BICCO-Net II. Final report to the Biological Impacts of Climate Change Observation Network (BICCO-Net) Steering Group

• BICCO-Net Phase II presents the most comprehensive single assessment of climate change impacts on UK biodiversity to date. • The results provide a valuable resource for the CCRA 2018, future LWEC report cards, the National Adaptation Programme and other policy-relevant initiatives linked to climate change impacts on biodiversity

A new approach to modelling the relationship between annual population abundance indices and weather data

Weather has often been associated with fluctuations in population sizes of species; however, it can be difficult to estimate the effects satisfactorily because population size is naturally measured by annual abundance indices whilst weather varies on much shorter timescales. We describe a novel method for estimating the effects of a temporal sequence of a weather variable (such as mean temperatures from successive months) on annual species abundance indices. The model we use has a separate regression coefficient for each covariate in the temporal sequence, and over-fitting is avoided by constraining the regression coefficients to lie on a curve defined by a small number of parameters. The constrained curve is the product of a periodic function, reflecting assumptions that associations with weather will vary smoothly throughout the year and tend to be repetitive across years, and an exponentially decaying term, reflecting an assumption that the weather from the most recent year will tend to have the greatest effect on the current population and that the effect of weather in previous years tends to diminish as the time lag increases. We have used this approach to model 501 species abundance indices from Great Britain and present detailed results for two contrasting species alongside an overall impression of the results across all species. We believe this approach provides an important advance to the challenge of robustly modelling relationships between weather and species population size

Influence of birth cohort on age of onset cluster analysis in bipolar I disorder

PURPOSE: Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database. METHODS: The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared. RESULTS: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups. CONCLUSION: These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research

Interweaving Monitoring Activities and Model Development towards Enhancing Knowledge of the Soil-Plant-Atmosphere Continuum

The study of water pathways from the soil to the atmosphere through plants-the so-called soil-plant-atmosphere continuum (SPAC)-has always been central to agronomy, hydrology, plant physiology, and other disciplines, using a wide range of approaches and tools. In recent years, we have been witnessing a rapid expansion of interweaving monitoring activities and model development related to SPAC in climatic, ecological, and applications other than the traditional agrohydrological, and it is therefore timely to review the current status of this topic and outline future directions of research. The initiative for the special section of Vadose Zone Journal on SPAC emanated from several sessions we recently organized in international conferences and meetings. With a view to the specific research questions covered in this special section, this article introduces and reviews SPAC underlying issues and then provides a brief overview of the invited contributions. We have grouped together the 15 contributions under three main sections related to the local, field, and landscape spatial scales of interests. Within these sections, the papers present their innovative results using different measuring techniques (from classic tensiometers and TDR sensors to more advanced and sophisticated equipment based on tomography and geophysics) and different modeling tools (from mechanistic models based on the Richards equation to more parametrically parsimonious hydrologic balance models). They provide a snapshot of the current state of the art while emphasizing the significant progress attained in this field of research. New technological developments and applications are also highlighted

Abstract P2-05-08: an assessment of the potential role of intracellular Ca2+store regulators in breast cancer cells

Background: Calcium (Ca2+) regulates many crucial cellular processes including cell survival, proliferation and death. Endoplasmic reticulum (ER) Ca2+ store levels are critical in the death-inducing effects of some anti-cancer agents. Modulators of ER Ca2+ signalling, such as neuronal calcium sensor-1 (NCS-1) may therefore represent new therapeutic opportunities to enhance the effect of some anti-cancer agents. NCS-1 is associated with poorer survival in breast cancer patients. However, the expression of NCS-1 in specific breast cancer molecular subtypes and its potential role in intracellular Ca2+ signalling in breast cancer cells has not been fully explored. Aim: To assess expression of NCS-1 in breast cancer molecular subtypes and assess the effect of silencing NCS-1 on intracellular Ca2+ homeostasis and on sensitivity to doxorubicin treatment in MDA-MB-231 breast cancer cells. Methods: NCS-1 levels were assessed in breast cancer molecular subtypes based on PAM50 subtyping using the TCGA public breast cancer database. Intracellular Ca2+ changes as a consequence of siRNA-mediated silencing of NCS-1 were evaluated using a Fluorescence Imaging Plate Reader (FLIPR) in MDA-MB-231 cells expressing the genetically-encoded Ca2+ indicator GCaMP6m. The effect of NCS-1 silencing on MDA-MB-231 cells treated with doxorubicin (0.03 and 1 μM, 24 h) was evaluated by cell nuclear enumeration (Hoechst 33342 staining) and the percentage of dead cells (propidium iodide staining). Images were acquired using an automated epifluorescence microscope (ImageXpress Micro). Results: Levels of NCS-1 were higher in the basal molecular subtype compared to other molecular subtypes. NCS-1 silencing promoted cell death induced by 1 μM doxorubicin. NCS-1 silencing had no major effect on cytosolic free Ca2+ levels as a result of either IP3-mediated Ca2+ store release with the purinergic receptor activator ATP or the protease activated receptor activator trypsin. However, NCS-1 silencing suppressed constitutive Ca2+ influx in MDA-MB-231 breast cancer cells. The expression of NCS-1 was positively correlated with the Ca2+ entry channel Orai1 in breast cancers on the TCGA database. Orai1 is associated with increased migration and invasiveness in some breast cancers

An indicator highlights seasonal variation in the response of Lepidoptera communities to warming

The impacts of climate change on species and ecosystems are increasingly evident. While these tend to be clearest with respect to changes in phenology and distribution ranges, there are also important consequences for population sizes and community structure. There is an urgent need to develop ecological indicators that can be used to detect climate-driven changes in ecological communities, and identify how those impacts may vary spatially. Here we describe the development of a new community-based seasonal climate change indicator that uses national population and weather indices. We test this indicator using Lepidopteran and co-located weather data collected across a range of UK Environmental Change Network (ECN) sites. We compare our butterfly indicator with estimates derived from an alternative, previously published metric, the Community Temperature Index (CTI). First, we quantified the effect of temperature on population growth rates of moths and butterflies (Species Temperature Response, STR) by modelling annual variation in national population indices as a function of nationally averaged seasonal variation in temperature, using species and weather data independent of the ECN data. Then, we calculated average STRs for annually summarised species data from each ECN site, weighted by species’ abundance, to produce the Community Temperature Response (CTR). Finally, we tested the extent to which CTR correlated with spatial variation in temperature between sites and the extent to which temporal variation in CTR tracked both annual and seasonal warming trends. Mean site CTR was positively correlated with mean site temperature for moths but not butterflies. However, spatial variation in moth communities was well explained by mean site summer temperature and butterfly communities by winter temperature, respectively accounting for 74% and 63% of variation. Temporal variation in moth and butterfly CTR within sites did not vary with the mean annual temperature but responded to variation in the mean temperature of specific seasons. There were positive correlations between moth seasonal CTRs and seasonal temperatures in winter, spring and summer; and butterfly seasonal CTRs and seasonal temperatures in winter and summer. Butterfly CTR and CTI both correlated spatially and temporally with winter temperature. Our results highlight the need for seasonality to be considered when examining the impact of climate change on communities. Seasonal CTRs may be used to track the impact of changing temperatures on biodiversity and help identify potential mechanisms by which climate change is affecting communities. In the case of Lepidoptera, our results suggest that future warming may reassemble Lepidoptera communities