Predicting adaptation of phenology in response to climate change, an insect herbivore example

Climate change has led to an advance in phenology in many species. Synchrony in phenology between different species within a food chain may be disrupted if an increase in temperature affects the phenology of the different species differently, as is the case in the winter moth egg hatch–oak bud burst system. Operophtera brumata (winter moth) egg hatch date has advanced more than Quercus robur (pedunculate oak) bud burst date over the past two decades. Disrupted synchrony will lead to selection, and a response in phenology to this selection may lead to species genetically adapting to their changing environment. However, a prerequisite for such genetic change is that there is sufficient genetic variation and severe enough fitness consequences. So far, examples of observed genetic change have been few. Using a half-sib design, we demonstrate here that O. brumata egg-hatching reaction norm is heritable, and that genetic variation exists. Fitness consequences of even a few days difference between egg hatch and tree bud opening are severe, as we experimentally determined. Estimates of genetic variation and of fitness were then combined with a climate scenario to predict the rate and the amount of change in the eggs' response to temperature. We predict a rapid response to selection, leading to a restoration of synchrony of egg hatch with Q. robur bud opening. This study shows that in this case there is a clear potential to adapt – rapidly – to environmental change. The current observed asynchrony is therefore not due to a lack of genetic variation and at present it is unclear what is constraining O. brumata to adapt. This kind of model may be particularly useful in gaining insight in the predicted amount and rate of change due to environmental changes, given a certain genetic variation and selection pressure.     

Influence of geographical extent on typology- and model-based assessments of taxonomic completeness of river macroinvertebrates

1. River InVertebrate Prediction and Classification System (RIVPACS)‐type predictive models are increasingly used to assess the biological condition of freshwaters, but management schemes may also be based on a priori groupings of similar water bodies (typologies) to control for natural variation in biota. The two approaches may lead to disagreements of the biological status of a site, depending on, for example, the spatial scale at which assessments are conducted. 2. We used data from 96 reference and 134 potentially impacted sites from Western and Central Finland to compare RIVPACS‐type models and a simple size‐based typology of rivers for the assessment of taxonomic completeness (the quotient of the Observed‐to‐Expected number of predicted taxa, O / E) of riffle macroinvertebrates. We specifically examined how geographical extent influences bioassessment performance (accuracy, precision and sensitivity to detect impact) of the two approaches. To fully examine the behaviour of the O / E‐index with the two approaches at differing spatial scales, we performed all assessments with a full range of thresholds for predicted taxa occurrence probabilities (p_t from 0+ to 0.9). 3. Both approaches performed consistently better than the corresponding null models. At the larger extent (i.e. assessment encompassing the whole study area), the RIVPACS‐approach performed in all aspects better than the typology‐approach. However, at the smaller extent (i.e. regional assessments) the RIVPACS‐type models and the typologies showed similar accuracy to predict the actual fauna (mean E), similar precision (SD) of cross‐validated O / E and similar sensitivity to detect sites with human impairment. 4. SD(O / E) decreased (i.e. precision increased) consistently with increasing p_t. However, both approaches were most sensitive at intermediate p_t:s (c. 0.2–0.6) when taxa with low predicted occurrence probabilities were excluded. 5. Our results show that RIVPACS‐type predictive models are less susceptible to variations in spatial scale, whereas the performance of a priori typologies increases with decreasing spatial extent. Thus, RIVPACS‐type models are more efficient for large‐scale bioassessments, but at restricted spatial scales, or with an otherwise biologically meaningful stratification, simple a priori classifications can be equally useful for the assessment of taxonomic completeness of river macroinvertebrates.     

Functional indicators of stream health: a river‐basin approach

1. The efficacy of leaf‐litter decomposition, sediment respiration, biofilm biomass, growth, chlorophyll a concentration and the autotrophic index (biofilm ash‐free dry mass/chlorophyll a) and fungal biomass for detecting human‐induced change was evaluated using 24 references and 15 disturbed stream sites located in central Portugal. 2. Decomposition rates of alder (Alnus glutinosa) and oak (Quercus robur) leaves and sediment respiration rates were effective in discriminating impairment. Decomposition was negatively correlated with abiotic factors, such as ammonium and nitrite concentrations, connectivity and alterations in the hydrological regime, and positively correlated with nitrate concentration and oxygen concentration. Sediment respiration rates were correlated with organic contamination, land use and morphological changes. 3. Growth rates of biofilm, concentration of chlorophyll a and the autotrophic index, although 41–73% higher at disturbed compared to reference sites, were not significantly different. These three variables were significantly correlated with total organic carbon, oxygen concentration, pH, nitrite and the presence of dams. Fungal biomass on leaves and biofilm biomass on natural substrata did not differ between reference and disturbed sites. 4. Our findings lend support to the use of functional variables like decomposition and sediment respiration in monitoring and when used together with structural variables should give a more holistic measure of stream health.     

