Grain yield increase in cereal variety mixtures: A meta-analysis of field trials

Plant ecology theory predicts that growing seed mixtures of varieties (variety mixtures) may increase grain yields compared to the average of component varieties in pure stands. Published results from field trials of cereal variety mixtures demonstrate, however, both positive and negative effects on grain yield. To investigate the prevalence and preconditions for positive mixing effects, reported grain yields of variety mixtures and pure variety stands were obtained from previously published variety trials, converted into relative mixing effects and combined using meta-analysis. Furthermore, available information on varieties, mixtures and growing conditions was used as independent variables in a series of meta-regressions. Twenty-six published studies, examining a total of 246 instances of variety mixtures of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), were identified as meeting the criteria for inclusion in the meta-analysis; on the other hand, nearly 200 studies were discarded. The accepted studies reported results on both winter and spring types of each crop species. Relative mixing effects ranged from −30% to 100% with an overall meta-estimate of at least 2.7% (p < 0.001), reconfirming the potential of overall grain yield increase when growing varieties in mixtures. The mixing effect varied between crop types, with largest and significant effects for winter wheat and spring barley. The meta-regression demonstrated that mixing effect increased significantly with (1) diversity in reported grain yields, (2) diversity in disease resistance, and (3) diversity in weed suppressiveness, all among component varieties. Relative mixing effect was also found to increase significantly with the effective number of component varieties. The effects of the latter two differed significantly between crop types. All analyzed models had large unexplained variation between mixing effects, indicating that the variables retrievable from the published studies explained only a minority of the differences among mixtures and trials.     

The impacts of roads and other infrastructure on mammal and bird populations: A meta-analysis

Biodiversity is being lost at an increased rate as a result of human activities. One of the major threats to biodiversity is infrastructural development. We used meta-analyses to study the effects of infrastructure proximity on mammal and bird populations. Data were gathered from 49 studies on 234 mammal and bird species. The main response by mammals and birds in the vicinity of infrastructure was either avoidance or a reduced population density. The mean species abundance, relative to non-disturbed distances (MSA), was used as the effect size measure. The impact of infrastructure distance on MSA was studied using meta-analyses. Possible sources of heterogeneity in the results of the meta-analysis were explored with meta-regression.Mammal and bird population densities declined with their proximity to infrastructure. The effect of infrastructure on bird populations extended over distances up to about 1 km, and for mammal populations up to about 5 km. Mammals and birds seemed to avoid infrastructure in open areas over larger distances compared to forested areas, which could be related to the reduced visibility of the infrastructure in forested areas. We did not find a significant effect of traffic intensity on the MSA of birds. Species varied in their response to infrastructure. Raptors were found to be more abundant in the proximity of infrastructure whereas other bird taxa tended to avoid it. Abundances were affected at variable distances from infrastructure: within a few meters for small-sized mammals and up to several hundred meters for large-sized mammals.Our findings show the importance of minimizing infrastructure development for wildlife conservation in relatively undisturbed areas. By combining actual species distributions with the effect distance functions we developed, regions sensitive to infrastructure development may be identified. Additionally, the effect distance functions can be used in models in support of decision making on infrastructure planning.