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McPherson L, Badger J, Fertitta K, Gordanier M, Nemeth C, Bejder L. Quantifying the abundance and survival rates of island-associated spinner dolphins using a multi-state open robust design model. Sci Rep 2024; 14:14764. [PMID: 38926420 DOI: 10.1038/s41598-024-64220-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Spinner dolphins (Stenella longirostris subsp.) occupy the nearshore waters of several Hawaiian Islands. Due to their constrained behavioral pattern and genetic isolation, they are vulnerable to anthropogenic threats. Their occurrence and behavior are well-described, yet a lack of data on their abundance and survival rates hinders optimal conservation action. Using design-based photo-identification surveys, this study estimated the abundance, apparent survival, and emigration of spinner dolphins off the Wai'anae Coast of O'ahu through multi-state open robust design (MSORD) and POPAN modelling. Eight seasonal field seasons, (two winter, spring, summer, and autumn) each comprised of six surveys of the study area, were completed during two consecutive years. Seasonal abundance estimates derived from the best fitting model ranged from 140 (± 36.8 SE, 95% CI 84-232) to 373 (± 60.0, 95% CI 273-509) individuals and were lowest during winter seasons. The MSORD estimated a survival rate of 0.95 (± 0.02 SE) and a Markovian pattern of temporary emigration. POPAN modelling estimated a super-population size of 633 (± 78 SE, 95% CI 492-798), reflecting the total number of individual dolphins that used the study area during the entire study period. Additional research on circum- and inter-island dolphin movements around and between O'ahu and the Maui Nui region may shed light on both seasonal movement patterns and overall abundance for the O'ahu/4-Islands stock. This work represents the first systematic mark-recapture effort to assess the abundance and survival rates of these highly exposed dolphins, providing valuable insights for conservation and management.
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Affiliation(s)
- Liah McPherson
- Marine Mammal Research Program, Hawai'i Institute of Marine Biology, University of Hawai'i at Mānoa, Honolulu, HI, USA.
| | - Janelle Badger
- Cetacean Research Program, Pacific Islands Fisheries Science Center, NOAA Fisheries, Honolulu, HI, USA
| | - Kyleigh Fertitta
- Marine Mammal Research Program, Hawai'i Institute of Marine Biology, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Madison Gordanier
- Marine Mammal Research Program, Hawai'i Institute of Marine Biology, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Cameron Nemeth
- Marine Mammal Research Program, Hawai'i Institute of Marine Biology, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Lars Bejder
- Marine Mammal Research Program, Hawai'i Institute of Marine Biology, University of Hawai'i at Mānoa, Honolulu, HI, USA
- Zoophysiology, Department of Bioscience, Aarhus University, 8000, Aarhus, Denmark
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2
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Smith JW, Johnson LR, Thomas RQ. Assessing Ecosystem State Space Models: Identifiability and Estimation. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2023. [DOI: 10.1007/s13253-023-00531-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
AbstractHierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.
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Powell JH, Kalinowski ST, Taper ML, Rotella JJ, Davis CS, Garrott RA. Evidence of an Absence of Inbreeding Depression in a Wild Population of Weddell Seals ( Leptonychotes weddellii). ENTROPY (BASEL, SWITZERLAND) 2023; 25:403. [PMID: 36981292 PMCID: PMC10047074 DOI: 10.3390/e25030403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/17/2023] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
Inbreeding depression can reduce the viability of wild populations. Detecting inbreeding depression in the wild is difficult; developing accurate estimates of inbreeding can be time and labor intensive. In this study, we used a two-step modeling procedure to incorporate uncertainty inherent in estimating individual inbreeding coefficients from multilocus genotypes into estimates of inbreeding depression in a population of Weddell seals (Leptonychotes weddellii). The two-step modeling procedure presented in this paper provides a method for estimating the magnitude of a known source of error, which is assumed absent in classic regression models, and incorporating this error into inferences about inbreeding depression. The method is essentially an errors-in-variables regression with non-normal errors in both the dependent and independent variables. These models, therefore, allow for a better evaluation of the uncertainty surrounding the biological importance of inbreeding depression in non-pedigreed wild populations. For this study we genotyped 154 adult female seals from the population in Erebus Bay, Antarctica, at 29 microsatellite loci, 12 of which are novel. We used a statistical evidence approach to inference rather than hypothesis testing because the discovery of both low and high levels of inbreeding are of scientific interest. We found evidence for an absence of inbreeding depression in lifetime reproductive success, adult survival, age at maturity, and the reproductive interval of female seals in this population.