Model‐free feedback design for a mixed cancer therapy

In this article, a model‐free feedback control design is proposed for the drug administration in mixed cancer therapy. This strategy is very attractive because of the important issue of parameter uncertainties unavoidable when dealing with biological models. The proposed feedback scheme use past measurements to update an on‐line simplified model. The control design is then based on model predictive control in which a suitable switching is performed between two different cost functions. The effectiveness of the proposed model‐free control strategy is validated using a recently developed model (unknown to the controller) governing the cancer growth on a cells population level under combined immune and chemotherapy and using real human data. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Will reduced host connectivity curb the spread of a devastating epidemic?

The white‐nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans, is threatening the cave‐dwelling bat fauna of North America by killing individuals by the thousands in hibernacula each winter since its appearance in New York State less than ten years ago. Epidemiological models predict that WNS will reach the western coast of the USA by 2035, potentially eliminating most populations of susceptible bat species in its path (Frick et al. 2015; O'Regan et al. 2015). These models were built and validated using distributional data from the early years of the epidemic, which spread throughout eastern North America following a route driven by cave density and winter severity (Maher et al. 2012). In this issue of Molecular Ecology, Wilder et al. (2015) refine these findings by showing that connectivity among host populations, as assessed by population genetic markers, is crucial in determining the spread of the pathogen. Because host connectivity is much reduced in the hitherto disease free western half of North America, Wilder et al. make the reassuring prediction that the disease will spread more slowly west of the Great Plains.     

From phenotype to genotype: a Bayesian solution

The study of biological systems commonly depends on inferring the state of a 'hidden' variable, such as an underlying genotype, from that of an 'observed' variable, such as an expressed phenotype. However, this cannot be achieved using traditional quantitative methods when more than one genetic mechanism exists for a single observable phenotype. Using a novel latent class Bayesian model, it is possible to infer the prevalence of different genetic elements in a population given a sample of phenotypes. As an exemplar, data comprising phenotypic resistance to six antimicrobials obtained from passive surveillance of Salmonella Typhimurium DT104 are analysed to infer the prevalence of individual resistance genes, as well as the prevalence of a genomic island known as SGI1 and its variants. Three competing models are fitted to the data and distinguished between using posterior predictive p-values to assess their ability to predict the observed number of unique phenotypes. The results suggest that several SGI1 variants circulate in a few fixed forms through the population from which our data were derived. The methods presented could be applied to other types of phenotypic data, and represent a useful and generic mechanism of inferring the genetic population structure of organisms.     

Finding fossils in new ways: an artificial neural network approach to predicting the location of productive fossil localities

Chance and serendipity have long played a role in the location of productive fossil localities by vertebrate paleontologists and paleoanthropologists. We offer an alternative approach, informed by methods borrowed from the geographic information sciences and using recent advances in computer science, to more efficiently predict where fossil localities might be found. Our model uses an artificial neural network (ANN) that is trained to recognize the spectral characteristics of known productive localities and other land cover classes, such as forest, wetlands, and scrubland, within a study area based on the analysis of remotely sensed (RS) imagery. Using these spectral signatures, the model then classifies other pixels throughout the study area. The results of the neural network classification can be examined and further manipulated within a geographic information systems (GIS) software package. While we have developed and tested this model on fossil mammal localities in deposits of Paleocene and Eocene age in the Great Divide Basin of southwestern Wyoming, a similar analytical approach can be easily applied to fossil-bearing sedimentary deposits of any age in any part of the world. We suggest that new analytical tools and methods of the geographic sciences, including remote sensing and geographic information systems, are poised to greatly enrich paleoanthropological investigations, and that these new methods should be embraced by field workers in the search for, and geospatial analysis of, fossil primates and hominins.