Predicting the phenology of codling moth,Cydia pomonella,for sustainable pest management in Swiss apple orchards

The effects of temperature on post‐diapause development, female lifespan, and reproduction of Cydia pomonella L. (Lepidoptera: Tortricidae) were studied under controlled conditions to determine the respective parameters for a temperature‐driven phenology model. Lower thermal thresholds of 10.0 and 9.7 °C and thermal constants of 417.2 and 427.7 degree days were established for post‐diapause development of females and males, respectively. Female lifespan was found to be 202.6 degree days on average, with a lower thermal threshold of 11.2 °C. Total fecundity was highest at 27.7 °C with an average of 241.9 eggs per female. Literature data were used to quantify the lower thermal thresholds and the thermal constants for eggs, larvae, and pupae. For validation, the model was run with weather data from 13 consecutive years and the output was compared with pheromone trap catches from 39 to 63 different orchards, depending on the year. The model proved to be a reliable and useful tool to forecast codling moth phenology under Swiss conditions not only for a whole growing season but also during several years with very variable weather conditions. Hence, it can be used to support apple growers in pest management decisions on the one hand and to predict changes in codling moth phenology with respect to climate change on the other.     

山东稻区褐飞虱的发生规律及预测研究

褐飞虱在山东稻区一年发生3-4代．当地不能越冬,第二代是主要为害世代,发生在8月下旬至9月中旬。将影响发生的因子如虫源,降水量、气温等输入微机．筛选出的预报因子．用模糊列联表方法建立的预报矩阵,经1994年应用．准确率为100％：     

Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique

Water quality indicators can be used to characterize the status and quantify and qualify the change of aquatic ecosystems under different disturbance regimes. Although many studies have been done to develop and assess indicators and discuss interactions among them, few studies have focused on how to improve the predicted indicators and explore their variations in receiving water bodies. Accurate and effective predictions of ecological indictors are critical to better understand changes of water quality in aquatic ecosystems, especially for the real-time forecasting. Process-based water quality models can predict the spatiotemporal variations of the water quality indicators and provide useful information for policy-makers on sound management of water resources. Given their inherent constraints, however, such process models alone cannot actually guarantee perfect results since water quality models generally have a large number of parameters and involve many processes which are too complex to be efficiently calibrated. To overcome these limitations and explore a fast and efficient forecasting method for the change of water quality indictors, we proposed a new framework which combines the process-based models and data assimilation technique. Unlike most traditional approaches in which only the model parameters or initial conditions are updated or corrected and the models are run online, this framework allows the information extracted from observations and outputs of process models to be directly used in a data-driven local/modified local model. The results from the data-driven model are then assimilated into the original process model to further improve its forecasting ability. This approach can be efficiently run offline to directly correct and update the output of water quality models. We applied this framework in a real case study in Singapore. Two of the water quality indicators, namely salinity and oxygen were selected and tested against the observations, suggesting that a good performance of improving the model results and reducing computation time can be obtained. This approach is simple and efficient, especially suitable for real-time forecasting systems. Thus, it can enhance forecasting of water quality indictors and thereby facilitate the effective management of water resources.     

The influence of bat ecology on viral diversity and reservoir status

Repeated emergence of zoonotic viruses from bat reservoirs into human populations demands predictive approaches to preemptively identify virus‐carrying bat species. Here, we use machine learning to examine drivers of viral diversity in bats, determine whether those drivers depend on viral genome type, and predict undetected viral carriers. Our results indicate that bat species with longer life spans, broad geographic distributions in the eastern hemisphere, and large group sizes carry more viruses overall. Life span was a stronger predictor of deoxyribonucleic acid viral diversity, while group size and family were more important for predicting ribonucleic acid viruses, potentially reflecting broad differences in infection duration. Importantly, our models predict 54 bat species as likely carriers of zoonotic viruses, despite not currently being considered reservoirs. Mapping these predictions as a proportion of local bat diversity, we identify global regions where efforts to reduce disease spillover into humans by identifying viral carriers may be most productive.     

The ability of synthetic sex pheromone traps to forecast Plutella xylostella infestations depends on survival of immature stages

The potential for using synthetic sex pheromone traps as a simple and practical method of monitoring population densities of insect pests has been investigated in many crop systems. Yet, factors enabling the forecast of infestations based on pheromone trap catches are not fully understood. This study tested the prediction that high survival of immature stages of the target pest is a pre‐requisite for trap catches to correlate well with future infestations on the crop. The influence of parasitoids, as an important natural mortality factor of diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), larvae and pupae in South Africa, on the ability of pheromone traps to forecast infestations was investigated continuously at weekly intervals over 6 years on unsprayed cabbage. During late October–May, when parasitism levels were high (≥50%), infestations and trap catches were significantly lower than during low parasitism (<50%) observed during June–early October. Because infestations were negatively related to parasitism level, trap catches correlated with infestations better when observations were made in the same week during periods of high parasitism. Conversely, when survival of P. xylostella immature stages was high due to low parasitism levels, trap catches correlated with future infestations well for up to 2 weeks. Thus, trap catches can be used to forecast infestations during September–October, a period that coincides with high P. xylostella infestations as a consequence of low natural control by parasitoids. This is the first study to show that the ability of pheromone trap catches to forecast infestations depends on survival of the immature stages of the target pest.     