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Affiliation(s)
- John H. Powell
- Department of Ecology, Montana State University, P.O. Box 173460, Bozeman, MT 59717, USA
| | - Steven T. Kalinowski
- Department of Ecology, Montana State University, P.O. Box 173460, Bozeman, MT 59717, USA
| | - Mark L. Taper
- Department of Ecology, Montana State University, P.O. Box 173460, Bozeman, MT 59717, USA
| | - Jay J. Rotella
- Department of Ecology, Montana State University, P.O. Box 173460, Bozeman, MT 59717, USA
| | - Corey S. Davis
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Robert A. Garrott
- Department of Ecology, Montana State University, P.O. Box 173460, Bozeman, MT 59717, USA
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4
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Gao J, May MR, Rannala B, Moore BR. PrioriTree: a utility for improving phylodynamic analyses in BEAST. Bioinformatics 2023; 39:6967033. [PMID: 36592035 PMCID: PMC9841403 DOI: 10.1093/bioinformatics/btac849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/20/2022] [Accepted: 12/30/2022] [Indexed: 01/03/2023] Open
Abstract
SUMMARY Phylodynamic methods are central to studies of the geographic and demographic history of disease outbreaks. Inference under discrete-geographic phylodynamic models-which involve many parameters that must be inferred from minimal information-is inherently sensitive to our prior beliefs about the model parameters. We present an interactive utility, PrioriTree, to help researchers identify and accommodate prior sensitivity in discrete-geographic inferences. Specifically, PrioriTree provides a suite of functions to generate input files for-and summarize output from-BEAST analyses for performing robust Bayesian inference, data-cloning analyses and assessing the relative and absolute fit of candidate discrete-geographic (prior) models to empirical datasets. AVAILABILITY AND IMPLEMENTATION PrioriTree is distributed as an R package available at https://github.com/jsigao/prioritree, with a comprehensive user manual provided at https://bookdown.org/jsigao/prioritree_manual/.
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Affiliation(s)
- Jiansi Gao
- To whom correspondence should be addressed
| | - Michael R May
- Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Bruce Rannala
- Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA
| | - Brian R Moore
- Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA
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5
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Taper ML, Ponciano JM, Dennis B. Entropy, Statistical Evidence, and Scientific Inference: Evidence Functions in Theory and Applications. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1273. [PMID: 36141159 PMCID: PMC9498250 DOI: 10.3390/e24091273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Scope and Goals of the Special Issue: There is a growing realization that despite being the essential tool of modern data-based scientific discovery and model testing, statistics has major problems [...].
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Affiliation(s)
- Mark L. Taper
- Department of Ecology, Montana State University, Bozeman, MT 59717, USA
| | - José Miguel Ponciano
- Biology Department, University of Florida, Gainesville, FL 32611, USA
- Mathematics Department, University of Florida, Gainesville, FL 32611, USA
| | - Brian Dennis
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID 83844, USA
- Department of Fish and Wildlife Sciences, University of Idaho, Moscow, ID 83844, USA
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Taper ML, Lele SR, Ponciano JM, Dennis B, Jerde CL. Assessing the Global and Local Uncertainty of Scientific Evidence in the Presence of Model Misspecification. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.679155] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Scientists need to compare the support for models based on observed phenomena. The main goal of the evidential paradigm is to quantify the strength of evidence in the data for a reference model relative to an alternative model. This is done via an evidence function, such as ΔSIC, an estimator of the sample size scaled difference of divergences between the generating mechanism and the competing models. To use evidence, either for decision making or as a guide to the accumulation of knowledge, an understanding of the uncertainty in the evidence is needed. This uncertainty is well characterized by the standard statistical theory of estimation. Unfortunately, the standard theory breaks down if the models are misspecified, as is commonly the case in scientific studies. We develop non-parametric bootstrap methodologies for estimating the sampling distribution of the evidence estimator under model misspecification. This sampling distribution allows us to determine how secure we are in our evidential statement. We characterize this uncertainty in the strength of evidence with two different types of confidence intervals, which we term “global” and “local.” We discuss how evidence uncertainty can be used to improve scientific inference and illustrate this with a reanalysis of the model identification problem in a prominent landscape ecology study using structural equations.
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Helmstetter AJ, Glemin S, Käfer J, Zenil-Ferguson R, Sauquet H, de Boer H, Dagallier LPMJ, Mazet N, Reboud EL, Couvreur TLP, Condamine FL. Pulled Diversification Rates, Lineages-Through-Time Plots and Modern Macroevolutionary Modelling. Syst Biol 2021; 71:758-773. [PMID: 34613395 PMCID: PMC9016617 DOI: 10.1093/sysbio/syab083] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 11/29/2022] Open
Abstract
Estimating time-dependent rates of speciation and extinction from dated phylogenetic trees of extant species (timetrees), and determining how and why they vary, is key to understanding how ecological and evolutionary processes shape biodiversity. Due to an increasing availability of phylogenetic trees, a growing number of process-based methods relying on the birth–death model have been developed in the last decade to address a variety of questions in macroevolution. However, this methodological progress has regularly been criticized such that one may wonder how reliable the estimations of speciation and extinction rates are. In particular, using lineages-through-time (LTT) plots, a recent study has shown that there are an infinite number of equally likely diversification scenarios that can generate any timetree. This has led to questioning whether or not diversification rates should be estimated at all. Here, we summarize, clarify, and highlight technical considerations on recent findings regarding the capacity of models to disentangle diversification histories. Using simulations, we illustrate the characteristics of newly proposed “pulled rates” and their utility. We recognize that the recent findings are a step forward in understanding the behavior of macroevolutionary modeling, but they in no way suggest we should abandon diversification modeling altogether. On the contrary, the study of macroevolution using phylogenetic trees has never been more exciting and promising than today. We still face important limitations in regard to data availability and methods, but by acknowledging them we can better target our joint efforts as a scientific community. [Birth–death models; extinction; phylogenetics; speciation.]
