DOI: 10.5061/dryad.x3ffbg7rj
title: Data from: A species' response to spatial climatic variation does not predict its response to climate change

description: The dominant paradigm for assessing ecological responses to climate change
 assumes that future states of individuals and populations can be predicted
 by current, species-wide performance variation across spatial climatic
 gradients. However, if the fates of ecological systems are better
 predicted by past responses to in situ climatic variation through time,
 this current analytical paradigm may be severely misleading. Empirically
 testing whether spatial or temporal climate responses better predict how
 species respond to climate change has been elusive, largely due to
 restrictive data requirements. Here we leverage a newly collected network
 of ponderosa pine tree-ring time series to test whether statistically
 inferred responses to spatial versus temporal climatic variation better
 predict how trees have responded to recent climate change. When compared
 to observed tree growth responses to climate change since 1980,
 predictions derived from spatial climatic variation were wrong in both
 magnitude and direction. This was not the case for predictions derived
 from climatic variation through time, which were able to replicate
 observed responses well. Future climate scenarios through the end of the
 21st century exacerbated these disparities. These results suggest that the
 currently dominant paradigm of forecasting the ecological impacts of
 climate change based on spatial climatic variation may be severely
 misleading over decadal to centennial timescales.
description: Study species Ponderosa pine (<em>Pinus
 ponderosa sensu lato</em>) is widely distributed in western North
 America throughout a highly disjunct range that encompasses a tremendous
 breadth of climatic conditions, with mean annual temperatures ranging from
 0 to 15 degrees Celsius and 200 to 2100 millimeters of mean annual
 cumulative precipitation (Figure 1).The most commonly used taxonomy
 recognizes two varieties of<em> P. ponderosa,</em>
 <em>var. scopulorum</em> and <em>var.
 ponderosa</em> – the interior and Pacific varieties, respectively
 (24).  The most recent molecular work has found evidence of more complex
 taxonomic structuring within ponderosa pine (17, 18), indicating at least
 four lineages. However, these finer taxonomic divisions do not seem to
 align with differences in climate sensitivities (19, 55), and do not yet
 have precisely defined geographic boundaries, preventing the confident
 assignment of populations to these taxonomic units without genetic
 analyses. Hence the analyses presented in the main body of this manuscript
 treat the <em>P. ponderosa</em> as a single unit, with
 supplementary analyses of how the Pacific – interior distinction impacts
 climate responses. Tree-ring data
 Data collection We selected study populations
 from across the distribution of <em>P. ponderosa s.l.</em>,
 following the niche-based methodology proposed by Perret & Sax
 (2021; (20)). We used curated and taxonomically verified botanical records
 compiled in the Conifer Database (82) to bound the climate space occupied
 by <em>P. ponderosa s.l.</em> across its geographic
 distribution. This climate space was defined by a set of seven climatic
 variables previously used to model the climatic niches of pines and other
 conifer species (20, 83). We limited site selection to public lands
 managed by the United States Forest Service or the Bureau of Land
 Management. Further criteria were that sites were free of obvious recent
 disturbance (e.g., timber harvest, thinning or other stand management,
 recent fire), were a minimum of one kilometer from high-traffic roadways,
 and were not located on either particularly steep slopes or along
 drainages. Wherever possible, we selected sites such that they
 corresponded with one of the Conifer Database botanical records used to
 build the species’ climatic niche model. This site selection procedure
 resulted in 24 study sites, spread across the states of Arizona,
 California, Colorado, Idaho, Montana, Oregon, and Montana (Figure 1, Table
 S1).  We used a consistent plot- and survey-based
 approach to collect tree-ring samples at each study site. Specifically, we
 established a 25-m by 25-m square plot in a representative portion of the
 stand at each site. Within this plot, we measured each ponderosa pine’s
 bole diameter at 1.4 m above ground level (i.e., diameter at breast
 height, DBH), assessed its general condition and vigor, recorded the
 presence or absence of new cones, and recorded any evidence of pathogens
 (e.g., sap flows, needle blight). Using a Haglöf increment borer, we
 collected two 4.3 mm-diameter cores from each tree greater than 15 cm DBH
 in the plot. One core was collected at breast height (140 cm), and the
 other was collected as close to the ground as possible given available
 equipment and the individual tree’s setting. In cases where there were
 fewer than 15 suitable trees on a plot, we sampled additional trees at
 increasing distances from the plot center. For 10 sites, we could not
 establish a fixed plot due either to excessive understory growth or site
 terrain characteristics. For these sites, trees were sampled at increasing
 distances from the intended plot location (i.e., an n-tree sampling
 design; 72). Sampling was conducted during the 2018 growing season between
 June and October. Sample preparation
  All increment cores were mounted, sanded, and visually
 cross-dated according to standard dendrochronological methods (85). We
 then measured the width in millimeters of each growth ring in every core
 sample using 2400 dpi digital scans and the computer program CooRecorder
 (86). We verified year assignments of the measured tree ring series using
 CDendro (87) and the ‘dplR’ package in R 3.6.3 (88, 89). Specifically, we
 used 20-year lagged inter-series correlations to identify dating and
 measurement errors across all series per site. These errors were
 iteratively identified and corrected until all inter-series correlations
 between 20-year segments were above 0.60. Both core samples for each tree
 were used during visual and statistical cross-dating, but only samples
 extracted from breast height were retained for growth analyses. For one
 site, located outside of Show Low, Arizona, a high rate of missing and
 false rings prevented confident assignment of a year of formation to
 growth rings. This site was excluded from all subsequent analyses. We used
 field-measured DBH for each tree to convert these ring width timeseries to
 annual basal area increments (BAI), a procedure that controls for the
 influence of increasing tree bole diameter on annual ring widths (90). In
 total, this yielded 360 tree growth time series from 23 sites (Table
 S1).
