DOI: 10.5061/dryad.h66k62p
title: Data from: Recent global changes have decoupled species richness from specialization patterns in North American birds

description: Aim: Theory suggests that increasing productivity and climate stability
 towards the tropics favours specialization, thus contributing to the
 latitudinal richness gradient. A positive relationship between species
 richness and specialization should therefore emerge as a fundamental
 biogeographical pattern. However, land‐use and climate changes
 disproportionally increase the local extirpation risk for specialists,
 potentially weakening the relationship between richness and
 specialization. Here, we quantify empirically the richness–specialization
 prediction and test how 50 years of climate and land‐use change has
 affected the richness–specialization relationship. Location: USA. Time
 period: 1966–2015. Major taxa studied: Birds. Methods: We used the North
 American Breeding Bird Survey to quantify bird community richness and
 specialization to habitat and climate. We (a) quantify temporal change in
 the slope of the richness–specialization relationship, using a generalized
 mixed model; (b) assess how this change translates spatially, using
 generalized additive models; and (c) attribute spatio‐temporal change in
 the richness–specialization relationship to land use, climate and
 topographic drivers. Results: We found evidence for a positive but weak
 richness–specialization relationship in bird communities that greatly
 weakened over time. Given that specialization was not the main driver of
 richness, this relationship did not translate spatially into a linear
 spatial covariation between richness and specialization. Instead, the
 spatial covariation in richness and specialization followed a unimodal
 pattern, the peak of which shifted towards less specialized communities
 over time. These temporal changes were associated with precipitation
 change, decreasing temperature stability and land use. Main conclusions:
 Recent climate and land‐use changes have induced two contrasting types of
 community responses. In human‐dominated areas, the decoupling of richness
 and specialization drove a general trend for biotic homogenization. In
 areas of low human impact experiencing increasing climate harshness,
 specialization increased, whereas richness decreased. Our results offer
 new support for specialization as a key driver of macroecological
 diversity patterns and show that global changes are weakening this
 fundamental macroecological pattern.
description: Data used for the
 analyses of the paperData used in the GEB
 paper "Recent global changes have decoupled species richness from
 specialization patterns in North American birds" The code using or
 producing the different data tables is available at
 https://github.com/AnneMimet/Ric-Spe-code List of provided data tables (by
 alphabetic order): - ci_coef_null.txt: Mean and confidence intervals of
 the slope of the Richness-Specialization relationship obtained from the
 null model (analysis (1)) - Community_data.txt: yearly data of community
 richness (“ric”) and specialization (“csi”) per route (“rte_id”), with
 associated longitude and latitude. - Community_data_decades.txt: Data of
 community richness (“ric”) and specialization (“csi”) per route
 (“rte_id”), aggregated by decade (“year”: year 1 is the first decade –
 1973-1983, year 2 is the second decade – 1985-1995; year 3 is the third
 decade – 2001-2011), with associated longitude and latitude -
 correspondance.txt: Correspondence between the combination of kKoppen
 (“world_kopp”) and land use classes (“loc”) and the habitat classes
 obtained with the multivariate regression tree - data_for_mrt.txt: Data
 used to compute species specialization, including species in columns
 (prefix “X”), the route id (“rte_id”), the Koppen climate class of the
 route (“world_kopp”) and the land use (“loc”) - data_for_ssi.txt:
 Sub-sample of data_for_mrt.txt, used to compute species specialization -
 data_for_tree.txt: Sub-sample of data_for_mrt.txt, used to create the
 habitat classes by multivariate regression tree (mrt) - data_rtes.txt: Row
 data (averaged oer route from the North American Breeding Bird survey) of
 species abundance per route (“rte_id”) and year. All other table
 containing biodiversity data are derived from this table - env.txt: Data
 of environmental variables for each route computed per decade (column
 “per”). Climate data are derived from the PRISM data base, land use data
 are derived from the HYDE database and topographic data are derived from
 the SRTM 30+ data base: spatial range of winter minimum temperature
 ("r_min_wint"), mean winter minimum temperature
 ("m_min_wint"), spatial range of winter mean temperature
 ("r_mean_wint"), mean winter mean temperature
 ("m_mean_wint"), spatial range of winter precipitation
 ("r_ppt_wint"), mean winter precipitation
 ("m_ppt_wint"), spatial range of minimum temperature during the
 breeding season ("r_min_breed"), mean minimum temperature during
 the breeding season ("m_min_breed"), spatial range of mean
 temperature during the breeding season ("r_mean_breed"), mean
 temperature during the breeding season ("m_mean_breed"), spatial
 range of precipitation during the breeding season
 ("r_ppt_breed"), precipitation during the breeding season
 ("m_ppt_breed"), temporal standard deviation of the minimum
 temperature during the breeding season ("tr_min_breed"),
 temporal standard deviation of the mean temperature during the breeding
 season ("tr_mean_breed"), temporal standard deviation of the
 precipitation during the breeding season ("tr_ppt_breed"),
 proportion of cropping area ("crop"), proportion of rangelands
 ("range"), proportion of pasture ("pasture"),
 proportion of irrigated croplands ("irrig"), proportion of urban
 areas ("urb"), population ("pop"), range in altitude
 ("alt_rge"), mean altitude ("alt_mean"), climate
 velocity ("velo_rte"), potential NPP ("npp0_rte"),
 human impact index ("impact") - mat_ssi_random.txt: 500
 randomizations of species specialization (only the first 200 were actually
 used in the analyses) - rtes_XY: Latitude and longitude of the routes -
 ssi.txt: Species specializationdata.zip