given the close correlation between climate and vegetation, these climate zones tend to align closely with global biome patterns (Belda et al., 2014), providing a proxy for examining how climate shapes biome distributions (Mucina, 2019). The Köppen-Geiger climate classification recognises 23 distinct climate regimes, grouped into five major zones: Tropical, Arid, Temperate, Cold, and Polar (Figure 1A). These zones served as the basis for our analysis of the impact of climate change on environmental fragmentation. Climate data for the last 5 million years were obtained from the high-resolution paleoclimate emulator, PALEO-PGEM (Holden et al., 2019). This dataset offers monthly climate information at a spatial resolution of 0.5º and temporal resolution of 1,000 years, beginning from the pre-industrial era (ca. 1760). We reclassified the climate data into the five major climate zones (tropical, arid, temperate, cold, and polar) for each 1,000-year interval following the methodology outlined by Beck et al. (2018). To facilitate computational operations, we introduced a "-99" value for missing data and made specific adjustments to the function 'KoppenGeiger.m' (Beck et al., 2018), as communicated by H. Beck (personal communication, December 18, 2021), to align with defined precipitation thresholds: "Pthreshold = 2×MAT if >70% of precipitation falls in winter, Pthreshold = 2×MAT+28 if >70% of precipitation falls in summer, otherwise Pthreshold = 2×MAT+14 (Galván et al., 2023). This change was made to rectify a previous code typo that prevented the accurate assignment of some pixels to their climate zone. "Pthreshold" refers to the precipitation threshold for determining the aridity of a climate zone. Meanwhile, "MAT" corresponds to Mean Annual Temperature. Geographical Framework This study was conducted on a global scale, to assess whether similar climate zones behaved consistently across different continents. To facilitate these comparisons, we divided the world into three distinct landmasses, hereafter referred to as Americas, Africa, and Eurasia+Oceania (EurOc). The rationale behind this division was to partition our planet into distinct landmasses, each of which would encompass a tropical zone. Upon delimiting the three main landmasses, the different islands were assigned to the nearest landmass in a straight line. This, in turn, corresponds with other biogeographical criteria based on the similarity of flora and fauna. Thus, the three studied landmasses were established as follows: · Americas: This category encompasses continental North, Central, and South America, as well as the Caribbean Islands. In the North (Bering Strait), we have included Aleutian St. Matthew, St. Paul, St. George, and Nunivak Islands. St. Lawrence Island is excluded due to its proximity to Europe. In the West, we encompass the Islands off the Mexican West Coast, the Galápagos Islands, and Easter Island. To the South, the Malvinas Islands are included. In the East, we consider Fernando de Noronha, Atol das Rocas Biological Reserve, and Boi Islands. Greenland is part of this category, while Iceland is excluded. · Africa: This category covers continental Africa and Madagascar. In the West, it includes the Canary, Madeira, and Savage Islands, the Cabo Verde archipelago, St. Helena, Tristan da Cunha, and Ascension Islands. In the East, Socotra, Seychelles, and the Mayotte archipelago, Comoros, and Mascarene Islands are encompassed. · Eurasia + Oceania: This category comprises continental Eurasia, the Arabian Peninsula, Iceland, St. Lawrence Island, Japan, Philippines, Indonesia Australia, New Zealand, and Papua-New Guinea Islands. All the islands of the Pacific Ocean, including the Hawaii Archipelago, are also included. In the Indian Ocean, we consider the Laccadive, Maldives, and Chagos Islands in the West and Ceylon and the Andaman Islands in the East. The French Austral and Antarctic Lands islands that are closer to the Antarctic region were excluded from the study. Measuring Fragmentation To assess climate zone fragmentation, we used the R package landscapemetrics v1.5.4 (Hesselbarth et al., 2019), employing the equal-area Mollweide projection. We applied the “lsm_p_area” function to calculate the number of fragments within each climate zone for each time interval, classifying them based on their area into four size categories: - Small fragments (S): Those with an area of up to 3,000 km2, approximately equivalent to the area of a single pixel under our 0.5º resolution. - Medium fragments (M): Those with an area between 3,000 and 30,000 km2. - Large fragments (L): Those with an area between 30,000 and 600,000 km2. - Extra-large fragments (XL): Those with an area exceeding 600,000 km2. Upon confirming that the number of fragments in the different climate zones followed a normal distribution but did not meet the assumption of variance homogeneity, we conducted the corresponding Welch One-Way ANOVA tests to determine the significance of the results. Given that we were comparing five climate zones, we applied Bonferroni correction to post-hoc results significance. Statistical analyses were conducted using the R library jmv (v2.3.4;53) In addition to quantifying the number of fragments within climate zones at each time in our series, we computed several additional measures to assess fragmentation: -Fragmentation Events: The count of instances when the number of fragments increased compared to the previous point in time. -Fragmentation Strength: The median number of fragments generated in each fragmentation event. -Maximum Fragmentation: The highest number of fragments produced in a single fragmentation event. Higher levels of climatic fragmentation are operationally defined as a prevalence of small (S) and medium (M) fragments, while lower levels of fragmentation are characterised by a greater abundance of larger patches (L and XL). Fragmentation vs. Richness To explore the relationship between climate fragmentation and specialist mammal richness we sourced mammal range maps from IUCN polygons (IUCN, 2022). Terrestrial mammal data was downloaded on 24th January 2022, while freshwater mammal data was obtained on 21th September 2022. We imported these range maps in shapefile format into R using the ‘rgdal’ package version 1.5-28 (Bivand et al., 2021). We excluded polygons associated with certain families such as Delphinidae, Iniidae, Phocidae, Phocoenidae, Platanistidae, Trichechidae, and the possibly extinct Lipotidae, due to their predominantly aquatic habits. We further excluded species range polygons with presence values of 3 (“possibly extant”) and 6 (“presence uncertain”), as well as range values of 3 (“introduced”) and 4 (“vagrant”) to retain only reliable natural range data (Miraldo et al., 2016). Range data for each species were converted into a 0.5º raster using the ‘terra’ R package version 1.5-21 (Hijmans, 2022). Mammal species were classified according to their range into specialists, those species that are restricted to a single climate zone, and generalists, which are found in more than one climate zone. To this end, we considered the current distribution of terrestrial mammal species as a reliable representation of their climatic specificities. We then quantified the richness of specialist and generalist mammal species within each climate zone on every continent. We considered various factors of climate fragmentation, including the total number of fragments categorized by size (S, M, L, and XL), the frequency of fragmentation events, as well as the fragmentation strength and maximum fragmentation within each fragment size, climate zone, and continent. In addition, we calculated the mean annual temperature and mean annual precipitation for each climate zone on each continent. To explore the relationship between these variables and specialist mammal richness, we employed a generalized linear model (GLM). To refine our model and identify the most influential predictors, we employed a bidirectional stepwise regression. This method systematically evaluates interaction terms, ensuring the final model contains only strong predictors or those involved in substantial interactions (Gelman & Hill, 2006). The stepwise regression process continues until no further terms can enhance the model. The selected variables were subsequently evaluated through significance tests, residual analysis, and sensitivity

