A species' response to spatial climatic variation does not predict its response to climate change

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.

Methods
Study species

Ponderosa pine (Pinus ponderosa sensu lato) 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 P. ponderosa, var. scopulorum and var. ponderosa – 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 P. ponderosa 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 P. ponderosa s.l., 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 P. ponderosa s.l. 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).