Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

A New Omomyid Primate from the Earliest Eocene of Southern England: First Phase of Microchoerine Evolution

Hooker, J.J. 2012. A new omomyid primate from the earliest Eocene of southern England: First phase of microchoerin

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects

Data from: Skeletal adaptations and phylogeny of the oldest mole Eotalpa (Talpidae, Lipotyphla, Mammalia) from the UK Eocene: the beginning of fossoriality in moles

The oldest talpid, Eotalpa, was previously known only from isolated cheek teeth from the European late Middle Eocene to earliest Oligocene. Screenwashing of Late Eocene sediments of the Hampshire Basin, UK, has yielded cranial and postcranial elements: maxilla, dentary, ulna, metacarpals, distal tibia, astragalus, calcaneum, metatarsals and phalanges. In addition to M1–2 myotodonty, typical talpid features are as follows: ulna with long medially curved olecranon and deep abductor fossa and astragalar body with lateral process. However, Eotalpa retains certain soricid-like primitive states (M1 preparacrista, P4 with prominent mesiolingual protocone lobe, strongly angled astragalar neck and calcaneum with no space for a cuboid medial process) not found in modern talpids. Eotalpa is more derived than the most primitive living talpid Uropsilus in having lost the M1–2 talon shelf, developed a convex radial facet on the ulna, an incipient proximal olecranon crest, relatively shorter metapodials and depressed manual unguals. Its astragalus with medial trochlear ridge taller than the lateral one and massive medial plantar process is typical of the Lipotyphla. Eotalpa lacks synostosis of tibia and fibula, found in other Talpidae, Soricidae and Erinaceidae, suggesting that synostosis in these groups has been independently acquired. Cladistic analysis places Eotalpa as stem member of the Talpidae and shows that much homoplasy arose during the early evolution of the family. Ground dwelling in Eotalpa is indicated by the following: astragalus with a medially dipping head, curved in a single plane; calcaneum with distal peroneal process and strongly overlapping ectal and sustentacular facets; and matching sized ectal and sustentacular facets on calcaneum and astragalus. These features would have restricted ankle mobility. Ungual and metatarsal shape and ulnar structure suggest a primitive stage in fossorial evolution and argue against a semiaquatic precursor stage in talpid fossoriality. Shrew-moles may represent a reversal to surface foraging rather than an intermediate stage in fossoriality

SupportingInformation

Appendix S1. Description of numbered characters used in the cladistic analysis. Appendix S2. Character-taxon matrix used in the cladistic analysis. Appendix S3. Character states at numbered nodes in the cladogram