Telemetry data: 39 adult female polar bears (Ursus maritimus) in Western Hudson Bay were immobilized via remote injection and fitted with satellite-linked GPS collars in 2012-2017. Collars were programmed to retrieve location data every 4 hours. Locations with biologically impossible movement (i.e. speed > 30 km/h) were removed and the data were re-sampled using consecutive bursts of 3 or more locations in 4 hour intervals to correct for missing locations. Locations are specific to the sea ice break-up period in Hudson Bay, and are separated into early and late break-up. This data set includes a generic ID for each individual, latitude, longitude, and time/date of each location, as well as the number of each burst that was retained after re-sampling. Sea ice data: Sea ice cover raster files for the entire Arctic were attained from NSIDC for all days in 2013-2018. Raster resolution is 6.25 x 6.25 km, and pixel values reflect percentage of sea ice concentration multiplied by 2. Sea ice data was clipped to the study area in Hudson Bay, and only days within the break-up period in each year were retained. File name reflects date "YYYMMDD-HB". Details on data collection procedures, re-sampling, defined Hudson Bay break-up period, source of sea ice data, and definition of study area are identified in: Brooke A. Biddlecombe, Erin M. Bayne, Nicholas J. Lunn, David McGeachy, & Andrew E. Derocher. 2020. Comparing sea ice habitat fragmentation metrics using integrated step selection analysis. Ecology and Evolution.

Relative depredation (binomial) data from a squidpop tethering experiment in summer 2017 in Back Sound, North Carolina. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt acquisition_description=For Table and Figure references below, see the document "SquidpopAssay_statistical_analysis.pdf" in the Supplemental Files section.