description: # Data from: A species' response to spatial climatic variation does
 not predict its response to climate change
 This dataset contains tree
 ring timeseries and associated historical climate data used in the
 manuscript titled "A species' response to spatial climatic
 variation does not predict its response to climate change", by Daniel
 L. Perret, Margaret E. K. Evans, and Dov F. Sax. See Methods for
 description of data collection procedures. In brief, representative
 samples were collected from populations of ponderosa pine in the western
 United States that span the breadth of the climate space occupied by the
 species. ## Description of the data and file structure The data are
 contained in a single .csv file. This file is a dataframe with 36,608 rows
 and 32 columns. Each row corresponds to an individual annual growth
 increment for some tree during some year in some location. The column
 labels and their meanings are thus: *Series* - unique identifier for the
 tree core sample the data came from. Consists of two parts *site_tree*
 (e.g., "a3r1_10" indicates the data come from tree 10 sampled in
 site a3r1). *Site, Tree* - information contained in *Series* *Year* - the
 crossdated year assigned to the measurement; the year the growth ring was
 formed in *RW* - the width (in mm) of the growth ring *dch_mm* - the
 diameter at coring height of the tree (in mm) as measured in 2018 at
 sample collection *Tree.Size* - the back-calculated diameter of the tree
 (in mm) in year *Year-1* *Basal.Area* - *Tree.Size* converted to area
 (mm^2) *next.Basal.Area* - *Basal.Area* in the subsequent *Year* *BAI* -
 basal area increment, the difference between next.Basal.Area and
 Basal.Area *x, y* - WGS84 latitude and longitude coordinates for the plot
 center associated with the Tree *bio01* - mean annual temperature
 associated with the year and location indicated (PRISM) *bio12* -
 cumulative annual precipitation associated with the year and location
 indicated (PRISM) *bio01.norm* - long-term mean annual temperature (over
 timeseries length) associated with location indicated (PRISM) *bio12.norm*
 - long-term mean annual precipitation (over timeseries length) associated
 with location indicated (PRISM) *p.jj.tmax* - *so.tmax* - monthly maximum
 temperature summarized in two to four-month seasonal periods, indicated by
 the letters preceding "."; for example, "so_tmax"
 indicates the maximum temperature in september and october during the
 indicated Year in the indicated location. Variables that begin with
 "p." correspond with the maximum temperature in the indicated
 seasonal period in the previous year. 'NA' values indicate that
 this variable could not be calculated for the given year because it
 required data from before the timeseries began. *p.jj.ppt* - *so.ppt* -
 monthly maximum temperature summarized in two to four-month seasonal
 periods, indicated by the letters preceding "."; for example,
 "so_tmax" indicates the maximum temperature in september and
 october during the indicated Year in the indicated location. Variables
 that begin with "p." correspond with the maximum temperature in
 the indicated seasonal period in the previous year. 'NA' values
 indicate that this variable could not be calculated for the given year
 because it required data from before the timeseries began. ##
 Sharing/Access information These data and associated summaries are also
 available at the corresponding author's GitHub repository:
 Climate data was
 derived from the following source: PRISM ( prism.oregonstate.edu ) ##
 Code/Software All code necessary to manipulate/generate data and perform
 analyses described in the associated manuscript are available at the
 corresponding author's GitHub respository: