Tag: Michelle Evans

Ricklefs, R. E. (2010). Evolutionary diversification, coevolution between populations and their antagonists, and the filling of niche space. Proceedings of the National Academy of Sciences, 107(4), 1265–1272. http://doi.org/10.1073/pnas.0913626107

It is difficult to think about ecological niches without considering the consequences for species coexistence and biodiversity. Stemming from this is the idea of “niche filling”, in that a finite niche exists and because one species is already “filling” it, another cannot persist in the same niche at the same geographical location. This has led to the theory of an equilibrium number of niche spaces, whereby diversification in one clade is balanced by a decrease in diversity in other clades. Ricklefs tested this hypothesis by analyzing several datasets of bird clade diversity and range sizes, predicting that if the hypothesis holds true, the total niche space per clade would scale with the species diversity. His results found that this was not the case, predicting this independence of niche space may be due to higher clade overlap and smaller niche space for individual species within high-diversity clades. The constraint on niche space, Ricklefs proposes, may be caused by the coevolution of pathogens. As pathogens co-evolve with their host, they keep the niche space of one particular species from expanding too broadly, thereby allowing for a higher diversity of closely related species. In the field of niche theory, the inclusion of pathogens is novel, as the ‘boundaries’ of niche space are conventionally defined by competition interactions or resource limitation. The inclusion of both pathogens and co-evolutionary dynamics in the defining of a species niche space represents an important, although somewhat daunting, step towards a further understanding of niche theory. Ricklef’s theory is based on the idea that ‘diversity begets diversity’ evolutionarily and that pathogens are host-specific and respond to the co-evolutionary arms race, otherwise known as the Red Queen Hypothesis, by host switching, and I am doubtful how often this is seen in nature.

Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., et al. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031–2064. http://doi.org/10.1002/joc.1688

Daly et al. created a 30 arc-sec climate map of the continental United States, mapping mean monthly precipitation, and minimum and maximum temperature, referred to as the PRISM map. Similar to other models, they interpolated point weather station data from 1971-2000 over a grid using a climate-elevation regression. Station data was weighted based on nine PRISM algorithms that incorporated spatial clustering and relevant topographic data, including coastal proximity, temperature inversion potential, and location on topographic slopes. Including data such as this allowed the model to more accurately predict local climatic events such as temperature inversion in valleys and rain shadows. To clean and validate the data, local experts were brought in for each region, in addition to manual exclusion of extreme and data errors. Daly et al. used two measures of uncertainty to evaluate their model, jack-knifed cross-validation and a 70% regression prediction interval. Error was highest in the more physiographically complex areas of the US, as expected, and the two measures performed similarly with increasing area scale. When compared to Daymet and WorldClim, PRISM better predicted temperatures at high elevation, because of its inclusion of the two-layer temperature inversion, while Daymet and WorldClim had a cold bias in these areas. Additionally, the inclusion of regional coastal algorithms for the different coasts of the US led to more accurate depictions of coastal temperatures in California than the other maps. In less complex areas, such as the MidWest, however, all three maps performed similarly. The inclusion of more topographic complexity in PRISM is an improvement over similar climate grids of the US, especially in the Pacific Northwest, but offers little benefit in areas of shallow elevation gradient in the middle of the country. When available, it seems logical and worthwhile, in my opinion, to include this additional complexity, as climate is not solely dependent on elevation and does not incorporate finer scale differences.

 

Leathwick, J. R., & Austin, M. P. (2001). Competitive Interactions between Tree Species in New Zealand’s Old-Growth Indigenous Forests. Ecology, 82(9), 2560–2573. DOI: 10.2307/2679936

A major limitation of current species distribution models is the exclusion of biotic predictors and species interactions in models. It is difficult to separate the spatial patterns of species interactions and environmental covariates in a model because the distribution of non-focal species is itself dependent on environmental covariates. The Nothofagus forests of New Zealand, however, present an ideal natural experiment, because their species distribution is based on historical landscape shifts, not environmental covariates, enabling the inclusion of Nothofagus presence- absence in a model without confounding the abiotic covariates. To examine the contribution of species interactions to SDMs, Leathwick and Austin fit GAMs to New Zealand tree species, including both environmental factors and presence-absence of Nothofagus species, and the interaction between environmental variables and competition. Environmental regressions in the absence of competition were first created for each species. Terms describing Nothofagus density and an interaction between Nothofagus density and temperature were then added to the regression. A reduction in deviance due to the addition of a term was interpreted to mean that the factor did influence the focal species distribution. The addition of competition significantly reduced the deviance for all species, and did so on a larger magnitude than did the environmental covariates, except for mean temperature, suggesting that competition significantly impacts species distributions. Additionally, including competition in the SDMs led to an upwards shift in species’ optimal temperature. The authors validated their model by comparing predictions using environment-only regressions and environment-competition regressions for two data sets. The environment-competition model seemed to predict species distributions more accurately, but this accuracy was not assessed quantitatively. Leathwick and Austin’s results suggest that future SDMs must take into account more realistic species interactions, as in some cases, they influence species distributions WORD at least as much as environmental covariates. However, their reliance on reduced deviance as proof of the role of competition could be improved upon by using a more rigorous, quantitative training method. Further work exploring the interaction between competition and environmental covariates, and that competition potentially alters the thermal niche of species, should be done and would be especially relevant in the face of climate change.