Study Site Selection

We conducted our study across eight discrete seagrass meadows (hereafter referred to as landscapes) located in Back Sound, North Carolina (NC), USA (3442\u2032 N to 3439\u2032 N, 7637\u2032 W to 7631\u2032 W) (Fig. S1). All of our sampled landscapes were composed of a mixture of Back Sound's dominant seagrasses: eelgrass and shoal grass, Halodule wrightii (Ascherson 1868) (Yeager et al. 2016). Landscapes were chosen based upon available aerial imagery in Google Earth Pro as of February 19, 2017, and ground-truthed for changes in seasonal seagrass growth/senescence using summer, 2017, drone photography and ImageJ 1.x (Schneider et al. 2012). No discernable differences in landscape fragmentation states (e.g. total area, number of patches) were found between the two aerial imagery sources. All landscapes were relatively shallow (1-1.5 m depth at high tide), reasonably isolated from other seagrass beds (distance to nearest seagrass meadow = 112 17 m [mean standard error]) and were appropriately sized to encompass short-term (e.g., daily, monthly) movements of common seagrass-associated fauna in this system (Yeager et al. 2016). We identified similarly sized landscapes (25882 6592 m2) available in Back Sound by defining the minimum convex polygon surrounding the seagrass meadow, regardless of the total seagrass cover within the polygon. Among eight candidate landscapes of similar size, we defined four continuous landscapes and four fragmented landscapes based on the number of patches, the perimeter- to-area ratio, and the largest patch's percent cover of the total seagrass area (Table 1). Seagrass fragmentation is often naturally coupled with habitat loss (Wilcove et al. 1986), resulting in the mean seagrass area of our fragmented landscapes being nearly half that of our continuous landscapes (Table 1). Thus, our experiment was designed to examine the effects of fragmentation (i.e., the breaking apart of habitat concomitant with habitat loss) rather than fragmentation per se (i.e., the breaking apart of habitat without habitat loss; sensu Fahrig 2003).

Squidpop Assays

Squidpops were also used to measure relative "depredation" across landscapes (acknowledging that a combination of predation and scavenging may account for observed loss patterns). Squidpops are 1-cm 1-cm squares of dried squid mantle tied to 1-cm segments of 12-lbs test monofilament (Duffy et al. 2015). We attached squidpops to 60-cm long, 0.5-cm diameter, fiberglass stakes. Twenty squidpops were deployed (stakes pushed 50 cm into the sediment to prevent squidpop tangling in seagrass or burial in sediment) within each of the eight landscapes per assay date during July and August (July 5, July 13, July 26, August 8, and August 30). Within each landscape, 10 squidpops were haphazardly placed within seagrass edges, defined as 30 cm (a crab tether length) from the seagrass-mudflat interface. The other 10 squidpops were haphazardly placed in seagrass interiors, defined as \u22651 m from the seagrass-mudflat interface. Only patches with a radius of 1 m or larger were used for squidpops classified as 'interior'. However, patches with a radius of <1 m were used for a portion of our 'edge' squidpops. All squidpops were placed at least 1 m apart. A total of 720 squidpops were deployed (Table S1). Squidpop depredation assays did not occur in June due to lack of dried squid availability. During the first two squidpop deployment cycles we checked squidpop status (present, absent/eaten) at 1 h and 24 h. We observed nearly 100% squidpop removal by 24 h, so for the remaining three deployment cycles we performed status checks at 1 h and 2 h.

Point measurements of water temperature (C) were taken in each landscape at the location and time of all squidpop assays hand-held thermometers (Table S1). We chose temperature as our seasonality proxy (Fig. S2) because several other seasonally affected factors including faunal densities correlate with water temperature variability. Additionally, the measurement of temperature is easy, cheap, reliable, and comparable to previous studies.

Equipment:

Dried squid mantel: whole dried squid from Asian food market
Tether materials:
EcoStakes \u2013 tomato plant stakes
12-lbs test monofilament fishing line
Pool noodles \u2013 cut into rounds for tether relocation floats
Hand-held digital thermometer- LYNCH Waterproof thermometer 39240
Hand-held refractometer-VEE GEE STX-3 Salinity 0-100%o
Hand-held Garmin GPSmap 78 awards_0_award_nid=714025 awards_0_award_number=OCE-1635950 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1635950 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=Squidpop Assay PI(s): Dr F. Joel Fodrie Data Version 1: 2019-11-06 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.780092.1 Easternmost_Easting=-76.526267 geospatial_lat_max=34.703251 geospatial_lat_min=34.651056 geospatial_lat_units=degrees_north geospatial_lon_max=-76.526267 geospatial_lon_min=-76.587826 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/780092 institution=BCO-DMO instruments_0_acronym=Refractometer instruments_0_dataset_instrument_description=Hand-held refractometer-VEE GEE STX-3 Salinity 0-100%o instruments_0_dataset_instrument_nid=781111 instruments_0_description=A refractometer is a laboratory or field device for the measurement of an index of refraction (refractometry). The index of refraction is calculated from Snell's law and can be calculated from the composition of the material using the Gladstone-Dale relation.

