The multidimensionality of the niche reveals functional diversity changes in benthic marine biotas across geological time

Abstract Despite growing attention on the influence of functional diversity changes on ecosystem functioning, a palaeoecological perspective on the long-term dynamic of functional diversity, including mass extinction crises, is still lacking. Here, using a novel multidimensional functional framework and comprehensive null-models, we compare the functional structure of Cambrian, Silurian and modern benthic marine biotas. We demonstrate that, after controlling for increases in taxonomic diversity, functional richness increased incrementally between each time interval with benthic taxa filling progressively more functional space, combined with a significant functional dissimilarity between periods. The modern benthic biota functionally overlaps with fossil biotas but some modern taxa, especially large predators, have new trait combinations that may allow more functions to be performed. From a methodological perspective, these results illustrate the benefits of using multidimensional instead of lower dimensional functional frameworks when studying changes in functional diversity over space and time

The Geozoic Supereon

Geological time units are the lingua franca of earth sciences: they are a terminological convenience, a vernacular of any geological conversation, and a prerequisite of geo-scientific writing found throughout in earth science dictionaries and textbooks. Time units include terms formalized by stratigraphic committees as well as informal constructs erected ad hoc to communicate more efficiently. With these time terms we partition Earth’s history into utilitarian and intuitively understandable time segments that vary in length over seven orders of magnitude: from the 225-year-long Anthropocene (Crutzen and Stoermer, 2000) to the ,4-billion-year-long Precambrian (e.g., Hicks, 1885; Ball, 1906; formalized by De Villiers, 1969)

Measurement-Based Automatic Parameterization of a Virtual Acoustic Room Model

Modernien auralisaatiotekniikoiden ansiosta kuulokkeilla voidaan tuottaa kuuntelukokemus, joka muistuttaa useimpien äänitteiden tuotannossa oletettua kaiutinkuuntelua. Huoneakustinen mallinnus on tärkeä osa toimivaa auralisaatiojärjestelmää. Huonemallinnuksen parametrien määrittäminen vaatii kuitenkin ammattitaitoa ja aikaa. Tässä työssä kehitetään järjestelmä parametrien automaattiseksi määrittämiseksi huoneakustisten mittausten perusteella. Parametrisaatio perustuu mikrofoniryhmällä mitattuihin huoneen impulssivasteisiin ja voidaan jakaa kahteen osaan: suoran äänen ja aikaisten heijastusten analyysiin sekä jälkikaiunnan analyysiin. Suorat äänet erotellaan impulssivasteista erilaisia signaalinkäsittelytekniikoita käyttäen ja niitä hyödynnetään heijastuksia etsivässä algoritmissa. Äänilähteet ja heijastuksia vastaavat kuvalähteet paikannetaan saapumisaikaeroon perustuvalla paikannusmenetelmällä ja taajuusriippuvat etenemistien vaikutukset arvioidaan kuvalähdemallissa käyttöä varten. Auralisaation jälkikaiunta on toteutettu takaisinkytkevällä viiveverkostomallilla. Sen parametrisointi vaatii taajuusriippuvan jälkikaiunta-ajan ja jälkikaiunnan taajuusvasteen määrittämistä. Normalisoitua kaikutiheyttä käytetään jälkikaiunnan alkamisajan löytämiseen mittauksista ja simuloidun jälkikaiunnan alkamisajan asettamiseen. Jälkikaiunta-aikojen määrittämisessä hyödynnetään energy decay relief -metodia. Kuuntelukokeiden perusteella automaattinen parametrisaatiojärjestelmä tuottaa parempia tuloksia kuin parametrien asettaminen manuaalisesti huoneen summittaisten geometriatietojen pohjalta. Järjestelmässä on ongelmia erityisesti jälkikaiunnan ekvalisoinnissa, mutta käytettyihin suhteellisen yksinkertaisiin tekniikoihin nähden järjestelmä toimii hyvin.Modern auralization techniques enable making the headphone listening experience similar to the experience of listening with loudspeakers, which is the reproduction method most content is made to be listened with. Room acoustic modeling is an essential part of a plausible auralization system. Specifying the parameters for room modeling requires expertise and time. In this thesis, a system is developed for automatic analysis of the parameters from room acoustic measurements. The parameterization is based on room impulse responses measured with a microphone array and can be divided into two parts: the analysis of the direct sound and early reflections, and the analysis of the late reverberation. The direct sounds are separated from the impulse responses using various signal processing techniques and used in the matching pursuit algorithm to find the reflections in the impulse responses. The sound sources and their reflection images are localized using time difference of arrival -based localization and frequency-dependent propagation path effects are estimated for use in an image source model. The late reverberation of the auralization is implemented using a feedback delay network. Its parameterization requires the analysis of the frequency-dependent reverberation time and frequency response of the late reverberation. Normalized echo density is used to determine the beginning of the late reverberation in the measurements and to set the starting point of the modeled late field. The reverberation times are analyzed using the energy decay relief. A formal listening test shows that the automatic parameterization system outperforms parameters set manually based on approximate geometrical data. Problems remain especially in the precision of the late reverberation equalization but the system works well considering the relative simplicity of the processing methods used

