Fig 1.
Flow diagram showing the selection process for studies on ant elevational diversity. Database searches returned 67 possible data sources. Several studies used previously published data, leaving 59 unique datasets. Of those, 19 were excluded due to either insufficient elevational diversity data (I) or just a scattering of sampling sites spread across multiple gradients (M). The remaining 40 were evaluated using the a priori criteria (see text), with 20 excluded due to heavy disturbance (D), elevational sampling gaps >500m (G), lack of sampling within the lowest 400m (L), sampling of less than 70% of the gradient (P), elevationally biased or minimal sampling (S), or some combination. See additional transect details in S2 Table and additional PRISMA details in S2 Text.
Fig 2.
Map of ant elevational diversity datasets (n = 20).
The distribution of ant study sites (circles), the three main elevational richness patterns for the eastern (n = 11) and western (n = 9) hemispheres (bars), and the number of patterns on wet and dry based mountains (black & white). (D = decreasing, LP = low plateau, MP = mid-elevational peak; see embedded figures and text for definitions).
Fig 3.
Scatterplots with regression lines for temperature, area, and MDE mean predictions with observed diversity showing wet and area mountains.
Points represent elevational sites (black: wet mountains; gray: arid mountains) and lines are the regression lines for each study. Panels show the ant diversity predicted by (a) mean annual temperature, (b) mid-domain effect mean predicted diversity, and (c) log-transformed area. The dotted line in (b) represents the 1:1 relationship that would be expected with a perfect MDE fit.
Fig 4.
Regression analyses of temperature-diversity and area-diversity relationships in well-sampled ant datasets.
(a) Fits to the temperature-ant diversity relationship (n = 20) with mean r2 = 0.465 ± 0.076 (SE). Wet mountains showed a significantly better fit than did arid mountains (r2wet = 0.635 ± 0.078, n = 12; r2arid = 0.210 ± 0.095, n = 8; P = 0.003). (b) Fits to the area-ant diversity relationship (n = 20) with mean r2 = 0.585 ± 0.068 (SE).
Fig 5.
Evaluation of mid-domain effect (MDE) predictions (n = 16).
MDE simulations poorly predicted diversity both in (a) the proportion of observed values falling within the 95% predictive bands (mean = 0.257 ± 0.044 (SE)) and in (b) the r2 values from linear regressions of the MDE-predicted means and observed diversity values (mean r2 = 0.354 ± 0.067). Mean range size was poorly predicted by the MDE simulations measured both by (c) the mean range sizes falling within the 95% predictive bands (mean = 0.335 ± 0.055) and by (d) linear regressions of the MDE-predicted means and observed mean range size values (mean r2 = 0.384 ± 0.059).
Fig 6.
Optimal environmental models for the EGCM (n = 16) and the multiple regression (n = 20).
The EGCM models were fit using only small-ranged species in each transect while the multiple regression used the diversity of all species. In the full model, log diversity in each elevational band was predicted by log area (A), mean annual temperature (T), and annual precipitation (P). Generally, wet mountain ant diversity (dark gray) was best predicted by one environmental variable, most often either area or temperature. In contrast, arid mountain ant diversity (light gray) was best predicted by models that included two or three variables, most often with area or temperature as one of the variables. Along most transects (lines), the optimal model differed between the EGCM and the multiple regression. Bars with transects included in the multiple regression but not in the EGCM are marked with (*).