In optics the refractive index (or index of refraction) n of a substance (optical medium) is a dimensionless number that describes how light, or any other radiation, propagates through that medium. instruments_0_instrument_name=Refractometer instruments_0_instrument_nid=679 instruments_0_supplied_name=VEE GEE STX-3 instruments_1_dataset_instrument_nid=781113 instruments_1_description=Acquires satellite signals and tracks your location. instruments_1_instrument_name=GPS receiver instruments_1_instrument_nid=706037 instruments_1_supplied_name=Hand-held Garmin GPSmap 78 instruments_2_dataset_instrument_description=Hand-held digital thermometer- instruments_2_dataset_instrument_nid=781110 instruments_2_instrument_name=Thermometer instruments_2_instrument_nid=725867 instruments_2_supplied_name=LYNCH Waterproof thermometer 39240 keywords_vocabulary=GCMD Science Keywords metadata_source=https://www.bco-dmo.org/api/dataset/780092 Northernmost_Northing=34.703251 param_mapping={'780092': {'lat': 'master - latitude', 'lon': 'master - longitude', 'ISO_DateTime_UTC_In': 'master - time'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/780092/parameters people_0_affiliation=University of North Carolina at Chapel Hill people_0_affiliation_acronym=UNC-Chapel Hill-IMS people_0_person_name=Dr F. Joel Fodrie people_0_person_nid=559341 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of North Carolina at Chapel Hill people_1_affiliation_acronym=UNC-Chapel Hill-IMS people_1_person_name=Amy Yarnall people_1_person_nid=780032 people_1_role=Contact people_1_role_type=related people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Amber York people_2_person_nid=643627 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=Habitat Fragmentation projects_0_acronym=Habitat Fragmentation projects_0_description=Amount and quality of habitat is thought to be of fundamental importance to maintaining coastal marine ecosystems. This research will use large-scale field experiments to help understand how and why fish populations respond to fragmentation of seagrass habitats. The question is complex because increased fragmentation in seagrass beds decreases the amount and also the configuration of the habitat (one patch splits into many, patches become further apart, the amount of edge increases, etc). Previous work by the investigators in natural seagrass meadows provided evidence that fragmentation interacts with amount of habitat to influence the community dynamics of fishes in coastal marine landscapes. Specifically, fragmentation had no effect when the habitat was large, but had a negative effect when habitat was smaller. In this study, the investigators will build artificial seagrass habitat to use in a series of manipulative field experiments at an ambitious scale. The results will provide new, more specific information about how coastal fish community dynamics are affected by changes in overall amount and fragmentation of seagrass habitat, in concert with factors such as disturbance, larval dispersal, and wave energy. The project will support two early-career investigators, inform habitat conservation strategies for coastal management, and provide training opportunities for graduate and undergraduate students. The investigators plan to target students from underrepresented groups for the research opportunities. Building on previous research in seagrass environments, this research will conduct a series of field experiments approach at novel, yet relevant scales, to test how habitat area and fragmentation affect fish diversity and productivity. Specifically, 15 by 15-m seagrass beds will be

Genotypic_Acoustic_Morphological_Preference_Data_PseudacrisThis file contains 5 worksheets: worksheet 1 describes identifiers used throughout data set; worksheet 2 (Genotypes (G)) encompasses 14 columns of microsatellite and mtDNA data; worksheet 3 (Call (C)) encompasses 21 columns of mean acoustic data; worksheet 4 (Morphology (M)) encompasses 11 columns of mean morphological measurements; worksheet 5 (Female Preference (F)) encompasses 6 columns of phonotaxis experimental results. For more detail refer to Methods and Table 1.Stewart et al Heredity 2015 DRYAD.xlsx

Fauna species count data from minnow trap sampling within seagrass in Summer 2017 in Back Sound, North Carolina. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=For Table and Figure references below, see the document "MinnowTrap_statistical_analysis.pdf" in the Supplemental Files section.

