ORCID Profile
0000-0001-9441-6645
Current Organisation
宇宙航空研究開発機構
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Statistics | Applied Statistics | Statistical Theory | Ecological Impacts of Climate Change | Environmental Science and Management | Global Change Biology | Applied Statistics | Community Ecology | Ecological Applications | Ecology And Evolution Not Elsewhere Classified | Land And Parks Management | Knowledge Representation and Machine Learning | Other Biological Sciences | Conservation and Biodiversity | Statistical Theory | Terrestrial Ecology | Natural Resource Management | Environmental Monitoring | Conservation And Biodiversity |
ANZSRC Socio-Economic Objective (SEO)
Ecosystem Adaptation to Climate Change | Expanding Knowledge in the Mathematical Sciences | Flora, Fauna and Biodiversity at Regional or Larger Scales | Climate change | Ecosystem Assessment and Management at Regional or Larger Scales | Other environmental aspects | Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Environmental Management Systems | Integrated (ecosystem) assessment and management | Integrated (ecosystem) assessment and management | Land and Water Management of environments not elsewhere classified | Living resources (flora and fauna) | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management | Global climate change adaptation measures
Related Links
Publications
Site-to-site variation in the demography of a fire-affected perennial, Acacia suaveolens, at Ku-ring-gai Chase National Park, New South Wales, Australia
Publisher: Wiley
Date: 02-2003
Model‐based assessment of ecological community classifications
Publisher: Wiley
Date: 15-03-2016
DOI: 10.1111/JVS.12400
A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix
Publisher: Wiley
Date: 02-03-2012
Order selection in finite mixture models: complete or observed likelihood information criteria?
Publisher: Oxford University Press (OUP)
Date: 12-06-2015
How many sites? Methods to assist design decisions when collecting multivariate data in ecology
Publisher: Wiley
Date: 20-04-2023
Abstract: S le size estimation through power analysis is a fundamental tool in planning an ecological study, yet there are currently no well‐established procedures for when multivariate abundances are to be collected. A power analysis procedure would need to address three challenges: designing a parsimonious simulation model that captures key community data properties measuring effect size in a realistic yet interpretable fashion and ensuring computational feasibility when simulation is used both for power estimation and significance testing. Here, we propose a power analysis procedure that addresses these three challenges by: using for simulation a Gaussian copula model with factor analytical structure, fitted to pilot data assuming a common effect size across all taxa, but applied in different directions according to expert opinion (to “increaser”, “decreaser” or “no effect” taxa) using a critical value approach to estimate power, which reduces computation time by a factor of 500 (if we would otherwise use 999 res les to estimate each p ‐value) with minor loss of accuracy. The procedure is demonstrated on pilot data from fish assemblages in a restoration study, where it was found that the planned study design would only be capable of detecting relatively large effects (change in abundance by a factor of 1.7 or more). The methods outlined in this paper are available in accompanying R software (the ecopower package), which allows researchers with pilot data to answer a wide range of design questions to assist them in planning their studies.
Model‐based approaches to unconstrained ordination
Publisher: Wiley
Date: 18-08-2014
Untangling direct species associations from indirect mediator species effects with graphical models
Publisher: Wiley
Date: 24-07-2019
Abstract: Ecologists often investigate co‐occurrence patterns in multi‐species data in order to gain insight into the ecological causes of observed co‐occurrences. Apart from direct associations between the two species of interest, they may co‐occur because of indirect effects, where both species respond to another variable, whether environmental or biotic (e.g. a mediator species). A wide variety of methods are now available for modelling how environmental filtering drives species distributions. In contrast, methods for studying other causes of co‐occurence are much more limited. “Graphical” methods, which can be used to study how mediator species impact co‐occurrence patterns, have recently been proposed for use in ecology. However, available methods are limited to presence/absence data or methods assuming multivariate normality, which is problematic when analysing abundances. We propose Gaussian copula graphical models (GCGMs) for studying the effect of mediator species on co‐occurence patterns. GCGMs are a flexible type of graphical model which naturally accommodates all data types , for ex le binary (presence/absence), counts, as well as ordinal data and biomass, in a unified framework. Simulations demonstrate that GCGMs can be applied to a much broader range of data types than the methods currently used in ecology, and perform as well as or better than existing methods in many settings. We apply GCGMs to counts of hunting spiders, in order to visualise associations between species. We also analyse abundance data of New Zealand native forest cover (on an ordinal scale) to show how GCGMs can be used analyse large and complex datasets. In these data, we were able to reproduce known species relationships as well as generate new ecological hypotheses about species associations.
