Supporting Information
Appendix S1 – Changes in the distribution of thermal and precipitation niches in Britain.
Methods
In addition to the climate space vectors we wanted to visualise the locations of different climate spaces and how they have changed because, as it turns out, these are not intuitively obvious. For each climate variable we divided British hectads into tertiles (low, mid and high) on the basis of observed values in period 1. For example, the tertiles for spring minimum temperature were defined by the ranges: low = 1.36–3.0°C, mid = 3.1–3.8°C and high = 3.9–7.2°C. Ranges such as these were applied to the observed climate in period 2 to identify where the low, mid and high hectads according to a given climate variable were located after 20 years of climate change. We also identified hectads with conditions that were outside the range observed in period 1 (“novel climates”). To indicate changes in the availability of different climate niches we counted the number of hectads in each category in each period, and measured spatial shifts by computing the vector linking the centroids of the hectads in each period.
Results
Analyses of climate in years preceding BA2010 compared to that preceding BA1990 showed complex patterns of warming and precipitation change (Fig. S2, Table S2). As expected from gross climate trends, increased temperatures in spring, summer and winter led to 45–68% fewer cool/cold squares. Mild conditions spread to the fringes of the uplands and very mild conditions that were formerly restricted to the coast spread through large parts of the lowlands, more than doubling in extent. Summer temperature rises were so great that 365 hectads, concentrated in southeast
England, had novel hot conditions in period 2. Spring and summer rainfall patterns were more stable than temperature patterns, with smaller changes in overall extent of the “niches” (Table S1).
However, the spatial pattern of changes was more complex, especially in spring. The region of low summer rainfall contracted into southeast England but high summer rainfall in the north extended further east. Changes in the prevalence of winter snow were more similar to those for spring temperatures, although with a greater contraction of the high snow-days region (-88%). The overall impact of these spatial changes, when summarised as a single centroid vector, was that 10 of the climate niches shifted between north and NNW (338–347°) by 15–405 km (mean 163 km). Summer rainfall niches shifted on an approximately southeasterly axis (136–150°) by 40–106 km (mean 77 km). These climate spaces are entirely arbitrary, but even just 18 crudely defined niches (tertiles of six variables) show a variety of directional shifts and extent changes. Species responding to such changes are likely to show a diversity of spatial responses.

Appendix S2 – Simulated shifts in climate
Methods
To test the role of the shape of Britain in constraining local climate vectors we produced simulated climate datasets in which there was a north–south trend but no systematic east–west trend. We used the spring minimum temperature data as our starting point as this showed a pronounced skew to the direction of apparent shift. Two linear regressions were performed, one relating period 1 temperature per hectad to each hectad’s northing, and the other relating period 2 temperatures to northing. We then used the simulate function in R to generate predicted values for temperature in each hectad in each period. The simulate function produces predictions from a linear model that incorporate the uncertainty around the parameter estimates of the models. The resulting values therefore included the north–south trend as estimated from the original data but east–west trends were replaced by random variation. The simulation was repeated 100 times and for each resulting dataset we performed the climate vector analysis (see Materials & Methods, Local Climate Vectors), retaining from each the mean direction and confidence limits calculated across all hectads. We expect that local climate vectors should be oriented strictly to the north; any deviation from this indicates that Britain’s coastline imposes constraints on the observable climate vectors.
Results
The histograms below show the range of values for mean and the lower and upper 95% confidence limits of angle of centroid shift from the 100 simulations. They indicate that despite no systematic east–west trend in the climate variable, local climate space vectors were on average skewed 5 degrees to the west of north. Of the 100 estimates of confidence limit for climate vectors, 75 were entirely to the west of north and 25 spanned north. These results suggest that the shape of Britain plays a small role in constraining apparent climate vectors. A similar phenomenon is likely to affect species range centroids.
Figure A. Histograms showing mean and confidence limits for expected angle of climate vectors from data simulated to have a north–south temporal trend but no east–west temporal trend.

Table S1. Changes in the extent and location (distance and direction) of climate space as defined by different climate variables between period 1 (1983–87) and period 2 (2003–07). Band describes the tertile of the climate space from period 1 (i.e. the low band corresponds to the 33% of hectads with the lowest values of a given variable during period 1).
Variable
Spring minimum temperature
Summer maximum temperature
Winter minimum temperature
Spring rainfall
Summer rainfall
Winter snow days
Low
Mid
High
Low
Mid
High
Band Change in extent Distance Direction
Low -56% 82 km 343°
Mid
High
Low
-66%
121%
-45%
233 km
60 km
71 km
338°
22°
346°
Mid
High
Low
Mid
High
Low
Mid
High
7%
-3%
-68%
-73%
142%
19%
-29%
3%
142 km
73 km
151 km
331 km
56 km
37 km
38 km
15 km
344°
314°
347°
340°
109°
344°
214°
347°
-68%
30%
39%
148%
-71%
-88%
84 km
106 km
40 km
100 km
405 km
161 km
146°
150°
136°
23°
343°
347°

