New light on the baseline importance of temperature for the origin of geographic species richness gradients
Temperature predicts the maximum tree-species richness and water and frost shape the residual variation
Whether environmental conditions –in particular energy and water availability– are sufficient to account for species richness gradients (e.g. Currie 1991), or the effects of other biotic and historical or regional factors need to be considered as well (e.g. Ricklefs 1987), was the subject of debate during the 1990s and 2000s (e.g. Francis & Currie 2003; Hawkins et al. 2003, 2006; Currie et al. 2004; Ricklefs 2004). The metabolic theory of ecology (Brown et al. 2004) provided a solid and well-rooted theoretical support for the preponderance of energy as the main driver for richness variations. As any good piece of theory, it provided testable predictions about the sign and shape (i.e. slope) of the relationship between temperature –a key aspect of ambient energy– and species richness. However, these predictions were not supported by empirical evaluations (e.g. Kreft & Jetz 2007; Algar et al. 2007; Hawkins et al. 2007a), as the effects of a myriad of other environmental gradients, regional factors and evolutionary processes result in a wide variety of richness–temperature responses across different groups and regions (Hawkins et al. 2007b; Hortal et al. 2008). So, in a textbook example of how good theoretical work helps advancing science even if proves to be (partially) wrong, the evaluation of this aspect of the metabolic theory of ecology led to current understanding that, while species richness does respond to current climatic conditions, many other ecological, evolutionary and historical factors do modify such response across scales (see, e.g., Ricklefs 2008; Hawkins 2008; D’Amen et al. 2017). And the kinetic model linking mean annual temperature and species richness (Allen et al. 2002; Brown et al. 2004) was put aside as being, perhaps, another piece of the puzzle of the origin of current diversity gradients.
Segovia (2021) puts together an elegant way of reinvigorating this part of the metabolic theory of ecology. He uses quantile regressions to model just the upper parts of the relationship between species richness and mean annual temperature, rather than modelling its central tendency through the classical linear regression family of methods –as was done in the past. This assumes that the baseline effect of ambient energy does produce the negative linear relationship between richness and temperature predicted by the kinetic model (Allen et al. 2002), but also that this effect only poses an upper limit for species richness, and the effects of other factors may result in lower levels of species co-occurrence, thus producing a triangular rather than linear relationship. The results of Segovia’s simple and elegant analytical design show unequivocally that the predictions of the kinetic model become progressively more explanatory towards the upper quartiles of the relationship between species richness and temperature along over 10,000 tree local inventories throughout the Americas, reaching over 70% of explanatory power for the upper 5% of the relationship (i.e. the 95% quantile). This confirms to a large extent his reformulation of the predictions of the kinetic model.
Further, the neat study from Segovia (2021) also provides evidence confirming that the well-known spatial non-stationarity in the richness–temperature relationship (see Cassemiro et al. 2007) also applies to its upper-bound segment. Both the explanatory power and the slope of the relationship in the 95% upper quantile vary widely between biomes, reaching values similar to the predictions of the kinetic model only in cold temperate environments –precisely where temperature becomes more important than water availability as a constrain to plant life (O’Brien 1998; Hawkins et al. 2003). Part of these variations are indeed related with changes in water deficit and number of frost days along the XXth Century, as shown by the residuals of this paper (Segovia 2021) and a more detailed separate study (Segovia et al. 2020). This pinpoints the importance of the relative balance between water and energy as two of the main climatic factors constraining species diversity gradients, confirming the value of hypotheses that date back to Humboldt’s work (see Hawkins 2001, 2008). There is however a significant amount of unexplained variation in Segovia’s analyses, in particular in the progressive departure of the predictions of the kinetic model as we move towards the tropics, or downwards along the lower quantiles of the richness–temperature relationship. This calls for a deeper exploration of the factors that modify the baseline relationship between richness and energy, opening a new avenue for the macroecological investigation of how different forces and processes shape up geographical diversity gradients beyond the mere energetic constrains imposed by the basal limitations of multicellular life on Earth.
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Joaquín Hortal (2021) New light on the baseline importance of temperature for the origin of geographic species richness gradients. Peer Community in Ecology, 100075. 10.24072/pci.ecology.100075
Evaluation round #204 Mar 2021
DOI or URL of the preprint: https://doi.org/10.1101/836338
Version of the preprint: 2
Decision by Joaquín Hortal
Your manuscript has significantly improved, and I agree with the two reviewers of this version in that it is a fantastic work, and indeed merits recommendation. I asked a new reviewer expert in quantile regressions to take a look at the criticisms on the method raised before. S/he agrees with your arguments, but also points to a couple of simple methods to address its limitations in what refers to providing metrics of goodness-of-fit. Please follow her/his advice about this issue, and in particular the test for the deviation of the slope from -0.65; I think it will certainly strengthen your work.
Besides that, Rafael Molina-Venegas provides a number of minor comments that will be quite useful to improve this final version of the manuscript. I'm sure that, after all these revisions are done, the manuscript will be ready for my recommendation.
Thanks again for sending this beautiful work to open discussion throuhg PCI.
Reviewed by Rafael Molina-Venegas, 28 Jan 2021
Reviewed by anonymous reviewer, 03 Mar 2021
Evaluation round #117 Feb 2020
DOI or URL of the preprint: https://doi.org/10.1101/836338
Version of the preprint:
Decision by Joaquín Hortal
Sorry for the time taken to reach a decision about your preprint. Briefly, it has been difficult to find reviewers due to the holiday season, and some expected reviews have been delayed. In any case, now we do have two reviews, and both agree with my assessment that the paper may merit a recommendation in PCI, once some key problems with the current version are solved.
More precisely, for the preprint to be recommendable, you need to:
(a) provide a much better theoretical explanation linking the environmental temperature suffered by plants and mean annual temperature, as well as other descriptors of "harshness" such as Freezing Days (by the way, Humboldt first proposal of a mechanism for the latitudinal diversity gradient was precisely harshness; I think Hawkins TREE 2001 highlighted that).
(b) Assess the effects of regional variations on the richness/MAT relationship, ideally using differences between biomes and/or ecoregions, realms and glaciated/unglaciated areas.
(c) Pay special attention to the conversion of units, as it determines the slope values, and make a clearer formulation of your hypothesis about the slope that allows identifying the actual slope that is assessed. Current information in the methods is not enough so as to ascertain the exact way you may reach a comparable -0.65 slope.
(d) Provide estimates of the goodness of fit of the models. It could be argued that within an information-theory-based hypothesis testing framework goodness of fit is not needed - because you effectively assess whether/to which extent some hypotheses are informative or not. However, you use AIC for comparing between alternative models, which leaves the reader with no information about which is the power of these models to "explain" the data. If goodness-of-fit lies below, say, 5%, we are talking about massive residuals and limited explanation of the overall phenomenon of richness. If, on the contrary, such percentage goes above 30 or even 40% of variation, that is really a lot. If you account for (b) and you end up having final models that include MAT, Frost Days, CWD, biome and realm, for example, and that accounts for more than half of the variation, your results will be much more convincing that if you explain one third of richness variations, and most of it is due to regional efects.
See the reviewer's assessments for more details on these four points, and several other issues. Among these, let me highlight that you should avoid using MAT as abbreviation in the title (Mean Annual Temperature or simply Temperature would be more clear, and of course informative), and also that this manuscript desperately needs maps with richness and residual values, to allow the readers to assess your results in a wider extent.
I am looking forward to receive a revised version of the preprint, together with a detailed answer to the comments provided. I'm convinced that your research has enough quality so as to finally merit a recommendation in PCI Ecology.
All the best,