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Affiliation(s)
- Andrew J Helmstetter
- Fondation pour la Recherche sur la Biodiversité - Centre for the Synthesis and Analysis of Biodiversity, 34000 Montpellier, France
| | - Sylvain Glemin
- CNRS, Ecosystmes Biodiversit Evolution (Universit de Rennes), 35000 Rennes, France
| | - Jos Käfer
- Universit de Lyon, Universit Lyon 1, CNRS, Laboratoire de Biomtrie et Biologie Evolutive UMR 5558, F-69622 Villeurbanne, France
| | | | - Herv Sauquet
- National Herbarium of New South Wales, Royal Botanic Gardens and Domain Trust, Sydney, New South Wales, 2000, Australia.,Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia
| | - Hugo de Boer
- Natural History Museum, University of Oslo, 0318 Oslo, Norway
| | | | - Nathan Mazet
- CNRS, Institut des Sciences de l'Evolution de Montpellier (Universit de Montpellier), Place Eugne Bataillon, 34095 Montpellier, France
| | - Eliette L Reboud
- CNRS, Institut des Sciences de l'Evolution de Montpellier (Universit de Montpellier), Place Eugne Bataillon, 34095 Montpellier, France
| | | | - Fabien L Condamine
- CNRS, Institut des Sciences de l'Evolution de Montpellier (Universit de Montpellier), Place Eugne Bataillon, 34095 Montpellier, France
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8
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Tourani M, Dupont P, Nawaz MA, Bischof R. Multiple observation processes in spatial capture-recapture models: How much do we gain? Ecology 2020; 101:e03030. [PMID: 32112415 DOI: 10.1002/ecy.3030] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 12/27/2019] [Accepted: 01/29/2020] [Indexed: 11/06/2022]
Abstract
Population monitoring data may originate from multiple methods and are often sparse and fraught with incomplete information due to practical and economic constraints. Models that can integrate multiple survey methods and are able to cope with incomplete data may help investigators exploit available information more thoroughly. Here, we developed an integrated spatial capture-recapture (SCR) model to incorporate multiple data sources with imperfect individual identification. We contrast inferences drawn from this model with alternate models incorporating only subsets of the data available. Using extensive simulations and an empirical example of multi-method brown bear (Ursus arctos) monitoring data from northern Pakistan, we quantified the benefits of including multiple sources of information in SCR models in terms of parameter precision and bias. Our multiple observation processes SCR model (MOP) yielded a more complete picture of the underlying processes, reduced bias, and led to more precise parameter estimates. Our results suggest that the greatest gains from integrated SCR models can be expected in situations where detection probability is low, a large proportion of detections is not attributable to individuals, and the degree of overlap between individual home ranges is low.
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Affiliation(s)
- Mahdieh Tourani
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
| | - Pierre Dupont
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
| | - Muhammad Ali Nawaz
- Department of Animal Sciences, Quaid-i-Azam University, Islamabad, 44000, Pakistan.,Snow Leopard Trust, Islamabad, 44000, Pakistan
| | - Richard Bischof
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
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9
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Easterday WR, Ponciano JM, Gomez JP, Van Ert MN, Hadfield T, Bagamian K, Blackburn JK, Stenseth NC, Turner WC. Coalescence modeling of intrainfection Bacillus anthracis populations allows estimation of infection parameters in wild populations. Proc Natl Acad Sci U S A 2020; 117:4273-4280. [PMID: 32054783 PMCID: PMC7049103 DOI: 10.1073/pnas.1920790117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Bacillus anthracis, the etiological agent of anthrax, is a well-established model organism. For B. anthracis and most other infectious diseases, knowledge regarding transmission and infection parameters in natural systems, in large part, comprises data gathered from closely controlled laboratory experiments. Fatal, natural anthrax infections transmit the bacterium through new host-pathogen contacts at carcass sites, which can occur years after death of the previous host. For the period between contact and death, all of our knowledge is based upon experimental data from domestic livestock and laboratory animals. Here we use a noninvasive method to explore the dynamics of anthrax infections, by evaluating the terminal diversity of B. anthracis in anthrax carcasses. We present an application of population genetics theory, specifically, coalescence modeling, to intrainfection populations of B. anthracis to derive estimates for the duration of the acute phase of the infection and effective population size converted to the number of colony-forming units establishing infection in wild plains zebra (Equus quagga). Founding populations are small, a few colony-forming units, and infections are rapid, lasting roughly between 1 d and 3 d in the wild. Our results closely reflect experimental data, showing that small founding populations progress acutely, killing the host within days. We believe this method is amendable to other bacterial diseases from wild, domestic, and human systems.