Untangling direct species associations from mediator species effects with graphical models
Publisher: Cold Spring Harbor Laboratory
Date: 19-11-2018
DOI: 10.1101/470161
Abstract: Ecologists often investigate co-occurrence patterns in multi-species data in order to gain insight into the ecological causes of observed co-occurrences. Apart from direct associations between two species, two species may co-occur because they both respond in similar ways to environmental variables, or due to the presence of other (mediator) species. A wide variety of methods are now available for modelling how environmental filtering drives species distributions. In contrast, methods for studying other causes of co-occurence are much more limited. “Graphical” methods, which can be used to study how mediator species impact co-occurrence patterns, have recently been proposed for use in ecology. However, available methods are limited to presence/absence data and methods assuming multivariate normality, which is problematic when analysing abundances. We propose Gaussian copula graphical models (GCGMs) for studying the effect of mediator species on co-occurence patterns. GCGMs are a flexible type of graphical model which naturally accommodates all data types – binary (presence/absence), counts, as well as ordinal data and biomass, in a unified framework. Simulations for count data demonstrate that GCGMs are better able to distinguish effects of mediator species from direct associations than using existing methods designed for multivariate normal data. We apply GCGMs to counts of hunting spiders, in order to visualise associations between species. We then analyze abundance data of New Zealand native forest cover (on an ordinal scale) to show how GCGMs can be used analyze large and complex datasets. In these data, we were able to reproduce known species relationships as well as generate new ecological hypotheses about species associations.
Model-based thinking for community ecology
Publisher: Springer Science and Business Media LLC
Date: 19-11-2014
The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses
Publisher: Public Library of Science (PLoS)
Date: 24-07-2017
Point process models for presence‐only analysis
Publisher: Wiley
Date: 21-03-2015
Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Publisher: Wiley
Date: 03-03-2017
DOI: 10.1111/ECOG.02881
smatr 3– an R package for estimation and inference about allometric lines
Publisher: Wiley
Date: 29-09-2011
Actionable Knowledge Discovery
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-026-4.CH002
Abstract: Actionable knowledge discovery is selected as one of the greatest challenges (Ankerst, 2002 Fayyad, Shapiro, & Uthurusamy, 2003) of next-generation knowledge discovery in database (KDD) studies (Han & Kamber, 2006). In the existing data mining, often mined patterns are nonactionable to real user needs. To enhance knowledge actionability, domain-related social intelligence is substantially essential (Cao et al., 2006b). The involvement of domain-related social intelligence into data mining leads to domaindriven data mining (Cao & Zhang, 2006a, 2007a), which complements traditional data-centered mining methodology. Domain-related social intelligence consists of intelligence of human, domain, environment, society and cyberspace, which complements data intelligence. The extension of KDD toward domain-driven data mining involves many challenging but promising research and development issues in KDD. Studies in regard to these issues may promote the paradigm shift of KDD from data-centered interesting pattern mining to domain-driven actionable knowledge discovery, and the deployment shift from simulated data set-based to real-life data and business environment-oriented as widely predicted.
Fast forward selection for generalized estimating equations with a large number of predictor variables
Publisher: Wiley
Date: 18-12-2013
DOI: 10.1111/BIOM.12118
Abstract: We propose a new variable selection criterion designed for use with forward selection algorithms the score information criterion (SIC). The proposed criterion is based on score statistics which incorporate correlated response data. The main advantage of the SIC is that it is much faster to compute than existing model selection criteria when the number of predictor variables added to a model is large, this is because SIC can be computed for all candidate models without actually fitting them. A second advantage is that it incorporates the correlation between variables into its quasi-likelihood, leading to more desirable properties than competing selection criteria. Consistency and prediction properties are shown for the SIC. We conduct simulation studies to evaluate the selection and prediction performances, and compare these, as well as computational times, with some well-known variable selection criteria. We apply the SIC on a real data set collected on arthropods by considering variable selection on a large number of interactions terms consisting of species traits and environmental covariates.