Projected impacts of climate change on spatio‐temporal patterns of freshwater fish beta diversity: a deconstructing approach

Aim To assess the potential impacts of future climate change on spatio‐temporal patterns of freshwater fish beta diversity. Location Adour–Garonne River Basin (France). Methods We first applied an ensemble modelling approach to project annually the future distribution of 18 fish species for the 2010–2100 period on 50 sites. We then explored the spatial and temporal patterns of beta diversity by distinguishing between its two additive components, namely species turnover and nestedness. Results Taxonomic homogenization of fish assemblages was projected to increase linearly over the 21st century, especially in the downstream parts of the river gradient. This homogenization process was almost entirely caused by a decrease in spatial species turnover. When considering the temporal dimension of beta diversity, our results reveal an overall pattern of decreasing beta diversity along the upstream–downstream river gradient. In contrast, when considering the turnover and nestedness components of temporal beta diversity we found significant U‐shaped and hump‐shaped relationships, respectively. Main conclusions Future climate change is projected to modify the taxonomic composition of freshwater fish assemblages by increasing their overall similarity over the Adour–Garonne River Basin. Our findings suggest that the distinction between the nestedness and turnover components of beta diversity is not only crucial for understanding the processes shaping spatial beta‐diversity patterns but also for identifying localities where the rates of species replacement are projected to be greatest. Specifically we recommend that future conservation studies should not only consider the spatial component of beta diversity but also its dynamic caused by climate warming.     

The predictability of a lake phytoplankton community,over time‐scales of hours to years

Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here, we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time‐scales. Communities were highly predictable over hours to months: model R² decreased from 0.89 at 4 hours to 0.74 at 1 month, and in a long‐term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell densities were examined separately, model‐inferred environmental growth dependencies matched laboratory studies, and suggested novel trade‐offs governing their competition. High‐frequency monitoring and machine learning can set prediction targets for process‐based models and help elucidate the mechanisms underlying ecological dynamics.     

基于Maxent模型的不同气候变化情景下我国草地螟越冬区预测

草地螟(Loxostege sticticalis L.)是我国华北、东北和西北地区农牧业生产的重大害虫,其每年暴发成灾的程度及造成的经济损失与越冬虫源基数或面积密切相关,但我国草地螟主要越冬区的变化规律至今尚未见报道。为了阐明全球气候变化条件下我国草地螟越冬区的变化规律,以1951—2000年我国草地螟越冬场所及面积为基础,结合政府间气候变化专门委员会(IPCC)第五次评估报告发布的BCC-CSM1.1气候模式数据,采用最大熵(Maxent)模型预测了4种气候变化(RCP 2.6,4.5,6.0和8.5)情景下2050s及2070s我国草地螟越冬区的变化情况。研究结果:1)训练数据集和测试数据集的受试者工作特征曲线下的面积(AUC)分别为0.989和0.987,表明模型的模拟精度很好。2)经Jackknife方法检验,降雨量变化方差bio_15是最重要的变量,最湿月份降雨量bio_13次之,最后为10月份降水量prec_10及年温变化范围bio_7。这些环境变量对模型的贡献率均超过10%,是模型构建最重要的环境变量。3)在各种气候变化情景下,到2050s及2070s我国草地螟的越冬区面积和位置相对于当前都有不同程度的扩大和北移。其中高适宜越冬区面积为当前的1.41—2.94倍,其质心位置向北移动78.79—226.97 km。这些结果表明,我国未来草地螟越冬场所将会扩大和北移。     

Species distributions models may predict accurately future distributions but poorly how distributions change: A critical perspective on model validation

Aim

Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance.

Location

Fennoscandia.

Methods

We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013–2016, ‘static validation’) and for a change between two time periods (difference between 1996–1999 and 2013–2016, ‘change validation’). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits.

Results

Even though static validation method evaluated predictive performance as good, change method indicated very poor performance. Predictive performance was not strongly related to any trait.

Main Conclusions

Static validation method might overestimate predictive performance by not revealing the model's inability to predict change events. If species' distributions remain mostly stable, then even an unfit model can predict the near future well due to temporal autocorrelation. We urge caution when working with forecasts of changes in spatial patterns of species occupancy or abundance, even for SDMs that are based on time series datasets unless they are critically validated for forecasting such change.