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Affiliation(s)
- W Ryan Easterday
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, 0317 Oslo, Norway
| | | | - Juan Pablo Gomez
- Departamento de Química y Biología, Universidad del Norte, 080020 Barranquilla, Colombia
| | - Matthew N Van Ert
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611
- Spatial Epidemiology & Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL 32611
| | - Ted Hadfield
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611
- Spatial Epidemiology & Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL 32611
| | - Karoun Bagamian
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611
- Spatial Epidemiology & Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL 32611
| | - Jason K Blackburn
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611
- Spatial Epidemiology & Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL 32611
| | - Nils Chr Stenseth
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, 0317 Oslo, Norway;
| | - Wendy C Turner
- Department of Biological Sciences, University at Albany, State University of New York, Albany, NY 12222
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10
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Lele SR. Consequences of Lack of Parameterization Invariance of Non-informative Bayesian Analysis for Wildlife Management: Survival of San Joaquin Kit Fox and Declines in Amphibian Populations. Front Ecol Evol 2020. [DOI: 10.3389/fevo.2019.00501] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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11
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Ferguson JM, Taper ML, Zenil-Ferguson R, Jasieniuk M, Maxwell BD. Incorporating Parameter Estimability Into Model Selection. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00427] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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12
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Ponciano JM, Taper ML. Model Projections in Model Space: A Geometric Interpretation of the AIC Allows Estimating the Distance Between Truth and Approximating Models. Front Ecol Evol 2019; 7:413. [PMID: 33796541 PMCID: PMC8011695 DOI: 10.3389/fevo.2019.00413] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Information criteria have had a profound impact on modern ecological science. They allow researchers to estimate which probabilistic approximating models are closest to the generating process. Unfortunately, information criterion comparison does not tell how good the best model is. In this work, we show that this shortcoming can be resolved by extending the geometric interpretation of Hirotugu Akaike's original work. Standard information criterion analysis considers only the divergences of each model from the generating process. It is ignored that there are also estimable divergence relationships amongst all of the approximating models. We then show that using both sets of divergences and an estimator of the negative self entropy, a model space can be constructed that includes an estimated location for the generating process. Thus, not only can an analyst determine which model is closest to the generating process, she/he can also determine how close to the generating process the best approximating model is. Properties of the generating process estimated from these projections are more accurate than those estimated by model averaging. We illustrate in detail our findings and our methods with two ecological examples for which we use and test two different neg-selfentropy estimators. The applications of our proposed model projection in model space extend to all areas of science where model selection through information criteria is done.
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Affiliation(s)
| | - Mark L. Taper
- Biology Department, University of Florida, Gainesville, FL, United States
- Department of Ecology, Montana State University, Bozeman, MT, United States
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13
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Jerde CL, Kraskura K, Eliason EJ, Csik SR, Stier AC, Taper ML. Strong Evidence for an Intraspecific Metabolic Scaling Coefficient Near 0.89 in Fish. Front Physiol 2019; 10:1166. [PMID: 31616308 PMCID: PMC6763608 DOI: 10.3389/fphys.2019.01166] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 08/28/2019] [Indexed: 12/19/2022] Open
Abstract
As an example of applying the evidential approach to statistical inference, we address one of the longest standing controversies in ecology, the evidence for, or against, a universal metabolic scaling relationship between metabolic rate and body mass. Using fish as our study taxa, we curated 25 studies with measurements of standard metabolic rate, temperature, and mass, with 55 independent trials and across 16 fish species and confronted this data with flexible random effects models. To quantify the body mass - metabolic rate relationship, we perform model selection using the Schwarz Information Criteria (ΔSIC), an established evidence function. Further, we formulate and justify the use of ΔSIC intervals to delineate the values of the metabolic scaling relationship that should be retained for further consideration. We found strong evidence for a metabolic scaling coefficient of 0.89 with a ΔSIC interval spanning 0.82 to 0.99, implying that mechanistically derived coefficients of 0.67, 0.75, and 1, are not supported by the data. Model selection supports the use of a random intercepts and random slopes by species, consistent with the idea that other factors, such as taxonomy or ecological or lifestyle characteristics, may be critical for discerning the underlying process giving rise to the data. The evidentialist framework applied here, allows for further refinement given additional data and more complex models.
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Affiliation(s)
- Christopher L. Jerde
- Marine Science Institute, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Krista Kraskura
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Erika J. Eliason
- Marine Science Institute, University of California, Santa Barbara, Santa Barbara, CA, United States
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Samantha R. Csik
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Adrian C. Stier
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Mark L. Taper
- Department of Ecology, Montana State University, Bozeman, MT, United States
- Department of Biology, University of Florida, Gainesville, FL, United States
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14
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Brown DG, Owen M. Mean and Variance of Phylogenetic Trees. Syst Biol 2019; 69:139-154. [DOI: 10.1093/sysbio/syz041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 05/13/2019] [Accepted: 05/24/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
We describe the use of the Fréchet mean and variance in the Billera–Holmes–Vogtmann (BHV) treespace to summarize and explore the diversity of a set of phylogenetic trees. We show that the Fréchet mean is comparable to other summary methods, and, despite its stickiness property, is more likely to be binary than the majority-rule consensus tree. We show that the Fréchet variance is faster and more precise than commonly used variance measures. The Fréchet mean and variance are more theoretically justified, and more robust, than previous estimates of this type and can be estimated reasonably efficiently, providing a foundation for building more advanced statistical methods and leading to applications such as mean hypothesis testing and outlier detection.