Study Site Selection

We conducted our study across eight discrete seagrass meadows (hereafter referred to as landscapes) located in Back Sound, North Carolina (NC), USA (3442\u2032 N to 3439\u2032 N, 7637\u2032 W to 7631\u2032 W) (Fig. S1). All of our sampled landscapes were composed of a mixture of Back Sound's dominant seagrasses: eelgrass and shoal grass, Halodule wrightii (Ascherson 1868) (Yeager et al. 2016). Landscapes were chosen based upon available aerial imagery in Google Earth Pro as of February 19, 2017, and ground-truthed for changes in seasonal seagrass growth/senescence using summer, 2017, drone photography and ImageJ 1.x (Schneider et al. 2012). No discernable differences in landscape fragmentation states (e.g. total area, number of patches) were found between the two aerial imagery sources. All landscapes were relatively shallow (1-1.5 m depth at high tide), reasonably isolated from other seagrass beds (distance to nearest seagrass meadow = 112 17 m [mean standard error]) and were appropriately sized to encompass short-term (e.g., daily, monthly) movements of common seagrass-associated fauna in this system (Yeager et al. 2016). We identified similarly sized landscapes (25882 6592 m2) available in Back Sound by defining the minimum convex polygon surrounding the seagrass meadow, regardless of the total seagrass cover within the polygon. Among eight candidate landscapes of similar size, we defined four continuous landscapes and four fragmented landscapes based on the number of patches, the perimeter- to-area ratio, and the largest patch's percent cover of the total seagrass area (Table 1). Seagrass fragmentation is often naturally coupled with habitat loss (Wilcove et al. 1986), resulting in the mean seagrass area of our fragmented landscapes being nearly half that of our continuous landscapes (Table 1). Thus, our experiment was designed to examine the effects of fragmentation (i.e., the breaking apart of habitat concomitant with habitat loss) rather than fragmentation per se (i.e., the breaking apart of habitat without habitat loss; sensu Fahrig 2003).

Seagrass-associated Fauna Sampling

Seagrass-associated fauna were sampled to explore relationships between our observed predation/depredation rates and seasonal faunal densities within each fragmentation state. We sampled seagrass-associated fauna during each crab tethering cycle with four baited (with ~ 8 pieces of dried dog food; Mahoney et al. 2018) Gee-style minnow traps (41-cm long, 22-cm wide, 0.3-cm galvanized mesh-wire cylinders, with 4-cm diameter funneled openings) haphazardly deployed in each landscape. We acknowledge that baiting traps increases catch rates in our system and could bias the captured community toward predators and scavengers (our target community), yet this increase in catch rate seems to be uniform across seagrass habitat structure variables (Mahoney et al. 2018). At 24 h, faunae were enumerated, identified to the lowest taxonomical level possible, and released.
Point measurements of water temperature (C) were taken in each landscape at the location and time of all faunal sampling using hand-held thermometers (Table S1). We chose temperature as our seasonality proxy (Fig. S2) because several other seasonally affected factors including faunal densities correlate with water temperature variability. Additionally, the measurement of temperature is easy, cheap, reliable, and comparable to previous studies.

