A comparison between data requirements and availability for calibrating predictive ecological models for lowland UK woodlands: learning new tricks from old trees

Woodlands provide valuable ecosystem services, and it is important to understand their dynamics. To predict the way in which these might change, we need process‐based predictive ecological models, but these are necessarily very data intensive. We tested the ability of existing datasets to provide the parameters necessary to instantiate a well‐used forest model (SORTIE) for a well‐studied woodland (Wytham Woods). Only five of SORTIE's 16 equations describing different aspects of the life history and behavior of individual trees could be parameterized without additional data collection. One age class – seedlings – was completely missed as they are shorter than the height at which Diameter at Breast Height (DBH) is measured. The mensuration of trees has changed little in the last 400 years (focussing almost exclusively on DBH) despite major changes in the nature of the source of value obtained from trees over this time. This results in there being insufficient data to parameterize process‐based models in order to meet the societal demand for ecological prediction. We do not advocate ceasing the measurement of DBH, but we do recommend that those concerned with tree mensuration consider whether additional measures of trees could be added to their data collection protocols. We also see advantages in integrating techniques such as ground‐based LIDAR or remote sensing techniques with long‐term datasets to both preserve continuity with what has been performed in the past and to expand the range of measurements made.     

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.     

Global vegetation productivity response to climatic oscillations during the satellite era

Climate control on global vegetation productivity patterns has intensified in response to recent global warming. Yet, the contributions of the leading internal climatic variations to global vegetation productivity are poorly understood. Here, we use 30 years of global satellite observations to study climatic variations controls on continental and global vegetation productivity patterns. El Niño‐Southern Oscillation (ENSO) phases (La Niña, neutral, and El Niño years) appear to be a weaker control on global‐scale vegetation productivity than previously thought, although continental‐scale responses are substantial. There is also clear evidence that other non‐ENSO climatic variations have a strong control on spatial patterns of vegetation productivity mainly through their influence on temperature. Among the eight leading internal climatic variations, the East Atlantic/West Russia Pattern extensively controls the ensuing year vegetation productivity of the most productive tropical and temperate forest ecosystems of the Earth's vegetated surface through directionally consistent influence on vegetation greenness. The Community Climate System Model (CCSM4) simulations do not capture the observed patterns of vegetation productivity responses to internal climatic variations. Our analyses show the ubiquitous control of climatic variations on vegetation productivity and can further guide CCSM and other Earth system models developments to represent vegetation response patterns to unforced variability. Several winter time internal climatic variation indices show strong potentials on predicting growing season vegetation productivity two to six seasons ahead which enables national governments and farmers forecast crop yield to ensure supplies of affordable food, famine early warning, and plan management options to minimize yield losses ahead of time.     

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 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.     

Foreseeing the future of mutualistic communities beyond collapse

Changing conditions may lead to sudden shifts in the state of ecosystems when critical thresholds are passed. Some well‐studied drivers of such transitions lead to predictable outcomes such as a turbid lake or a degraded landscape. Many ecosystems are, however, complex systems of many interacting species. While detecting upcoming transitions in such systems is challenging, predicting what comes after a critical transition is terra incognita altogether. The problem is that complex ecosystems may shift to many different, alternative states. Whether an impending transition has minor, positive or catastrophic effects is thus unclear. Some systems may, however, behave more predictably than others. The dynamics of mutualistic communities can be expected to be relatively simple, because delayed negative feedbacks leading to oscillatory or other complex dynamics are weak. Here, we address the question of whether this relative simplicity allows us to foresee a community's future state. As a case study, we use a model of a bipartite mutualistic network and show that a network's post‐transition state is indicated by the way in which a system recovers from minor disturbances. Similar results obtained with a unipartite model of facilitation suggest that our results are of relevance to a wide range of mutualistic systems.     

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.     

Forecasting olive (Olea europaea) crop production by monitoring airborne pollen

Forecasting harvests of olives destined for the production of olive oil can be based on counts of airborne olive pollen, and meteorological and agronomic observations. This study was carried out during six consecutive years (1990–1995) in the Campiña Alta (an olive-producing region in the province of Córdoba, south-west Spain). Olive pollen totals are the annual sum of the concentrations recorded for the periods that the filters of a Cour trap were exposed. The meteorological data are the values of accumulated rainfall between 1 September and the following 15 April (a date prior to the beginning of olive flowering). The agronomic data are the forecast and actual productions for the province of Córdoba, supplied by the Board of Agriculture of the Andalusian government, and the actual production of the Campiña Alta, supplied at the end of harvest by private olive-growing co-operatives. The data were combined, and four mathematical equations were obtained to forecast the crop 6 months in advance, with varying degrees of reliability. The reliability was very high for an appropriate agricultural area. The most accurate equation isY=?1.90×10⁴+2.35X+53.94 (which forecasts the production of the Campiña Alta), whereY is the olive production (MT),X the olive pollen count,Z the rainfall prior to flowering, anda, b andc are constants. The least accurate equation is that relating olive pollen concentrations with olive production in the province of Córdoba.