Finite Mixture of Regression Modeling for High-Dimensional Count and Biomass Data in Ecology
Publisher: Springer Science and Business Media LLC
Date: 16-05-2013
The time value of leaf area
Publisher: University of Chicago Press
Date: 05-2000
DOI: 10.1086/303346
Abstract: When a plant invests in construction of a leaf, the revenue-stream that accrues is shaped by three variables: first, the light-capture area per milligram dry mass invested, analogous to a potential rate of return on investment second, the longevity of the leaf, analogous to the expected duration of the revenue stream and third, a time-discount rate, quantifying the fact that light-capture area deployed in the immediate future is more valuable to the plant than the same area deployed at some later time. Recent comparative data make it possible to quantify the cross-species trade-off between the first variable and the second variable. Here we develop an approach through which the consequences of the third variable, the time-discount rate, can be related to the trade-off between the first variable and the second variable. The approach involves an equal-benefit set, the cross-species equivalent of a fitness set. A wide spread of strategies is actually observed to coexist in vegetation, from low to high light capture area per gram and, correspondingly, from high to low leaf longevity. The coexistence suggests that the different observed strategies do not have a clear-cut advantage over the other. The equal-benefit set can be used to investigate what levels of time discount would make it the case that neither the highest-longevity nor the highest light-capture area per milligram strategies would have a clear advantage over the other, with regard to the time-discounted value of the revenue stream generated per milligram invested in leaf.
Pebbles and sand on asteroid (162173) Ryugu: In situ observation and particles returned to Earth
Publisher: American Association for the Advancement of Science (AAAS)
Date: 04-03-2022
Abstract: The Hayabusa2 spacecraft investigated the C-type (carbonaceous) asteroid (162173) Ryugu. The mission performed two landing operations to collect s les of surface and subsurface material, the latter exposed by an artificial impact. We present images of the second touchdown site, finding that ejecta from the impact crater was present at the s le location. Surface pebbles at both landing sites show morphological variations ranging from rugged to smooth, similar to Ryugu’s boulders, and shapes from quasi-spherical to flattened. The s les were returned to Earth on 6 December 2020. We describe the morphology of grams of returned pebbles and sand. Their erse color, shape, and structure are consistent with the observed materials of Ryugu we conclude that they are a representative s le of the asteroid.
First asteroid gas sample delivered by the Hayabusa2 mission: A treasure box from Ryugu.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 18-11-2022
Abstract: The Hayabusa2 spacecraft returned to Earth from the asteroid 162173 Ryugu on 6 December 2020. One day after the recovery, the gas species retained in the s le container were extracted and measured on-site and stored in gas collection bottles. The container gas consists of helium and neon with an extraterrestrial
Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference
Publisher: Wiley
Date: 11-06-2018
DOI: 10.1002/ECM.1309
Modelling of wildlife fatality hotspots along the Snowy Mountain Highway in New South Wales, Australia
Publisher: Elsevier BV
Date: 12-2005
Modulation of leaf economic traits and trait relationships by climate
Publisher: Wiley
Date: 18-08-2005
Sprouting by semi-arid plants
Publisher: Wiley
Date: 09-09-2004
Frequent inundation helps counteract land use impacts on wetland propagule banks
Publisher: Wiley
Date: 07-02-2017
DOI: 10.1111/AVSC.12295
Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology
Publisher: Wiley
Date: 04-02-2013
DOI: 10.1111/J.1541-0420.2012.01824.X
Abstract: Modeling the spatial distribution of a species is a fundamental problem in ecology. A number of modeling methods have been developed, an extremely popular one being MAXENT, a maximum entropy modeling approach. In this article, we show that MAXENT is equivalent to a Poisson regression model and hence is related to a Poisson point process model, differing only in the intercept term, which is scale-dependent in MAXENT. We illustrate a number of improvements to MAXENT that follow from these relations. In particular, a point process model approach facilitates methods for choosing the appropriate spatial resolution, assessing model adequacy, and choosing the LASSO penalty parameter, all currently unavailable to MAXENT. The equivalence result represents a significant step in the unification of the species distribution modeling literature.
Ryugu: A brand-new planetary sample returned from a C-type asteroid
Publisher: Research Square Platform LLC
Date: 21-06-2021
DOI: 10.21203/RS.3.RS-608561/V1
Abstract: C-type asteroids are considered to be primitive small Solar-System bodies enriched in water and organics, providing clues for understanding the origin and evolution of the Solar System and the building blocks of life. C-type asteroid 162173 Ryugu has been characterized by remote sensing and on-asteroid measurements with Hayabusa2, but further studies are expected by direct analyses of returned s les. Here we describe the bulk s le mainly consisting of rugged and smooth particles of millimeter to submillimeter size, preserving physical and chemical properties as they were on the asteroid. The particle size distribution is found steeper than that of surface boulders11. Estimated grain densities of the s les have a peak around 1350 kg m-3, which is lower than that of meteorites suggests a high micro-porosity down to millimeter-scale, as estimated at centimeter-scale by thermal measurements. The extremely dark optical to near-infrared reflectance and the spectral profile with weak absorptions at 2.7 and 3.4 microns implying carbonaceous composition with indigenous aqueous alteration, respectively, match the global average of Ryugu, confirming the s le’s representativeness. Together with the absence of chondrule and Ca-Al-rich inclusion of larger than sub-mm, these features indicate Ryugu is most similar to CI chondrites but with darker, more porous and fragile characteristics.
