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- Materials and Methods
- Supporting Information
Understanding the role of the environment in shaping species diversity patterns has long motivated ecological and biogeographical research on local to global scales. In recent years, this research has greatly benefited from the development of large species occurrence databases and from conceptual and technical advances in niche based species distribution models (SDMs; Guisan & Thuiller, 2005; Elith & Leathwick, 2009). SDMs are used to predict the potential distribution of species in space and time by relating observed occurrence or abundance patterns to a set of environmental variables (Guisan & Thuiller, 2005). When most species pertaining to a given geographical species pool are considered, such as the whole flora of a given area, different structural or compositional aspects of species assemblages can be predicted. For example, several studies have used stacked-SDMs (S-SDMs) to predict current and future distributions of species richness (e.g. Guisan & Theurillat, 2000; Feria & Peterson, 2002; Algar et al., 2009; Newbold et al., 2009; Pineda & Lobo, 2009) or turnover (Thuiller et al., 2005; Maiorano et al., 2011) under various scenarios of climate change.
Geographical distributions of species are constrained by strict eco-physiological requirements and various other factors, including dispersal processes, positive and negative biotic interactions as well as anthropogenic and geomorphic perturbations (Soberón, 2007). Thus, SDMs are often assumed to fit spatial realizations of the environmental niche of the studied species (Araújo & Guisan, 2006). However, there are still ambiguities over the components of the realized niches that are estimated in climate-based SDMs (Elith & Leathwick, 2009). In addition, there is a large amount of evidence indicating that the relative importance of species distribution/assembly drivers are not constant over space and time or along productivity gradients (Michalet et al., 2006) and that these factors can mutually influence each other along these trajectories (Agrawal et al., 2007). Finally, little is known about how SDMs are affected by such variations of species distribution/assembly drivers.
Despite an increasing use of climate-based S-SDMs and promising associated perspectives, few studies have evaluated the models’ predictive accuracy (Feria & Peterson, 2002; Algar et al., 2009; Newbold et al., 2009; Pineda & Lobo, 2009; Trotta-Moreu & Lobo, 2010; Dubuis et al., 2011). To our knowledge, no study has assessed whether the performance of S-SDMs for predicting communities is constant throughout space or along environmental gradients. Such an evaluation is important because S-SDMs represent crucial tools for assessing how future species assemblages will look under future climatic conditions (Ferrier & Guisan, 2006; Guisan & Rahbek, 2011).
In harsh or stressful climatic conditions, community assembly is commonly driven by environmental filtering, which permits only those species sharing the appropriate physiological, behavioural and/or ecological attributes required to survive in the local climate to coexist (Weiher et al., 1999). In this case, climate directly affects the physiology of the species and represents an important filter of community assembly. In addition, climate influences the nature and strength of community- and ecosystem-level processes, which also determine the assembly and distribution of species. For example, it has been reported that low summer temperatures are associated with an increase in the occurrence of facilitative effects among plants (Callaway et al., 2002). Alternatively, the importance of species interactions has been shown to be reduced in alpine ecosystems (Mitchell et al., 2009). Because precipitation and frost partially control geomorphological processes (including erosion), these factors are also responsible for increased disturbance regimes. These examples emphasize climate as a strong direct or indirect determinant of species distribution and assembly, especially in harsh conditions, where more accurate SDM and S-SDM predictions should thus be expected.
In milder climates, ecosystems are more productive, and biotic interactions, such as competition, may be more important than purely abiotic effects for shaping both species distributions and communities (Grime, 2001). In the context of the realized niche, a typical expected consequence of competitive exclusion is a restriction in the occupation of the fundamental niche space. Therefore, predictions based on species responses modelled from distribution data should also account for the role of biotic interactions. In addition, climatically mild habitats are also noticeably affected by intense and diverse human activities, which locally modify abiotic and biotic factors and/or enhance the stochasticity driven by the interplay between disturbance regimes and dispersal events. This impact leads to situations in which the species are likely not at equilibrium with the climate (Araújo & Pearson, 2005). Among locations sharing similar climatic constraints, the same species may encounter very different biotic and abiotic constraints, which may cause the species to exhibit different performances in terms of establishment, growth and fitness. In such conditions, climatic factors may make less of a contribution to the distribution and assembly of the species, and climatic niche models would accordingly fit weaker species responses and produce predictions that are less accurate. As a result, the S-SDM predictions of such communities are likely to be less accurate.
