Functional traits determine the different effects of prey, predators, and climatic extremes on desert reptiles

Abstract

Terrestrial reptiles are particularly vulnerable to climate change. Their highest density and diversity can be found in hot drylands, ecosystems which demonstrate extreme climatic conditions. However, reptiles are not isolated systems but part of a large species assemblage with many trophic dependencies. While direct relations among climatic conditions, invertebrates, vegetation, or reptiles have already been explored, to our knowledge, species’ responses to direct and indirect pathways of multiple climatic and biotic factors and their interactions have rarely been examined comprehensively. We investigated direct and indirect effects of climatic and biotic parameters on the individual (body condition) and population level (occupancy) of eight abundant lizard species with different functional traits in an arid Australian lizard community using a 30‐yr multi‐trophic monitoring study. We used structural equation modeling to disentangle single and interactive effects. We then assessed whether species could be grouped into functional groups according to their functional traits and their responses to different parameters. We found that lizard species differed strongly in how they responded to climatic and biotic factors. However, the factors to which they responded seemed to be determined by their functional traits. While responses on body condition were determined by habitat, activity time, and prey, responses on occupancy were determined by habitat specialization, body size, and longevity. Our findings highlight the importance of indirect pathways through climatic and biotic interactions, which should be included into predictive models to increase accuracy when predicting species’ responses to climate change. Since one might never obtain all mechanistic pathways at the species level, we propose an approach of identifying relevant species traits that help grouping species into functional groups at different ecological levels, which could then be used for predictive modeling.

Water availability as a major climatic driver of taxonomic and functional diversity in a desert reptile community

“…In contrast, despite G. variegata also being dominant in RWI, diversity responses were additionally strongly driven by changes in the abundance of terrestrial geckos. Hence, and because climatic parameters such as winter Darling River heights negatively affected terrestrial geckos (Grimm‐Seyfarth et al 2019), they also negatively affected diversity parameters.…”

“…Whereas the total lizard abundance was determined by the most common species ( M. boulengeri ) followed by L. xanthura and M. greyii , alpha and functional diversity measures were affected by the two most common species only ( M. boulengeri , C. regius ). Therefore, the total lizard abundance increased with the climatic parameters favoring M. boulengeri and L. xanthura , namely summer Darling River heights and the number of years since flooding (Grimm‐Seyfarth et al 2019). In turn, most alpha and functional diversity indices decreased with the number of years since flooding, which favored the two most abundant terrestrial, diurnal skinks ( M. boulengeri and C. regius ; Grimm‐Seyfarth et al 2019) likely outnumbering the other species.…”

“…Therefore, the total lizard abundance increased with the climatic parameters favoring M. boulengeri and L. xanthura , namely summer Darling River heights and the number of years since flooding (Grimm‐Seyfarth et al 2019). In turn, most alpha and functional diversity indices decreased with the number of years since flooding, which favored the two most abundant terrestrial, diurnal skinks ( M. boulengeri and C. regius ; Grimm‐Seyfarth et al 2019) likely outnumbering the other species. Finally, the RWII was the only plot where the CT max I constantly increased over time, likely as a consequence of the decreasing abundance or activity of M. boulengeri but rising abundance or activity of C. regius (which has a higher CT max ) with rising temperatures over time (Grimm‐Seyfarth et al 2017).…”

“…We collected data in three study plots (Henle 1989 a , b , c , 1990 a , b , c , Grimm‐Seyfarth et al 2017, 2018, 2019): (1) a plot of 150 × 100 m in a riverine woodland (RWI, 15,000 m 2 ) on heavy‐textured cracking clay, containing 60 black box trees ( Eucalyptus largiflorens ) and characterized by the absence of bushes, an interannually highly variable shrub and herb layer, and occasional flooding; (2) a 80 × 25 m plot in riverine woodland (RWII, 2000 m 2 ) bordering a sand dune area, with black box trees covering approximately 50% of the area and small bushes dominated by Maireana pyramidata , Sclerolaena paradoxa , and Enchylaena tomentosa ; (3) the Kinchega Station (station) consisting of eight huts made of corrugated iron on an approximately 2000‐m 2 area. Sandy soil covered 30% of the area, and scrub cover ranges from 10% to 40%.…”

“…We used the above‐mentioned climatic parameters as test predictors. We further included the interactions between summer temperature and each water parameter, namely summer and winter rainfall (Robertson 1987, 1988), and summer and winter Darling River heights (Robertson 1987), as both could affect the development of vegetation and arthropod preys (Kwok et al 2016, Grimm‐Seyfarth et al 2019). In the beta diversity analysis, we used the difference between climatic parameters of the two consecutive years to correlate the biodiversity turnover with climatic turnover.…”