Multi-species distribution modeling using penalized mixture of regressions
Publisher: Institute of Mathematical Statistics
Date: 06-2015
DOI: 10.1214/15-AOAS813
Modeling recreational fishing intensity in a complex urbanised estuary
Publisher: Elsevier BV
Date: 02-2021
Assessing the generality of global leaf trait relationships
Publisher: Wiley
Date: 03-02-2005
DOI: 10.1111/J.1469-8137.2005.01349.X
Abstract: Global-scale quantification of relationships between plant traits gives insight into the evolution of the world's vegetation, and is crucial for parameterizing vegetation-climate models. A database was compiled, comprising data for hundreds to thousands of species for the core 'leaf economics' traits leaf lifespan, leaf mass per area, photosynthetic capacity, dark respiration, and leaf nitrogen and phosphorus concentrations, as well as leaf potassium, photosynthetic N-use efficiency (PNUE), and leaf N : P ratio. While mean trait values differed between plant functional types, the range found within groups was often larger than differences among them. Future vegetation-climate models could incorporate this knowledge. The core leaf traits were intercorrelated, both globally and within plant functional types, forming a 'leaf economics spectrum'. While these relationships are very general, they are not universal, as significant heterogeneity exists between relationships fitted to in idual sites. Much, but not all, heterogeneity can be explained by variation in s le size alone. PNUE can also be considered as part of this trait spectrum, whereas leaf K and N : P ratios are only loosely related.
Bivariate line-fitting methods for allometry
Publisher: Wiley
Date: 30-03-2006
Preliminary analysis of the Hayabusa2 samples returned from C-type asteroid Ryugu
Publisher: Springer Science and Business Media LLC
Date: 20-12-2021
DOI: 10.1038/S41550-021-01550-6
Abstract: C-type asteroids 1 are considered to be primitive small Solar System bodies enriched in water and organics, providing clues to the origin and evolution of the Solar System and the building blocks of life. C-type asteroid 162173 Ryugu has been characterized by remote sensing 2–7 and on-asteroid measurements 8,9 with Hayabusa2 (ref. 10 ). However, the ground truth provided by laboratory analysis of returned s les is invaluable to determine the fine properties of asteroids and other planetary bodies. We report preliminary results of analyses on returned s les from Ryugu of the particle size distribution, density and porosity, spectral properties and textural properties, and the results of a search for Ca–Al-rich inclusions (CAIs) and chondrules. The bulk s le mainly consists of rugged and smooth particles of millimetre to submillimetre size, confirming that the physical and chemical properties were not altered during the return from the asteroid. The power index of its size distribution is shallower than that of the surface boulder observed on Ryugu 11 , indicating differences in the returned Ryugu s les. The average of the estimated bulk densities of Ryugu s le particles is 1,282 ± 231 kg m −3 , which is lower than that of meteorites 12 , suggesting a high microporosity down to the millimetre scale, extending centimetre-scale estimates from thermal measurements 5,9 . The extremely dark optical to near-infrared reflectance and spectral profile with weak absorptions at 2.7 and 3.4 μm imply a carbonaceous composition with indigenous aqueous alteration, matching the global average of Ryugu 3,4 and confirming that the s le is representative of the asteroid. Together with the absence of submillimetre CAIs and chondrules, these features indicate that Ryugu is most similar to CI chondrites but has lower albedo, higher porosity and more fragile characteristics.
DO SMALL-SEEDED SPECIES HAVE HIGHER SURVIVAL THROUGH SEED PREDATION THAN LARGE-SEEDED SPECIES?
Publisher: Wiley
Date: 12-2003
DOI: 10.1890/02-0662
What is the effective sample size of a spatial point process?
Publisher: Wiley
Date: 03-2021
DOI: 10.1111/ANZS.12337
Abstract: Point process models are a natural approach for modelling data that arise as point events. In the case of Poisson counts, these may be fitted easily as a weighted Poisson regression. Point processes lack the notion of s le size. This is problematic for model selection, because various classical criteria such as the Bayesian information criterion (BIC) are a function of the s le size, n , and are derived in an asymptotic framework where n tends to infinity. In this paper, we develop an asymptotic result for Poisson point process models in which the observed number of point events, m , plays the role that s le size does in the classical regression context. Following from this result, we derive a version of BIC for point process models, and when fitted via penalised likelihood, conditions for the LASSO penalty that ensure consistency in estimation and the oracle property. We discuss challenges extending these results to the wider class of Gibbs models, of which the Poisson point process model is a special case.