In summary, when stacking climate-based SDMs to predict species assemblages, and provided that the most relevant environmental predictors are available, one could expect the following (Fig. 1): (1) H1 – the accuracy of assemblage predictions increases when moving from productive and mild conditions towards climatically stressful habitats; (2) H2A – the best S-SDM performance in the harshest habitats is primarily associated with strong environmental filtering; and (3), as a corollary, H2B – the inaccurate assemblage predictions in milder habitats are associated with a larger spectrum of constraints driving the local assembly of species.
Figure 1. Hypothetical explanation and expected variation in the performance of stacked-species distribution models (S-SDMs) along elevation. At high elevations, the climate is the main determinant of species assembly and species responses to topo-climatic environment are precisely fitted (i.e. tight confidence intervals). Stacked-species distributions are thus expected to provide accurate predictions of species assemblages. In contrast, at low elevations, species distribution is not always at equilibrium with the climate and is rather caused by a variety of assembly rules (possibly mediated by human activities) while climatic conditions remain the same. Fitted species responses to the climate are weak (i.e. large confidence intervals) and the accuracy of the S-SDMs is reduced.
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The objective of this study was to conduct a thorough evaluation of S-SDMs. We tested hypotheses H1 and H2 using elevation as a gradient of climate stress and habitat productivity. We used a large vegetation dataset derived from a robust sampling at a fine resolution, covering the full elevation range of the western Swiss Alps. We reconstructed plant communities by stacking predictions from individual SDMs based on high-resolution topo-climatic predictors and evaluated the deviation between the predicted and observed species assemblages using an independent dataset. Finally, we assessed whether the performance of S-SDMs changed with the spatial variations in the mean and dispersion patterns of the plant functional traits, which are used to infer the constraints that drive the assembly of communities (assembly rules in their broader definition, following Keddy, 1992). Trait convergence was interpreted as a signature of environmental filtering, implying that only species that share common ecological abilities to face local environmental conditions can coexist. Conversely, trait divergence was interpreted as a signature that two competing species cannot coexist unless they exhibit limited similarity in their ecological requirements.
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- Materials and Methods
- Supporting Information
SDMs were successfully calibrated for the 211 dominant species in the study area and showed fair to good prediction accuracy (all of the AUCs were greater than 0.7; Araújo et al., 2005). For the large majority of the species, no spatial autocorrelation was observed in the residuals between the calibration and evaluation datasets, and the correlations remained very low even when significant (average Moran's I = 0.143 for the shortest distances separating the calibration and evaluation plots; see Appendix S1). We therefore considered the second dataset of 298 plots as valid for the S-SDM evaluation.
The modelled species responses using GAMs were not consistently well adjusted along the elevation gradient. In particular, smaller standard prediction errors were observed at high elevations than at middle or low elevations (Fig. 3).
Figure 3. Change in the accuracy of fitted species response to the topo-climatic factors along elevation. This was assessed using generalised additive models (GAMs) and the confidence interval of predicted probabilities for the calibration plots. Each sample represents the mean confidence interval of a given species for a given elevation band.
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Using S-SDMs to predict species assemblages in our evaluation dataset resulted in a significant relationship between the predicted and observed species richness (r = 0.45, P < 0.001; Fig. 4c). The slope estimate of this relationship was 0.23, the standard error was 0.03 and the intercept estimate was 26.52 with a standard error of 0.73. The mean species richness error in the overall dataset was 7.30 (SE = 0.58), the mean assemblage prediction success was 0.78 (SE = 0.003), the mean assemblage kappa was 0.72 (SE = 0.004), the mean assemblage specificity was 0.85 (SE = 0.001), the mean assemblage sensitivity was 0.23 (SE = 0.004) and the mean Jaccard index was 0.11 (SE = 0.003).
Figure 4. The evaluation of stacked-species distribution models (S-SDMs) based on topo-climatic predictors. Calibration of topo-climatic niche models of the species was based on 613 plots, while evaluation of S-SDMs was based on 298 different evaluation plots (see Supporting Information for a test of independence between the calibration and evaluation datasets). Solid lines represent significant trends fitted with generalized additive models.