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Affiliation(s)
- Daniel G Brown
- David R. Cheriton School of Computer Science, University of Waterloo, 200 University Ave. W, Waterloo ON N2L 3G1, Canada
| | - Megan Owen
- Department of Mathematics, Lehman College, City University of New York, 250 Bedford Park Blvd West, Bronx, New York, NY 10468, USA
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15
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López-Uribe MM, Jha S, Soro A. A trait-based approach to predict population genetic structure in bees. Mol Ecol 2019; 28:1919-1929. [PMID: 30667117 DOI: 10.1111/mec.15028] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 01/11/2019] [Indexed: 02/06/2023]
Abstract
Understanding population genetic structure is key to developing predictions about species susceptibility to environmental change, such as habitat fragmentation and climate change. It has been theorized that life-history traits may constrain some species in their dispersal and lead to greater signatures of population genetic structure. In this study, we use a quantitative comparative approach to assess if patterns of population genetic structure in bees are driven by three key species-level life-history traits: body size, sociality, and diet breadth. Specifically, we reviewed the current literature on bee population genetic structure, as measured by the differentiation indices Nei's GST, Hedrick's G'ST , and Jost's D. We then used phylogenetic generalised linear models to estimate the correlation between the evolution of these traits and patterns of genetic differentiation. Our analyses revealed a negative and significant effect of body size on genetic structure, regardless of differentiation index utilized. For Hedrick's G'ST and Jost's D, we also found a significant impact of sociality, where social species exhibited lower levels of differentiation than solitary species. We did not find an effect of diet specialization on population genetic structure. Overall, our results suggest that physical dispersal or other functions related to body size are among the most critical for mediating population structure for bees. We further highlight the importance of standardizing population genetic measures to more easily compare studies and to identify the most susceptible species to landscape and climatic changes.
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Affiliation(s)
- Margarita M López-Uribe
- Department of Entomology, Center for Pollinator Research, Pennsylvania State University, University Park, Pennsylvania
| | - Shalene Jha
- Deparment of Integrative Biology, The University of Texas at Austin, Austin, Texas
| | - Antonella Soro
- Institute for Biology, Martin-Luther University, Halle (Saale), Germany
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16
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Blackburn JK, Ganz HH, Ponciano JM, Turner WC, Ryan SJ, Kamath P, Cizauskas C, Kausrud K, Holt RD, Stenseth NC, Getz WM. Modeling R₀ for Pathogens with Environmental Transmission: Animal Movements, Pathogen Populations, and Local Infectious Zones. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E954. [PMID: 30884913 PMCID: PMC6466347 DOI: 10.3390/ijerph16060954] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 03/04/2019] [Accepted: 03/07/2019] [Indexed: 01/24/2023]
Abstract
How a disease is transmitted affects our ability to determine R₀, the average number of new cases caused by an infectious host at the onset of an epidemic. R₀ becomes progressively more difficult to compute as transmission varies from directly transmitted diseases to diseases that are vector-borne to environmentally transmitted diseases. Pathogens responsible for diseases with environmental transmission are typically maintained in environmental reservoirs that exhibit a complex spatial distribution of local infectious zones (LIZs). Understanding host encounters with LIZs and pathogen persistence within LIZs is required for an accurate R₀ and modeling these contacts requires an integrated geospatial and dynamical systems approach. Here we review how interactions between host and pathogen populations and environmental reservoirs are driven by landscape-level variables, and synthesize the quantitative framework needed to formulate outbreak response and disease control.
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Affiliation(s)
- Jason K Blackburn
- Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, 3141 Turlington Hall, Gainesville, FL 32611, USA.
- Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, Gainesville, FL 32611, USA.
| | - Holly H Ganz
- Davis Genome Center, University of California, 451 Health Sciences Dr., Davis, CA 95616, USA.
| | | | - Wendy C Turner
- Department of Biological Sciences, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA.
| | - Sadie J Ryan
- Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, Gainesville, FL 32611, USA.
- Quantitative Disease Ecology & Conservation Lab, Department of Geography, University of Florida, 3141 Turlington Hall, Gainesville, FL 32611, USA.
- School of Life Sciences, University of KwaZulu-Natal, Durban 4041, South Africa.
| | - Pauline Kamath
- School of Food and Agriculture, University of Maine, 5763 Rogers Hall, Room 210, Orono, ME 04469, USA.
| | - Carrie Cizauskas
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, 130 Mulford Hall, Berkeley, CA 94720, USA.
| | - Kyrre Kausrud
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, P.O. Box 1066 Blindern, 0361 Oslo, Norway.
| | - Robert D Holt
- Department of Biology, University of Florida, Gainesville, FL 32611, USA.
| | - Nils Chr Stenseth
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, P.O. Box 1066 Blindern, 0361 Oslo, Norway.
| | - Wayne M Getz
- School of Food and Agriculture, University of Maine, 5763 Rogers Hall, Room 210, Orono, ME 04469, USA.
- School of Mathematical Sciences, University of KwaZulu-Natal, Durban 4041, South Africa.