Invasions: the trail behind, the path ahead, and a test of a disturbing idea
Publisher: Wiley
Date: 13-12-2011
Leaf economics fundamentals explained by optimality principles
Publisher: American Association for the Advancement of Science (AAAS)
Date: 20-01-2023
Abstract: The life span of leaves increases with their mass per unit area (LMA). It is unclear why. Here, we show that this empirical generalization (the foundation of the worldwide leaf economics spectrum) is a consequence of natural selection, maximizing average net carbon gain over the leaf life cycle. Analyzing two large leaf trait datasets, we show that evergreen and deciduous species with erse construction costs (assumed proportional to LMA) are selected by light, temperature, and growing-season length in different, but predictable, ways. We quantitatively explain the observed ergent latitudinal trends in evergreen and deciduous LMA and show how local distributions of LMA arise by selection under different environmental conditions acting on the species pool. These results illustrate how optimality principles can underpin a new theory for plant geography and terrestrial carbon dynamics.
Variational Approximations for Generalized Linear Latent Variable Models
Publisher: Informa UK Limited
Date: 02-01-2017
Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes
Publisher: Informa UK Limited
Date: 06-04-2023
Effect of contact load upon attrition-corrosion of human dental enamel
Publisher: Elsevier BV
Date: 11-2018
gllvm: Fast analysis of multivariate abundance data with generalized linear latent variable models inr
Publisher: Wiley
Date: 22-10-2019
Tuning Parameter Selection for the Adaptive Lasso Using ERIC
Publisher: Informa UK Limited
Date: 02-01-2015
To mix or not to mix: comparing the predictive performance of mixture models vs. separate species distribution models
Publisher: Wiley
Date: 09-2013
DOI: 10.1890/12-1322.1
Abstract: Species distribution models (SDMs) are an important tool for studying the patterns of species across environmental and geographic space. For community data, a common approach involves fitting an SDM to each species separately, although the large number of models makes interpretation difficult and fails to exploit any similarities between in idual species responses. A recently proposed alternative that can potentially overcome these difficulties is species archetype models (SAMs), a model-based approach that clusters species based on their environmental response. In this paper, we compare the predictive performance of SAMs against separate SDMs using a number of multi-species data sets. Results show that SAMs improve model accuracy and discriminatory capacity compared to separate SDMs. This is achieved by borrowing strength from common species having higher information content. Moreover, the improvement increases as the species become rarer.
Evidence at hand: Diversity, functional implications, and locomotor prediction in intrinsic hand proportions of diprotodontian marsupials
Publisher: Wiley
Date: 2006
DOI: 10.1002/JMOR.10495
Abstract: Knowledge about the ersity, locomotor adaptations, and evolution of the marsupial forelimb is limited, resulting in an underrepresentation of marsupials in comparative anatomical literature on mammalian forelimb anatomy. This study investigated hand proportions in the erse marsupial order Diprotodontia. Fifty-two measurements of 95 specimens representing 47 species, as well as 6 non-diprotodontian specimens, were explored using principal components analysis (PCA). Bootstrapping was used to assess the reliability of the loadings. Phylogenetically independent contrasts and phylogenetic ANOVA were used to test for correlation with size and functional adaptation of forelimbs for locomotor habit, scored as arboreal vs. terrestrial. Analysis of first principal component (PC1) scores revealed significant differences between arboreal and terrestrial species, and was related to relative slenderness of their phalangeal elements. Both locomotor groups displayed allometry along PC1 scores, but with different intercepts such that PC1 discriminated between the two locomotor habits almost completely. PC2 separated some higher-level clades and burrowing species. Analysis of locomotor predictors commonly applied by palaeontologists indicates that ratios between proximal and intermediate phalanges were unsuitable as predictors of arboreality/terrestriality, but the phalangeal index was more effective. From PCA results, a phalangeal slenderness ratio was developed which proved to be a useful discriminator, suggesting that a single unallocated phalanx can be used for an impression of locomotor mode in fossils. Most Diprotodontia are laterally paraxonic or ectaxonic, with the exception of digging species whose hands are medially paraxonic. Our results complement those of studies on placental mammals, suggesting that the demands of arboreality, terrestriality, or frequent digging on intrinsic hand proportions are met with similar anatomical adaptations in marsupials.