Supplementary Figure 7

Comparing statistical dynamics for different ecospace framework structures: varying character types, (A) factor, (B) ordered factor, (C) ordered numeric, and (D) binary. Each framework had 15 characters, four states per character (except for binary, which had two binary states per character), and five seed species. Trends in total variance were excluded in parts A and B because the inclusion of factors prevented their calculation. Other simulation details and graphical interpretation are the same as in Supplementary Figure 6. Dynamics are generally similar, but frameworks built with ordered factors performed substantially better (94% of trained models classified correctly) than the others (78% for unordered factors, 79% for ordered numerics, and 81% for binaries). See Supplementary Appendix 2 for additional details

Data from: General models of ecological diversification. II. Simulations and empirical applications

Models of functional ecospace diversification within life-habit frameworks (functional-trait spaces) are increasingly used across community ecology, functional ecology, and paleoecology. In general, these models can be represented by four basic processes, three that have driven causes and one that occurs through a passive process. The driven models include redundancy (caused by forms of functional canalization), partitioning (specialization), and expansion (divergent novelty), but they also share important dynamical similarities with the passive neutral model. In this second of two companion articles, Monte Carlo simulations of these models are used to illustrate their basic statistical dynamics across a range of data structures and implementations. Ecospace frameworks with greater numbers of characters (functional traits) and ordered (multistate) character types provide more distinct dynamics and greater ability to distinguish the models, but the general dynamics tend to be congruent across all implementations. Classification-tree methods are proposed as a powerful means to select among multiple candidate models when using multivariate data sets. Well-preserved Late Ordovician (type Cincinnatian) samples from the Kope and Waynesville formations are used to illustrate how these models can be inferred in empirical applications. Initial simulations overestimate the ecological disparity of actual assemblages, confirming that actual life habits are highly constrained. Modifications incorporating more realistic assumptions (such as weighting potential life habits according to actual frequencies and adding a parameter controlling the strength of each model’s rules) provide better correspondence to actual assemblages. Samples from both formations are best fit by partitioning (and to lesser extent redundancy) models, consistent with a role for local processes. When aggregated as an entire formation, the Kope Formation pool remains best fit by the partitioning model, whereas the entire Waynesville pool is better fit by the redundancy model, implying greater beta diversity within this unit. The ‘ecospace’ package is provided to implement the simulations and to calculate their dynamics using the R statistical language

Supplementary Figure 6

Comparing statistical dynamics for different ecospace framework structures: varying number of characters, (A) 5 characters, (B) 15 characters, and (C) 25 characters. Each framework had mixed character types, in identical proportions (40% binary, 20% three-state factor, 20% five-state factor, and, 20% five-state ordered numeric character types). 5 "seed" species were chosen at random to begin each simulation. Other simulation details and graphical interpretation are the same as is Figure 2. Trends in total variance were excluded because the inclusion of factors prevented their calculation. The dynamics are generally similar, although larger frameworks allow modestly more powerful model selection using classification-tree methods (83%, 85%, and 86% of training models, respectively, classified correctly using classification-tree methods). See Supplementary Appendix 2 for additional details

Life-habit/functional-trait codings for the Kope and Waynesville Formation species pool

KWTraits.csv is a comma-separated value (.csv) format file listing the aggregate species pool for the Kope and Waynesville Formation used in empirical analyses. (The file is also included as a data file within the 'ecospace' R package.) The first three columns list taxonomic information. The remaining columns list ecospace character states (functional traits). See supplementary appendix A and Novack-Gottshall (2007) for information on characters and states. See text for explanation of how multistate characters were rescaled

Supplementary Appendices 1-4 for manuscript

Appendix 1 gives an example of how life-habit character states were inferred and coded. Appendix 2 describes technical details on classification tree methods and confusion matrices. Appendix 3-4 give further details for the other Supplementary data files on Data Dryad

Three-model model-selection support data files for Kope and Waynesville Formation samples, stratigraphic section, member, and formation aggregates

File is in comma-separated value (.csv) format. The first five columns describe the Paleobiology Database collection identification number, scale (hand sample, stratigraphic section, etc.) of the sample, and stratigraphic/section names. Columns 6–14 list sample size (S, species richness) and values for eight disparity statistics (with NA designating when a statistic could not be calculated, because there were fewer than four unique life habits in the sample); see text for descriptions and abbreviations of statistics. The last column identifies which model has the best support among those candidates considered. The remaining columns list the classification-tree support each sample has for each candidate model considered. emp3-modelfits.csv lists model support for the tree trained on 50%, 90%, and 100% training data