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17
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Ferguson JM, Hopkins JB, Witteveen BH. Integrating abundance and diet data to improve inferences of food web dynamics. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jake M. Ferguson
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota St Paul MN USA
| | - John B. Hopkins
- School of Biodiversity Conservation Unity College Unity ME USA
- Division of Biological Sciences, Ecology, Behavior and Evolution Section University of California San Diego La Jolla CA USA
| | - Briana H. Witteveen
- School of Fisheries and Ocean Sciences Alaska Sea Grant Marine Advisory Program University of Alaska Fairbanks Kodiak AK USA
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18
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Zenil‐Ferguson R, Burleigh JG, Ponciano JM. chromploid: An R package for chromosome number evolution across the plant tree of life. APPLICATIONS IN PLANT SCIENCES 2018; 6:e1037. [PMID: 29732267 PMCID: PMC5895187 DOI: 10.1002/aps3.1037] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 02/26/2018] [Indexed: 05/25/2023]
Abstract
PREMISE OF THE STUDY Polyploidy has profound evolutionary consequences for land plants. Despite the availability of large phylogenetic and chromosomal data sets, estimating the rates of polyploidy and chromosomal evolution across the tree of life remains a challenging, computationally complex problem. We introduce the R package chromploid, which allows scientists to perform inference of chromosomal evolution rates across large phylogenetic trees. METHODS AND RESULTS chromploid is an open-source package in the R environment that calculates the likelihood function of models of chromosome evolution. Models of discrete character evolution can be customized using chromploid. We demonstrate the performance of the BiChroM model, testing for associations between rates of chromosome doubling (as a proxy for polyploidy) and a binary phenotypic character, within chromploid using simulations and empirical data from Solanum. In simulations, estimated chromosome-doubling rates were unbiased and the variance decreased with larger trees, but distinguishing small differences in rates of chromosome doubling, even from large data sets, remains challenging. In the Solanum data set, a custom model of chromosome number evolution demonstrated higher rates of chromosome doubling in herbaceous species compared to woody. CONCLUSIONS chromploid enables researchers to perform robust likelihood-based inferences using complex models of chromosome number evolution across large phylogenies.
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Affiliation(s)
- Rosana Zenil‐Ferguson
- Department of Biological ScienceUniversity of IdahoMoscowIdaho83844USA
- Department of Ecology, Evolution, and BehaviorUniversity of MinnesotaSt. PaulMinnesota55108USA
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19
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Gomez JP, Robinson SK, Blackburn JK, Ponciano JM. An efficient extension of N-mixture models for multi-species abundance estimation. Methods Ecol Evol 2018; 9:340-353. [PMID: 29892335 PMCID: PMC5992910 DOI: 10.1111/2041-210x.12856] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this study we propose an extension of the N-mixture family of models that targets an improvement of the statistical properties of rare species abundance estimators when sample sizes are low, yet typical for tropical studies. The proposed method harnesses information from other species in an ecological community to correct each species' estimator. We provide guidance to determine the sample size required to estimate accurately the abundance of rare tropical species when attempting to estimate the abundance of single species.We evaluate the proposed methods using an assumption of 50 m radius plots and perform simulations comprising a broad range of sample sizes, true abundances and detectability values and a complex data generating process. The extension of the N-mixture model is achieved by assuming that the detection probabilities are drawn at random from a beta distribution in a multi-species fashion. This hierarchical model avoids having to specify a single detection probability parameter per species in the targeted community. Parameter estimation is done via Maximum Likelihood.We compared our multi-species approach with previously proposed multi-species N-mixture models, which we show are biased when the true densities of species in the community are less than seven individuals per 100 hectares. The beta N-mixture model proposed here outperforms the traditional Multi-species N-mixture model by allowing the estimation of organisms at lower densities and controlling the bias in the estimation.We illustrate how our methodology can be used to suggest sample sizes required to estimate the abundance of organisms, when these are either rare, common or abundant. When the interest is full communities, we show how the multi-species approaches, and in particular our beta model and estimation methodology, can be used as a practical solution to estimate organism densities from rapid inventory datasets. The statistical inferences done with our model via Maximum Likelihood can also be used to group species in a community according to their detectabilities.
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Affiliation(s)
- Juan Pablo Gomez
- Department of Biology, University of Florida, Gainesville, Florida
- Florida Museum of Natural History, Gainesville, Florida
- Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville Florida
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida
| | | | - Jason K Blackburn
- Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville Florida
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida
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20
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Reddy S, Kimball RT, Pandey A, Hosner PA, Braun MJ, Hackett SJ, Han KL, Harshman J, Huddleston CJ, Kingston S, Marks BD, Miglia KJ, Moore WS, Sheldon FH, Witt CC, Yuri T, Braun EL. Why Do Phylogenomic Data Sets Yield Conflicting Trees? Data Type Influences the Avian Tree of Life more than Taxon Sampling. Syst Biol 2018; 66:857-879. [PMID: 28369655 DOI: 10.1093/sysbio/syx041] [Citation(s) in RCA: 171] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Accepted: 03/22/2017] [Indexed: 01/27/2023] Open
Abstract
Phylogenomics, the use of large-scale data matrices in phylogenetic analyses, has been viewed as the ultimate solution to the problem of resolving difficult nodes in the tree of life. However, it has become clear that analyses of these large genomic data sets can also result in conflicting estimates of phylogeny. Here, we use the early divergences in Neoaves, the largest clade of extant birds, as a "model system" to understand the basis for incongruence among phylogenomic trees. We were motivated by the observation that trees from two recent avian phylogenomic studies exhibit conflicts. Those studies used different strategies: 1) collecting many characters [$\sim$ 42 mega base pairs (Mbp) of sequence data] from 48 birds, sometimes including only one taxon for each major clade; and 2) collecting fewer characters ($\sim$ 0.4 Mbp) from 198 birds, selected to subdivide long branches. However, the studies also used different data types: the taxon-poor data matrix comprised 68% non-coding sequences whereas coding exons dominated the taxon-rich data matrix. This difference raises the question of whether the primary reason for incongruence is the number of sites, the number of taxa, or the data type. To test among these alternative hypotheses we assembled a novel, large-scale data matrix comprising 90% non-coding sequences from 235 bird species. Although increased taxon sampling appeared to have a positive impact on phylogenetic analyses the most important variable was data type. Indeed, by analyzing different subsets of the taxa in our data matrix we found that increased taxon sampling actually resulted in increased congruence with the tree from the previous taxon-poor study (which had a majority of non-coding data) instead of the taxon-rich study (which largely used coding data). We suggest that the observed differences in the estimates of topology for these studies reflect data-type effects due to violations of the models used in phylogenetic analyses, some of which may be difficult to detect. If incongruence among trees estimated using phylogenomic methods largely reflects problems with model fit developing more "biologically-realistic" models is likely to be critical for efforts to reconstruct the tree of life. [Birds; coding exons; GTR model; model fit; Neoaves; non-coding DNA; phylogenomics; taxon sampling.].