Model-Based Control of Observer Bias for the Analysis of Presence-Only Data in Ecology
Publisher: Public Library of Science (PLoS)
Date: 18-11-2013
Topoclimate versus macroclimate: how does climate mapping methodology affect species distribution models and climate change projections?
Publisher: Wiley
Date: 22-05-2014
DOI: 10.1111/DDI.12216
Analyzing environmental‐trait interactions in ecological communities with fourth‐corner latent variable models
Publisher: Wiley
Date: 25-05-2021
DOI: 10.1002/ENV.2683
Abstract: In ecological community studies it is often of interest to study the effect of species related trait variables on abundances or presence‐absences. Specifically, the interest may lay in the interactions between environmental and trait variables. An increasingly popular approach for studying such interactions is to use the so‐called fourth‐corner model, which explicitly posits a regression model where the mean response of each species is a function of interactions between covariate and trait predictors (among other terms). On the other hand, many of the fourth‐corner models currently applied in the literature are too simplistic to properly account for variation in environmental and trait response and any residual covariation between species. To overcome this problem, we propose a fourth‐corner latent variable model which combines the following three features: latent variables to capture the correlation between species, fourth‐corner terms to account for environment‐trait interactions, and species‐specific random slopes for modeling excess heterogeneity between species in their environmental response. We perform an extensive numerical study comparing a variety of fourth‐corner models available in the literature which account for the aforementioned sources of variation to varying degrees. Simulation results demonstrate that the proposed fourth‐corner latent variable models performed well when testing for the fourth‐corner (interaction) coefficients, across both Type I error and power. By comparison, some models that do not full account for all relevant sources of variation suffer from inflated Type I error leading to potentially misleading inference. The proposed method is illustrated by an ex le on ground beetle data.
Extending Joint Models in Community Ecology: A Response to Beissinger et al .
Publisher: Elsevier BV
Date: 10-2016
A general algorithm for covariance modeling of discrete data
Publisher: Elsevier BV
Date: 05-2018
Global patterns in plant height
Publisher: Wiley
Date: 09-2009
The central role of mean‐variance relationships in the analysis of multivariate abundance data: a response to Roberts (2017)
Publisher: Wiley
Date: 26-07-2017
Fast model‐based ordination with copulas
Publisher: Wiley
Date: 18-11-2021
Abstract: Visualising data is a key step in data analysis, allowing researchers to find patterns, and assess and communicate the results of statistical modelling. In ecology, visualisation is often challenging when there are many variables (often for different species or other taxonomic groups) and they are not normally distributed (often counts or presence–absence data). Ordination is a common and powerful way to overcome this hurdle by reducing data from many response variables to just two or three, to be easily plotted. Ordination is traditionally done using dissimilarity‐based methods, most commonly non‐metric multidimensional scaling (nMDS). In the last decade, however, model‐based methods for unconstrained ordination have gained popularity. These are primarily based on latent variable models, with latent variables estimating the underlying, unobserved ecological gradients. Despite some major benefits, a drawback of model‐based ordination methods is their speed, as they typically take much longer to return a result than dissimilarity‐based methods, especially for large s le sizes. We introduce copula ordination, a new, scalable model‐based approach to unconstrained ordination. This method has all the desirable properties of model‐based ordination methods, with the added advantage that it is computationally far more efficient. In particular, simulations show copula ordination is an order of magnitude faster than current model‐based methods, and can even be faster than nMDS for large s le sizes, while being able to produce similar ordination plots and trends as these methods.
Samples returned from the asteroid Ryugu are similar to Ivuna-type carbonaceous meteorites
Publisher: American Association for the Advancement of Science (AAAS)
Date: 24-02-2023
Abstract: Carbonaceous meteorites are thought to be fragments of C-type (carbonaceous) asteroids. S les of the C-type asteroid (162173) Ryugu were retrieved by the Hayabusa2 spacecraft. We measured the mineralogy and bulk chemical and isotopic compositions of Ryugu s les. The s les are mainly composed of materials similar to those of carbonaceous chondrite meteorites, particularly the CI (Ivuna-type) group. The s les consist predominantly of minerals formed in aqueous fluid on a parent planetesimal. The primary minerals were altered by fluids at a temperature of 37° ± 10°C, about 5.2 − 0.7 + 0.8 million (statistical) or 5.2 − 2.1 + 1.6 million (systematic) years after the formation of the first solids in the Solar System. After aqueous alteration, the Ryugu s les were likely never heated above ~100°C. The s les have a chemical composition that more closely resembles that of the Sun’s photosphere than other natural s les do.