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The predicted species richness pattern in the evaluation dataset (Fig. 4b) did not reproduce the observed hump-shaped curve with a peak of diversity at 1500–1700 m (Fig. 4a). Instead, the S-SDM predicted a progressive and slow (compared with the observed) decrease of species richness as the elevation of the plots increased (Fig. 4b). In addition, the different evaluation metrics reported here showed significant variations along the elevation gradient (Fig. 4d–i). More specifically, we observed a larger overprediction of species richness at low elevations than at mid-range and high elevations, and a larger overprediction was observed at high elevations than at mid-range elevations (Fig. 4, Appendix S2). Next, we observed very similar nonlinear trends of assemblage prediction success (Fig. 4e) and assemblage kappa (Fig. 4f), with no significant variation from low to mid-range elevations and a strong increase from mid-range to high elevations (Appendix S2). The specificity and sensitivity showed significant linear variations along elevation, positive for the specificity (linear regression R2 = 0.52, P < 0.001; Fig. 4g) and negative for the sensitivity (linear regression R2 = 0.41, P < 0.001; Fig. 4h). Conversely, the Jaccard index showed a decrease towards higher elevations (Fig. 4i, Appendix S2). The variation of most of the evaluation metrics was strongly correlated with the observed plant species richness, except the assemblage specificity and sensitivity (Fig. S3.1 in Appendix S3). Species richness errors standardized by the observed species richness were higher by far at high elevations compared with mid-range or low elevations. The assemblage prediction success, kappa and specificity, standardized by the observed species richness, confirmed the increase of S-SDM performance with elevation. The relative sensitivity also increased with elevation, whereas the Jaccard index showed a U-curve-like trend (Fig. S3.2 of Appendix S3). The absolute sensitivity (and to some extent the Jaccard index) and specificity were strongly and positively correlated with the predicted species richness (Fig. S3.1 of Appendix S3): the more species the S-SDM predicts, the better it predicts true presences, and conversely the fewer species it predicts, the poorer it predicts true absences.
We only report here on the assemblage prediction success, assemblage specificity and assemblage sensitivity for the analyses that tracked the association between the assembly rules and variation in the S-SDM accuracy. The results with the other metrics provided complementary support to our conclusion or non-significant trends (Appendix S4). The assemblage prediction success was only significantly related to the community aggregated canopy height (CHCA; R2 = 0.49; Fig. 5b). We observed a significant decrease (R2 = 0.21; Fig. 5c) of assemblage specificity with the deviation in the Rao index of canopy height values compared with the null expectation (measured as the SES of Rao: CHSES-Rao), and the specificity decreased with the community aggregated canopy height (CHCA; R2 = 0.49; Fig. 5d). The assemblage sensitivity increased with CHSES-Rao (R2 = 0.13; Fig. 5e) and CHCA (R2 = 0.21; Fig. 5g). The observed patterns revealed that the best prediction success and specificity, the worst sensitivity and the highest elevations were almost exclusively associated with the significant convergence of CH. On the contrary, the worst prediction success and specificity, the best sensitivity and the lowest elevations were associated equally with the convergence, divergence and null distribution of CH. Regarding SLA and LDMC, we observed significant, although weak, positive relationships between SLASES-Rao (SLA for specific leaf area) and LDMCSES-Rao (LDMC for leaf dry matter content) with assemblage specificity and negative relationships with assemblage specificity (Appendix S4). LDMCCA showed a significant but hardly interpretable inverse-parabolic (decreasing then increasing) trend with the assemblage specificity and a parabolic (increasing then decreasing) trend with the assemblage sensitivity. SLACA showed a significantly negative relationship with the assemblage specificity and a positive relationship with the sensitivity (Appendix S4).
Figure 5. Variation in the accuracy of stacked-species distribution models (S-SDMs) with plant functional patterns in the evaluation plots. The plant functional trait being considered here is canopy height (CH, measured in mm). Its pattern was estimated using community aggregated mean (CHCA, in mm) and evenness using the standardized effect size of the Rao index (CHSES-Rao). Null distribution of trait values was estimated using 9999 simulations. Positive values of CHSES-Rao indicate that the observed distribution of canopy height tends to be more dissimilar than by chance (trait divergence indicative of limiting similarity) while negative values indicate more similarity (trait convergence indicative of environmental filtering). Evaluation metrics are the means over 9999 community samples based on the predicted probabilities of the presence of the 211 species. Solid lines represent significant trends fitted with generalized additive models.
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