BACKGROUND: The international invasive and quarantined defoliating insect Hyphantria cunea Drury (Lepidoptera: Arctiidae) causes huge ecological and economic losses in the world. Furthermore, future climate change may alter the distribution of H. cunea and aggravate the damage. In the present study, we used CLIMEX to project the potential global distribution of H. cunea according to both historical climate data (1961–1990) and future climate warming estimates (2011–2100) to define the impact of climate change.RESULTS: Under the historical climate scenario, we found that H. cunea can survive on every continent, and temperature is the main factor that limits its establishment. With climate change, suitability will increase in middle and high latitude regions, while decrease in the low latitude regions. Moreover, tropic regions will be the most sensitive to climate change impacts for the pest to survive. The impacts of climate change will also increase over time, whether they be positive impacts or negative impacts.CONCLUSION: The projected potential distributions provide a theoretical basis for quarantine and control strategies for the management of this pest in each country. Furthermore, these results provide substantial guidance for studies of the effects of climate change on other major forest pests. © 2018 Society of Chemical Industry

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

Pine wilt disease is one of the most severe and devastating diseases affecting pine forests worldwide, resulting in huge economic losses in many countries. The pinewood nematode (PWN), Bursaphelenchus xylophilus, is the causal agent of pine wilt disease and is obligately vectored by pine sawyer beetles, of the genus Monochamus. For the disease to be present, the habitat must be suitable for the PWN, and include at least one vector species, and at least one host species. To predict its potential distribution, a model must consider all three components. However, no comprehensive study has examined the influence of climatic suitability on the distribution of this “biological complex”. This study addresses this gap by incorporating biotic interactions, specifically involving 13 vectors and 61 host plants, into projections based on the PWN model. We predicted the global potential distribution of pine wilt disease and compared it with the PWN model to highlight the importance of including biotic interactions in species distribution models under climate change. We found that the model revealed an overall trend of increasing suitability scores for both the PWN and pine wilt disease models under future climate scenarios. Furthermore, compared to the PWN model, the biotic model results in an apparent increase in suitability worldwide in the future as the climate will be more suitable to vector and host complexes, suggesting that pine wilt disease could potentially spread to other places via available hosts and vectors. Synthesis and applications. By incorporating biotic interactions, we projected a more accurate suitable area for pine wilt disease, offering valuable insights into regions at high risk for future invasions by the disease and its vectors. This information supports the development of management and early detection strategies in areas of high suitability, helping to mitigate potential economic and ecological losses. Additionally, this study introduces a novel approach for integrating biotic factors into species distribution models.