Generalized Linear Latent Variable Models for Multivariate Count and Biomass Data in Ecology
Publisher: Springer Science and Business Media LLC
Date: 24-08-2017
Seed size and survival in the soil in arid Australia
Publisher: Wiley
Date: 10-2003
Efficient estimation of generalized linear latent variable models
Publisher: Public Library of Science (PLoS)
Date: 05-2019
Does a latitudinal gradient in seedling survival favour larger seeds in the tropics?
Publisher: Wiley
Date: 12-08-2004
Selecting the model for multiple imputation of missing data: Just use an IC!
Publisher: Wiley
Date: 24-02-2021
DOI: 10.1002/SIM.8915
Spatiotemporal joint species distribution modelling: A basis function approach
Publisher: Wiley
Date: 10-07-2023
Abstract: We introduce community‐level basis function models (CBFMs) as an approach for spatiotemporal joint distribution modelling. CBFMs can be viewed as related to spatiotemporal latent variable models, where the latent variables are replaced by a set of pre‐specified spatiotemporal basis functions which are common across species. In a CBFM, the coefficients that link the basis functions to each species are treated as random slopes. As such, the CBFM can be formulated to have a similar structure to a generalised additive model. This allows us to adapt existing techniques to fit CBFMs efficiently. CBFMs can be used for a variety of reasons, such as inferring patterns of habitat use in space and time, understanding how residual covariation between species varies spatially and/or temporally, and spatiotemporal predictions of species‐ and community‐level quantities. A simulation study and an application to data from a bottom trawl survey conducted across the U.S. Northeast shelf show that CBFMs can achieve similar and sometimes better predictive performance compared to existing approaches for spatiotemporal joint species distribution modelling, while being computationally more scalable.
The arcsine is asinine: the analysis of proportions in ecology
Publisher: Wiley
Date: 2011
DOI: 10.1890/10-0340.1
Abstract: The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Ex les are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.
Does morphology predict trophic position and habitat use of ant species and assemblages?
Publisher: Springer Science and Business Media LLC
Date: 07-10-2015
DOI: 10.1007/S00442-014-3101-9
Abstract: A functional traits-based theory of organismal communities is critical for understanding the principles underlying community assembly, and predicting responses to environmental change. This is particularly true for terrestrial arthropods, of which only 20% are described. Using epigaeic ant assemblages, we asked: (1) can we use morphological variation among species to predict trophic position or preferred microhabitat (2) does the strength of morphological associations suggest recent trait ergence (3) do environmental variables at site scale predict trait sets for whole assemblages? We pitfall-trapped ants from a revegetation chronosequence and measured their morphology, trophic position [using C:N stoichiometry and stable isotope ratios (δ)] and characteristics of microhabitat and macrohabitat. We found strong associations between high trophic position (low C:N and high δ(15)N) in body tissue and morphological traits: predators were larger, had more laterally positioned eyes, more physical protection and tended to be monomorphic. In addition, morphological traits were associated with certain microhabitat features, e.g. smaller heads were associated with the bare ground microhabitat. Trait-microhabitat relationships were more pronounced when phylogenetic adjustments were used, indicating a strong influence of recent trait ergences. At the assemblage level, our fourth corner analysis revealed associations between the prevalence of traits and macrohabitat, although these associations were not the same as those based on microhabitat associations. This study shows direct links between species-level traits and both diet and habitat preference. Trait-based prediction of ecological roles and community structure is thus achievable when integrating stoichiometry, morphology and phylogeny, but scale is an important consideration in such predictions.