Equipment:
* Generic brand dog food
* Memphis and twine Gee-style minnow traps (41-cm long, 22-cm wide, 0.3-cm galvanized mesh-wire cylinders, with 4-cm diameter funneled openings) awards_0_award_nid=714025 awards_0_award_number=OCE-1635950 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1635950 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=Seagrass fauna counts - Minnowtrap data PI: Joel Fodrie Data Version 1: 2019-10-29 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.780027.1 Easternmost_Easting=-76.526267 geospatial_lat_max=34.703251 geospatial_lat_min=34.651056 geospatial_lat_units=degrees_north geospatial_lon_max=-76.526267 geospatial_lon_min=-76.587826 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/780027 institution=BCO-DMO instruments_0_dataset_instrument_description=Memphis and twine Gee-style minnow traps (41-cm long, 22-cm wide, 0.3-cm galvanized mesh-wire cylinders, with 4-cm diameter funneled openings) instruments_0_dataset_instrument_nid=780121 instruments_0_description=shore fishing gear instruments_0_instrument_name=minnow trap instruments_0_instrument_nid=679340 metadata_source=https://www.bco-dmo.org/api/dataset/780027 Northernmost_Northing=34.703251 param_mapping={'780027': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/780027/parameters people_0_affiliation=University of North Carolina at Chapel Hill people_0_affiliation_acronym=UNC-Chapel Hill-IMS people_0_person_name=Dr F. Joel Fodrie people_0_person_nid=559341 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of North Carolina at Chapel Hill people_1_affiliation_acronym=UNC-Chapel Hill-IMS people_1_person_name=Amy Yarnall people_1_person_nid=780032 people_1_role=Contact people_1_role_type=related people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Amber York people_2_person_nid=643627 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=Habitat Fragmentation projects_0_acronym=Habitat Fragmentation projects_0_description=Amount and quality of habitat is thought to be of fundamental importance to maintaining coastal marine ecosystems. This research will use large-scale field experiments to help understand how and why fish populations respond to fragmentation of seagrass habitats. The question is complex because increased fragmentation in seagrass beds decreases the amount and also the configuration of the habitat (one patch splits into many, patches become further apart, the amount of edge increases, etc). Previous work by the investigators in natural seagrass meadows provided evidence that fragmentation interacts with amount of habitat to influence the community dynamics of fishes in coastal marine landscapes. Specifically, fragmentation had no effect when the habitat was large, but had a negative effect when habitat was smaller. In this study, the investigators will build artificial seagrass habitat to use in a series of manipulative field experiments at an ambitious scale. The results will provide new, more specific information about how coastal fish community dynamics are affected by changes in overall amount and fragmentation of seagrass habitat, in concert with factors such as disturbance, larval dispersal, and wave energy. The project will support two early-career investigators, inform habitat conservation strategies for coastal management, and provide training opportunities for graduate and undergraduate students. The investigators plan to target students from underrepresented groups for the research opportunities. Building on previous research in seagrass environments, this research will conduct a series of field experiments approach at novel, yet relevant scales, to test how habitat area and fragmentation affect fish diversity and productivity. Specifically, 15 by 15-m seagrass beds will be created using artificial seagrass units (ASUs) that control for within-patch-level (~1-10 m2) factors such as shoot density and length. The investigators will employ ASUs to manipulate total habitat area and the degree of fragmentation within seagrass beds in a temperate estuary in North Carolina. In year one, response of the fishes that colonize these landscapes will be measured as abundance, biomass, community structure, as well as taxonomic and functional diversity. Targeted ASU removals will then follow to determine species-specific responses to habitat disturbance. In year two, the landscape array and sampling regime will be doubled, and half of the landscapes will be seeded with post-larval fish of low dispersal ability to test whether pre- or post-recruitment processes drive landscape-scale patterns. In year three, the role of wave exposure (a natural driver of seagrass fragmentation) in mediating fish community response to landscape configuration will be tested by deploying ASU meadows across low and high energy environments. projects_0_end_date=2019-08 projects_0_geolocation=North Carolina projects_0_name=Collaborative Research: Habitat fragmentation effects on fish diversity at landscape scales: experimental tests of multiple mechanisms projects_0_project_nid=714026 projects_0_start_date=2016-09 sourceUrl=(local files) Southernmost_Northing=34.651056 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-76.587826 xml_source=osprey2erddap.update_xml() v1.3

Background: African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases. In many areas, ungulate populations are now largely confined within a network of loosely connected protected areas. These metapopulations face gene flow restriction and run the risk of genetic diversity erosion. In this context, we assessed the "genetic health" of free ranging southern African Cape buffalo populations (S.c. caffer) and investigated the origins of their current genetic structure. The analyses were based on 264 samples from 6 southern African countries that were genotyped for 14 autosomal and 3 Y-chromosomal microsatellites. Results: The analyses differentiated three significant genetic clusters, hereafter referred to as Northern (N), Central (C) and Southern (S) clusters. The results suggest that splitting of the N and C clusters occurred around 6000 to 8400 years ago. Both N and C clusters displayed high genetic diversity (mean allelic richness (Ar) of 7.217, average genetic diversity over loci of 0.594, mean private alleles (Pa) of 11), low differentiation, and an absence of an inbreeding depression signal (mean FIS = 0.037). The third (S) cluster, a tiny population enclosed within a small isolated protected area, likely originated from a more recent isolation and experienced genetic drift (FIS = 0.062, mean Ar = 6.160, Pa = 2). This study also highlighted the impact of translocations between clusters on the genetic structure of several African buffalo populations. Lower differentiation estimates were observed between C and N sampling localities that experienced translocation over the last century. Conclusions: We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes. The splitting time of N and C clusters suggests that the current pattern results from human-induced factors and/or from the aridification process that occurred during the Holocene period. The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases). Management practices of African buffalo populations should consider the micro-evolutionary changes highlighted in the present study.