Anthropogenic climate change is predicted to be a major cause of species extinctions in the next 100 years. But what will actually cause these extinctions? For example, will it be limited physiological tolerance to high temperatures, changing biotic interactions or other factors? Here, we systematically review the proximate causes of climate-change related extinctions and their empirical support. We find 136 case studies of climatic impacts that are potentially relevant to this topic. However, only seven identified proximate causes of demonstrated local extinctions due to anthropogenic climate change. Among these seven studies, the proximate causes vary widely. Surprisingly, none show a straightforward relationship between local extinction and limited tolerances to high temperature. Instead, many studies implicate species interactions as an important proximate cause, especially decreases in food availability. We find very similar patterns in studies showing decreases in abundance associated with climate change, and in those studies showing impacts of climatic oscillations. Collectively, these results highlight our disturbingly limited knowledge of this crucial issue but also support the idea that changing species interactions are an important cause of documented population declines and extinctions related to climate change. Finally, we briefly outline general research strategies for identifying these proximate causes in future studies.
Aim The factors that set species range limits underlie many patterns in ecology, evolution, biogeography and conservation. These factors have been the subject of several reviews, but there has been no systematic review of the causes of warm-edge limits (low elevations and latitudes). Understanding these causes is urgent, given that the factors that set these limits might also drive extinction at warm edges as global climate changes. Many authors have suggested that warm-edge limits are set by biotic factors (particularly competition) whereas others have stressed abiotic factors (particularly temperature). We synthesize the known causes of species' warm-edge range limits, with emphasis on the underlying mechanisms (proximate causes).Location Global.Methods We systematically searched the literature for studies testing the causes of warm-edge range limits.Results We found 125 studies that address the causes of warm-edge limits, from a search including > 4000 studies. Among the species in these studies, abiotic factors are supported more often than biotic factors in setting species range limits at warm edges, in contrast to the widely held view that biotic factors are more important. Studies that test both types of factors support abiotic factors significantly more frequently. In addition, only 23 studies (61 species) identified proximate causes of these limits, and these overwhelmingly support physiological tolerances to abiotic factors (primarily temperature). Only eight species with identified proximate causes were tested for both biotic and abiotic factors, but the majority support abiotic factors.Main conclusions Although it is often assumed that warm-edge limits are set by biotic factors, our review shows that abiotic factors are supported more often among the species in these 125 studies. However, few studies both identify proximate causes and test alternative mechanisms, or examine the interaction between biotic and abiotic factors. Filling these gaps should be a high priority as warm-edge populations are increasingly driven to extinction by climate change.
Background: The evolution education research community has defined the construct of "evolution acceptance" in different ways and measured it using different instruments. One of these instruments-the GAENE-has not been analyzed across different student populations, demographic groups, degree plans, and instructional treatments. Such comparisons are crucial for examining whether the inferences drawn from instrument measures are valid, reliable, and generalizable. In this study, we attempt to replicate findings produced in the original validation study and explore aspects of the instrument not previously examined. Methods: We use Rasch analysis to study a large sample (n > 700) of undergraduates enrolled in standard introductory biology classes in the Northeastern USA. Participants completed the GAENE pre-and post-course for two semesters, and the MATE pre-and post-course for one semester. We assessed dimensionality, reliability, item fit, and rating scale functioning. We used regression analyses and generalized eta squared to evaluate the contribution of demographic and background variables to pre-course measures and pre-post course acceptance gains. Results: Our analyses of GAENE dimensionality and item properties were generally in line with prior work, including the finding that particular items displayed psychometric problems. Surprisingly, GAENE measures did not differ between biology majors and non-majors. Evolution instruction produced significant but small pre-post improvements in GAENE measures. GAENE measures were significantly associated with MATE measures (0.68-0.80). White and male participants had the highest evolution acceptance measures using both the MATE and the GAENE; race had a much stronger contribution to MATE measures as compared to GAENE measures. Race and gender acceptance differences were found to be as large as the differences produced in response to evolution instruction. Conclusions: Overall measures of acceptance change will be similar, but not identical, using the MATE and the GAENE. We make several recommendations for the modification or removal of some GAENE items, as well as future research directions for the measurement of evolution acceptance.
Theoretical perspectives and empirical research suggest that acceptance of evolution may be contingent upon what is evolving (e.g., plants vs. humans) and the scale of change (microevolution vs. macroevolution). The Inventory of Student Evolution Acceptance (I-SEA) is the only instrument designed to measure acceptance at different evolutionary scales and in different evolutionary contexts. Nevertheless, current validity testing for this instrument remains limited and grounded in Classical Test Theory. In this study, we examine patterns of evolution acceptance using the I-SEA instrument in a large sample of undergraduates (n > 2,000 participants) from six semesters of an evolution-focused biology class. We examine three research questions: (RQ1) Does Rasch analysis support the psychometric properties of the I-SEA discussed by Nadelson and Southerland? (RQ2) How variable are I-SEA measures across semesters, and are they sensitive to evolution instruction? (RQ3) Are I-SEA measures comparable to evolution acceptance measures produced by the Generalized Acceptance of EvolutioN Evaluation? Our empirical results support theoretical claims that evolution acceptance does not appear to be a unidimensional construct among novice learners. We identify limitations of the I-SEA and recommend multiple modifications to improve instrument quality.
A large body of research has examined students' conceptions of evolution and their relationships to acceptance of evolution. Proficiency in statistical and probabilistic reasoning has long been considered to be an essential feature of evolutionary reasoning, yet almost no empirical work has explored these putative connections. The RaPro instruments have recently been developed to measure statistical reasoning in the contexts of mathematics (RaProMath) and evolution (RaProEvo). Our study provides additional validation of these instruments using Rasch analysis and quantifies the contribution of statistical reasoning to both understanding and accepting evolution. We recruited a large sample (N = 564) of undergraduate students enrolled in an introductory biology course at a large public research university in the United States. Students completed a suite of published instruments that assessed statistical reasoning, evolutionary understanding, and evolutionary acceptance. Our findings indicate that validity inferences derived from RaPro scores generalized to the new sample, and that proficiency in statistical reasoning explained 28% of the variance in evolutionary knowledge and 19% of the variation in evolutionary acceptance. The inclusion of demographic variables into the model significantly increased the explained variance in acceptance. Notably, the variance in evolution acceptance explained by statistical reasoning was comparable to that of thinking dispositions or evolutionary knowledge reported in the literature. This work provides the first large-scale evidence of the role of statistical reasoning in evolutionary knowledge and acceptance and motivates future work to explore how statistical literacy should be integrated into evolution education efforts.
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