Table S2. Results of 24 linear regressions relating shifts in the locations of range margins along different spatial axes to changes in the statuses of species between 1988–91 and 2008–11. The intercept coefficients provide estimates of the magnitude (km) of range margin shift in the absence of changes in status. Statistically significant coefficients and models are emboldened.
Intercept
Slope
Model
Axis N. species Estimate t P
Estimate t P R 2 F df P
0°
107 4.4 1.2 0.23 59.9 6.8 <0.0001 31% 46.7 105 <0.0001
15°
101 5.3 1.5 0.13 56.8 6.9 <0.0001 32% 47.1 99 <0.0001
30°
105 5.8 2.1 0.046 47.9 7.3 <0.0001 34% 53.6 103 <0.0001
45°
100 4.9 2.5 0.015 37.4 8.2 <0.0001 40% 65.2 98 <0.0001
60°
94 4.0 3.3 0.0021 32.0 11.3 <0.0001 58% 124.6 92 <0.0001
75°
79 -0.1 -0.02 0.99 49.4 8.6 <0.0001 49% 74.6 77 <0.0001
90°
76 1.2 0.3 0.80 71.7 7.3 <0.0001 42% 52.8 74 <0.0001
105°
80 -0.7 -0.1 0.89 76.7 7.1 <0.0001 39% 50.5 78 <0.0001
120°
84 -3.0 -0.6 0.54 78.1 7.5 <0.0001 40% 55.8 82 <0.0001
135°
76 -6.8 -1.4 0.16 78.5 7.8 <0.0001 45% 60.1 74 <0.0001
150°
76 -8.8 -2.0 0.045 67.1 7.6 <0.0001 44% 57.1 74 <0.0001
165°
75 -10.0 -2.6 0.012 54.8 6.9 <0.0001 39% 47.5 73 <0.0001
180°
93 -7.2 -2.5 0.014 58.1 9.0 <0.0001 47% 81.4 91 <0.0001
195°
99 -6.8 -2.6 0.0098 66.5 11.2 <0.0001 56% 125.9 97 <0.0001
210°
103 -5.9 -2.3 0.023 71.9 12.0 <0.0001 59% 144.9 101 <0.0001
225°
103 -4.9 -1.8 0.073 71.5 11.4 <0.0001 56% 129.6 101 <0.0001
240° 103 -3.4 -1.4 0.17 66.2 11.4 <0.0001 56% 130.9 101 <0.0001
255°
102 -1.6 -0.8 0.42 52.1 11.0 <0.0001 55% 121.5 100 <0.0001
270°
108 0.2 0.1 0.92 41.5 10.1 <0.0001 49% 101.2 106 <0.0001
285°
112 4.8 2.5 0.013 47.2 10.2 <0.0001 49% 103.7 110 <0.0001
300°
113 5.2 2.1 0.037 55.0 9.1 <0.0001 42% 81.9 111 <0.0001
315°
110 4.6 1.5 0.13 60.8 8.1 <0.0001 38% 65.9 108 <0.0001
330°
114 3.7 1.1 0.27 60.4 7.4 <0.0001 33% 55.3 112 <0.0001
345°
112 3.9 1.1 0.27 61.4 7.1 <0.0001 31% 50 110 <0.0001

Figure S1. Map showing the hectads for which fixed-effort distribution information could be produced. Hectads marked in red had insufficient sample tetrad data to generate hectad information.

Figure S2. Spatial patterns of change in six climate variables in Britain between 1983–87 and 2003–
07. For each variable the range of observed values in period 1 was divided into three equal-sized categories (i.e. tertiles: low, mid and high). Three maps show where squares bearing those characteristics were distributed in period 1, period 2 or both (with numbers of hectads in brackets in each legend). Any squares with a novel climate in period 2 (e.g. colder or hotter than observed in period 1) are shown on the fourth map.
(a) Spring minimum temperature
(b) Summer maximum temperature
(c) Winter minimum temperature

(d) Spring rainfall
(e) Summer rainfall
(f) Winter snow days

Figure S3. Shift in the northern range margin for southerly distributed species between 1988–91 and
2008–11, based on fixed-effort distribution data. The solid red line shows the linear regression line for all southerly distributed species (see Table 1 for regression statistics); the dashed line is from a regression excluding the outlier (Pochard Aythya farina) near the x axis (F
1,74
= 90.9, P < 0.0001, R 2 =
55%; intercept estimate±sd = 16.0±4.4, t = 3.6, P < 0.0001).