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Affiliation(s)
- Sushma Reddy
- Biology Department, Loyola University Chicago, 1032 West Sheridan Road, Chicago, IL 60660, USA
| | - Rebecca T Kimball
- Department of Biology, University of Florida, Gainesville, FL 32607, USA
| | - Akanksha Pandey
- Department of Biology, University of Florida, Gainesville, FL 32607, USA
| | - Peter A Hosner
- Department of Biology, University of Florida, Gainesville, FL 32607, USA.,Florida Museum of Natural History, University of Florida, Gainesville, FL 32607, USA
| | - Michael J Braun
- Behavior, Ecology, Evolution, and Systematics Program, University of Maryland, College Park, MD 20742, USA.,Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution-MRC 163, PO Box 37012, Washington, DC 20013-7012, USA
| | - Shannon J Hackett
- Zoology Department, Field Museum of Natural History, 1400 South Lake Shore Drive, Chicago, IL 60605, USA
| | - Kin-Lan Han
- Department of Biology, University of Florida, Gainesville, FL 32607, USA
| | | | - Christopher J Huddleston
- Collections Program, National Museum of Natural History, Smithsonian Institution, 4210 Silver Hill Road, Suitland, MD 20746, USA
| | - Sarah Kingston
- Behavior, Ecology, Evolution, and Systematics Program, University of Maryland, College Park, MD 20742, USA.,Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution-MRC 163, PO Box 37012, Washington, DC 20013-7012, USA.,Bowdoin College, Department of Biology and Coastal Studies Center, 6500 College Station, Brunwick, ME 04011, USA
| | - Ben D Marks
- Zoology Department, Field Museum of Natural History, 1400 South Lake Shore Drive, Chicago, IL 60605, USA
| | - Kathleen J Miglia
- Department of Biological Sciences, Wayne State University, 5047 Gullen Mall, Detroit, MI 48202, USA
| | - William S Moore
- Department of Biological Sciences, Wayne State University, 5047 Gullen Mall, Detroit, MI 48202, USA
| | - Frederick H Sheldon
- Museum of Natural Science and Department of Biological Sciences, Louisiana State University, 119 Foster Hall, Baton Rouge, LA 70803, USA
| | - Christopher C Witt
- Department of Biology and Museum of Southwestern Biology, University 15 of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Tamaki Yuri
- Department of Biology, University of Florida, Gainesville, FL 32607, USA.,Sam Noble Museum, University of Oklahoma, 2401 Chautauqua Avenue, Norman, OK 73072, USA
| | - Edward L Braun
- Department of Biology, University of Florida, Gainesville, FL 32607, USA.,Genetics Institute, University of Florida, Gainesville, FL 32607, USA
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21
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Gottard A, Calzolari G. Estimating multiple-membership logit models with mixed effects: indirect inference versus data cloning. J STAT COMPUT SIM 2017. [DOI: 10.1080/00949655.2017.1331440] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Anna Gottard
- Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy
| | - Giorgio Calzolari
- Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy
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22
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Zenil‐Ferguson R, Ponciano JM, Burleigh JG. Testing the association of phenotypes with polyploidy: An example using herbaceous and woody eudicots. Evolution 2017; 71:1138-1148. [DOI: 10.1111/evo.13226] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 02/17/2017] [Indexed: 01/08/2023]
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23
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Taper ML, Ponciano JM. Evidential statistics as a statistical modern synthesis to support 21st century science. POPUL ECOL 2015. [DOI: 10.1007/s10144-015-0533-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Mark L. Taper
- Ecology DepartmentMontana State University59717‐3460BozemanMTUSA
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24
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Technow F, Messina CD, Totir LR, Cooper M. Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation. PLoS One 2015; 10:e0130855. [PMID: 26121133 PMCID: PMC4488317 DOI: 10.1371/journal.pone.0130855] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 05/25/2015] [Indexed: 11/18/2022] Open
Abstract
Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.
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Affiliation(s)
- Frank Technow
- Breeding Technologies, DuPont Pioneer, Johnston, IA, USA
- * E-mail:
| | - Carlos D. Messina
- Trait Characterization & Development, DuPont Pioneer, Johnston, IA, USA
| | - L. Radu Totir
- Breeding Technologies, DuPont Pioneer, Johnston, IA, USA
| | - Mark Cooper
- Trait Characterization & Development, DuPont Pioneer, Johnston, IA, USA
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25
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Abstract
The Gompertz state-space (GSS) model is a stochastic model for analyzing time-series observations of population abundances. The GSS model combines density dependence, environmental process noise, and observation error toward estimating quantities of interest in biological monitoring and population viability analysis. However, existing methods for estimating the model parameters apply only to population data with equal time intervals between observations. In the present paper, we extend the GSS model to data with unequal time intervals, by embedding it within a state-space version of the Ornstein-Uhlenbeck process, a continuous-time model of an equilibrating stochastic system. Maximum likelihood and restricted maximum likelihood calculations for the Ornstein-Uhlenbeck state-space model involve only numerical maximization of an explicit multivariate normal likelihood, and so the extension allows for easy bootstrapping, yielding confidence intervals for model parameters, statistical hypothesis testing of density dependence, and selection among sub-models using information criteria. Ecologists and managers previously drawn to models lacking density dependence or observation error because such models accommodated unequal time intervals (for example, due to missing data) now have an alternative analysis framework incorporating density dependence, process noise, and observation error.