Plant traits of propagule banks and standing vegetation reveal flooding alleviates impacts of agriculture on wetland restoration
Publisher: Wiley
Date: 15-05-2017
Graphical diagnostics for occupancy models with imperfect detection
Publisher: Wiley
Date: 04-2017
Technical advances at the interface between ecology and statistics: improving the biodiversity knowledge generation workflow
Publisher: Wiley
Date: 04-2017
mvabund– an R package for model‐based analysis of multivariate abundance data
Publisher: Wiley
Date: 21-02-2012
Global patterns of leaf mechanical properties
Publisher: Wiley
Date: 25-01-2011
DOI: 10.1111/J.1461-0248.2010.01582.X
Abstract: Leaf mechanical properties strongly influence leaf lifespan, plant-herbivore interactions, litter decomposition and nutrient cycling, but global patterns in their interspecific variation and underlying mechanisms remain poorly understood. We synthesize data across the three major measurement methods, permitting the first global analyses of leaf mechanics and associated traits, for 2819 species from 90 sites worldwide. Key measures of leaf mechanical resistance varied c. 500-800-fold among species. Contrary to a long-standing hypothesis, tropical leaves were not mechanically more resistant than temperate leaves. Leaf mechanical resistance was modestly related to rainfall and local light environment. By partitioning leaf mechanical resistance into three different components we discovered that toughness per density contributed a surprisingly large fraction to variation in mechanical resistance, larger than the fractions contributed by lamina thickness and tissue density. Higher toughness per density was associated with long leaf lifespan especially in forest understory. Seldom appreciated in the past, toughness per density is a key factor in leaf mechanical resistance, which itself influences plant-animal interactions and ecosystem functions across the globe.
The fourth-corner solution - using predictive models to understand how species traits interact with the environment
Publisher: Wiley
Date: 04-2014
Responses of foliage-living spider assemblage composition and traits to a climatic gradient in Themeda grasslands
Publisher: Wiley
Date: 10-10-2015
DOI: 10.1111/AEC.12195
Tropical plants do not have narrower temperature tolerances, but are more at risk from warming because they are close to their upper thermal limits
Publisher: Wiley
Date: 08-06-2020
DOI: 10.1111/GEB.13117
Abstract: Tropical species are thought to be more susceptible to climate warming than are higher latitude species. This prediction is largely based on the assumption that tropical species can tolerate a narrower range of temperatures. While this prediction holds for some animal taxa, we do not yet know the latitudinal trends in temperature tolerance for plants. We aim to address this knowledge gap and establish if there is a global trend in plant warming risk. Global. Present–2070. Plants. We used 9,737 records for 1,312 species from the Kew Gardens’ global germination database to quantify global patterns in germination temperature. We found no evidence for a latitudinal gradient in the breadth of temperatures at which plant species can germinate. However, tropical plants are predicted to face the greatest risk from climate warming, because they experience temperatures closer to their upper germination limits. By 2070, over half (79/142) of tropical plant species are predicted to experience temperatures exceeding their optimum germination temperatures, with some even exceeding their maximum germination temperature (41/190). Conversely, 95% of species at latitudes above 45° are predicted to benefit from warming, with environmental temperatures shifting closer to the species’ optimal germination temperatures. The prediction that tropical plant species would be most at risk under future climate warming was supported by our data, but through a different mechanism to that generally assumed.
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels
Publisher: Wiley
Date: 12-06-2019
DOI: 10.1002/ECM.1370
Order selection and sparsity in latent variable models via the ordered factor LASSO
Publisher: Wiley
Date: 11-05-2018
DOI: 10.1111/BIOM.12888
Abstract: Generalized linear latent variable models (GLLVMs) offer a general framework for flexibly analyzing data involving multiple responses. When fitting such models, two of the major challenges are selecting the order, that is, the number of factors, and an appropriate structure for the loading matrix, typically a sparse structure. Motivated by the application of GLLVMs to study marine species assemblages in the Southern Ocean, we propose the Ordered Factor LASSO or OFAL penalty for order selection and achieving sparsity in GLLVMs. The OFAL penalty is the first penalty developed specifically for order selection in latent variable models, and achieves this by using a hierarchically structured group LASSO type penalty to shrink entire columns of the loading matrix to zero, while ensuring that non-zero loadings are concentrated on the lower-order factors. Simultaneously, in idual element sparsity is achieved through the use of an adaptive LASSO. In conjunction with using an information criterion which promotes aggressive shrinkage, simulation shows that the OFAL penalty performs strongly compared with standard methods and penalties for order selection, achieving sparsity, and prediction in GLLVMs. Applying the OFAL penalty to the Southern Ocean marine species dataset suggests the available environmental predictors explain roughly half of the total covariation between species, thus leading to a smaller number of latent variables and increased sparsity in the loading matrix compared to a model without any covariates.
Distance‐based multivariate analyses confound location and dispersion effects
Publisher: Wiley
Date: 06-06-2011
So Many Variables: Joint Modeling in Community Ecology
Publisher: Elsevier BV
Date: 12-2015
DOI: 10.1016/J.TREE.2015.09.007
Abstract: Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by ex le and discuss recent computation tools and future directions.
Are Introduced Species Better Dispersers Than Native Species? A Global Comparative Study of Seed Dispersal Distance
Publisher: Public Library of Science (PLoS)
Date: 20-06-2013
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