TY  - JOUR
A1  - Grimm-Seyfarth, Annegret
A1  - Mihoub, Jean-Baptiste
A1  - Henle, Klaus
T1  - Functional traits determine the different effects of prey, predators, and climatic extremes on desert reptiles
JF  - Ecosphere : the magazine of the International Ecology University
N2  - 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.
KW  - Australia
KW  - climate change
KW  - Gekkonidae
KW  - periodic flooding
KW  - Scincidae
KW  - species functional traits
KW  - species interactions
KW  - structural equation modeling
Y1  - 2019
U6  - https://doi.org/10.1002/ecs2.2865
SN  - 2150-8925
VL  - 10
IS  - 9
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Muwonge, Charles Magoba
A1  - Schiefele, Ulrich
A1  - Ssenyonga, Joseph
A1  - Kibedi, Henry
T1  - Modeling the relationship between motivational beliefs, cognitive learning strategies, and academic performance of teacher education students
JF  - South African journal of psychology
N2  - Although self-regulated learning has received much attention over the past decades, research on how teacher education students regulate their own learning has been scarce, particularly in third world countries. In the present study, we examined the structural relationships between motivational beliefs, cognitive learning strategies, and academic performance among teacher education students in Uganda. The sample comprised of 1081 students selected from seven universities. Data were collected using several subscales from the modified Motivated Strategies for Learning Questionnaire and were analyzed by structural equation modeling. Cognitive learning strategies fully mediated the relationship between motivational beliefs and academic performance. Motivational beliefs contributed to students’ academic performance mainly through influencing their critical thinking and organizational skills. Therefore, interventions to improve teacher education students’ academic performance should focus not only on boosting their motivation but also on enhancing their use of cognitive learning strategies.
KW  - Academic performance
KW  - Motivated Strategies for Learning Questionnaire
KW  - self-regulated learning
KW  - structural equation modeling
KW  - teacher education students
Y1  - 2018
U6  - https://doi.org/10.1177/0081246318775547
SN  - 0081-2463
SN  - 2078-208X
VL  - 49
IS  - 1
SP  - 122
EP  - 135
PB  - Sage Publ.
CY  - London
ER  -