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Affiliation(s)
- Brian Dennis
- Department of Fish and Wildlife Sciences and Department of Statistical Science, University of Idaho, Moscow ID 83844-1136, USA
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26
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Ruhfel BR, Gitzendanner MA, Soltis PS, Soltis DE, Burleigh JG. From algae to angiosperms-inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes. BMC Evol Biol 2014; 14:23. [PMID: 24533922 PMCID: PMC3933183 DOI: 10.1186/1471-2148-14-23] [Citation(s) in RCA: 322] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Accepted: 01/13/2014] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Next-generation sequencing has provided a wealth of plastid genome sequence data from an increasingly diverse set of green plants (Viridiplantae). Although these data have helped resolve the phylogeny of numerous clades (e.g., green algae, angiosperms, and gymnosperms), their utility for inferring relationships across all green plants is uncertain. Viridiplantae originated 700-1500 million years ago and may comprise as many as 500,000 species. This clade represents a major source of photosynthetic carbon and contains an immense diversity of life forms, including some of the smallest and largest eukaryotes. Here we explore the limits and challenges of inferring a comprehensive green plant phylogeny from available complete or nearly complete plastid genome sequence data. RESULTS We assembled protein-coding sequence data for 78 genes from 360 diverse green plant taxa with complete or nearly complete plastid genome sequences available from GenBank. Phylogenetic analyses of the plastid data recovered well-supported backbone relationships and strong support for relationships that were not observed in previous analyses of major subclades within Viridiplantae. However, there also is evidence of systematic error in some analyses. In several instances we obtained strongly supported but conflicting topologies from analyses of nucleotides versus amino acid characters, and the considerable variation in GC content among lineages and within single genomes affected the phylogenetic placement of several taxa. CONCLUSIONS Analyses of the plastid sequence data recovered a strongly supported framework of relationships for green plants. This framework includes: i) the placement of Zygnematophyceace as sister to land plants (Embryophyta), ii) a clade of extant gymnosperms (Acrogymnospermae) with cycads + Ginkgo sister to remaining extant gymnosperms and with gnetophytes (Gnetophyta) sister to non-Pinaceae conifers (Gnecup trees), and iii) within the monilophyte clade (Monilophyta), Equisetales + Psilotales are sister to Marattiales + leptosporangiate ferns. Our analyses also highlight the challenges of using plastid genome sequences in deep-level phylogenomic analyses, and we provide suggestions for future analyses that will likely incorporate plastid genome sequence data for thousands of species. We particularly emphasize the importance of exploring the effects of different partitioning and character coding strategies.
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Affiliation(s)
- Brad R Ruhfel
- Department of Biological Sciences, Eastern Kentucky University, Richmond, KY 40475, USA
| | - Matthew A Gitzendanner
- Department of Biology, University of Florida, Gainesville, FL 32611-8525, USA
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611-7800, USA
- Genetics Institute, University of Florida, Gainesville, FL 32610, USA
| | - Pamela S Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611-7800, USA
- Genetics Institute, University of Florida, Gainesville, FL 32610, USA
| | - Douglas E Soltis
- Department of Biology, University of Florida, Gainesville, FL 32611-8525, USA
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611-7800, USA
- Genetics Institute, University of Florida, Gainesville, FL 32610, USA
| | - J Gordon Burleigh
- Department of Biology, University of Florida, Gainesville, FL 32611-8525, USA
- Genetics Institute, University of Florida, Gainesville, FL 32610, USA
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27
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Ferguson JM, Ponciano JM. Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series. Ecol Lett 2013; 17:251-9. [PMID: 24304946 PMCID: PMC3912915 DOI: 10.1111/ele.12227] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Revised: 10/15/2013] [Accepted: 10/31/2013] [Indexed: 11/29/2022]
Abstract
Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series.
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Affiliation(s)
- Jake M Ferguson
- Department of Biology, University of Florida, Gainesville, FL, USA
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28
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Barbour AB, Ponciano JM, Lorenzen K. Apparent survival estimation from continuous mark-recapture/resighting data. Methods Ecol Evol 2013. [DOI: 10.1111/2041-210x.12059] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Andrew B. Barbour
- School of Forest Resources and Conservation; Program of Fisheries and Aquatic Sciences; University of Florida; 7922 NW 71st Street; Gainesville; FL; 32653; USA
| | - José M. Ponciano
- Department of Biology; University of Florida; Gainesville; FL; 32611; USA
| | - Kai Lorenzen
- School of Forest Resources and Conservation; Program of Fisheries and Aquatic Sciences; University of Florida; 7922 NW 71st Street; Gainesville; FL; 32653; USA
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