ISM Symposium on
Environmental Statistics 2014
Date & Time: Feb. 5(Wed), 2014 , 10:30~16:45
Venue: The Institute of Statistical Mathematics,
10-3 Midori-cho Tachikawa, Tokyo 190-8562, Japan(2F Auditorium)

Speakers:

・Alan Welsh(The Australian National University)
・Richard Huggins (The University of Melbourne)
・Anne Chao (National Tsing Hua University)
・Shuangzhe Liu(University of Canberra)
・Kunio Shimizu (Keio University)
・Hideyasu Shimadzu(University of St Andrews)
・Keiichi Fukaya (The Institute of Statistical Mathematics)
Aim of Symposium:
In order to enhance the understanding of the global environment, statistical science is extremely
important. Centered around the topic of directional statistics, we are holding a symposium inorder to better develop research on statistical theory which can be applied to solve specificissues in the fields of environmental and ecological data.

Host Organization:The Institute of Statistical Mathematics
Organizing Committee:
Koji Kanefuji (ISM), Alan Welsh (ANU), Kenichiro Shimatani (ISM),
Atsushi Yoshimoto (ISM), Kunio Shimizu (Keio University)

ISM Symposium on Environmental Statistics 2014(Second Report)
Date & Time
Feb. 5 (Wed), 2014 10:00～17:30
Venue2rd Floor Auditorium
The Institute of Statistical Mathematics,
10-3 Midori-cho Tachikawa, Tokyo 190-8562, JapanTopics
In order to enhance the understanding of the global environment, statistical science isextremely important. Centered around the topic of directional statistics, we are holding
a symposium in order to better develop research on statistical theory which can beapplied to solve specific issues in the fields of environmental and ecological data.
Joint Hosting
Ministry of Education, Culture, Sports, Science and Technology (MEXT)
Organizers
Koji Kanefuji (ISM)
Alan Welsh (ANU)
Atsushi Yoshimoto (ISM)
Kunio Shimizu (Keio University)
Kenichiro Shimatani (ISM)Program(Tentative)

10:30-10:40 Opening Address:

10:40-11:20 The prediction of random effects in hurdle models
E. Cantoni*1, J. Mills Flemming*2 and A.H. Welsh*3(*1:The University of Geneva, *2:Dalhousie University, *3:The
Australian National University)
11:20-12:00 Coverage-based rarefaction and extrapolation: standardizing
samples by completeness rather than size
Anne Chao (Institute of Statistics, National Tsing HuaUniversity)
Lunch
13:00-13:40 Semivarying coefficient models for capture-recapture data:Colony size estimation for the little penguin
Richard Huggins (Department of Mathematics and Statistics,
The University of Melbourne)
13:40-14:20 Estimation of Markov transition probabilities for ecologicalcommunities using dynamic site occupancy models
Keiichi Fukaya (The Institute of Statistical Mathematics)
14:20-15:00 Investigating species interactions in a fish community
Hideyasu Shimadzu, Maria Dornelas, Peter A. Henderson, AnneE. Magurran(School of Biology, University of St Andrews)
Coffee Break
15:10-15:50

A new probability model for cylindrical data
Kunio Shimizu(Department of Mathematics, Keio University)
15:50-16:30 Influence diagnostics in regression and time series models ofcircular data.
Shuangzhe Liu(Department of Mathematics and Statistics,
University of Canberra)
16:30-16:35 Closing address

[1]The prediction of random effects in hurdle models
E. Cantoni*1, J. Mills Flemming*2 and A.H. Welsh*3*1:The University of Geneva, *2:Dalhousie University, *3:The Australian
National University
For many endangered marine species, such as sharks, the only available
data on their abundance are counts of when they are caught unintentionallyin a fishery (that is, as bycatch). These data typically involve a largernumber of zero counts (indicating that none were caught as bycatch in aparticular haul, for example) and very few positive counts (obtained if one ormore are caught as bycatch in a haul) and are clustered because hauls are
clustered within trips which may also be clustered within vessels. We are
therefore interested in fitting models to the data which accommodate thezeros and the clustering, and then using these models to properly answer
important scientific questions such as predicting the probability of bycatch(and other cluster specific targets).
We will discuss the prediction of random effects and functions of random
effects in the context of fitting hurdle models (also referred to as two-part,zero-altered or separated models) with random effects for modelling
clustered count data with excess zeros. We implement empirical best
predictors of the random effects and other cluster specific targets. We discuss
estimating their prediction mean squared errors using a fast bootstrap
approach. The methodology is validated through simulation anddemonstrated using real data on critically endangered hammerhead sharks
where the prediction of cluster specific targets is essential for informing
conservation and management decisions.

[2]Coverage-based rarefaction and extrapolation: standardizing samples bycompleteness rather than size
ANNE CHAO(Institute of Statistics, National Tsing Hua University)
In this talk, I will review a recent proposal of an integrated sampling,rarefaction, and extrapolation methodology to compare species richness of aset of communities based on samples of equal completeness (as measured bysample coverage) instead of equal size. The concept of “sample coverage” (orsimply “coverage”) was originally developed by the founder of modern
computer science, Alan Turing, and I. J. Good. It is a measure of sample
completeness, giving (in our context) the proportion of the total number ofindividuals in a community that belong to the species represented in thesample. Traditional rarefaction or extrapolation to equal-sized samples canmisrepresent the relationships between the richnesses of the communities
being compared, because a sample of a given size may be sufficient to fully
characterize the lower-diversity community but insufficient to characterize
the richer community. Thus the traditional method systematically biases thedegree of differences between community richnesses. A new analytic method
for seamless coverage-based rarefaction and extrapolation was recently
developed by Chao and Jost (2012). I will use examples to show that thismethod yields less-biased comparisons of richness between communities, andmanages this with less total sampling effort. Several hypothetical and realexamples demonstrate these advantages. I will also briefly mention theextension of this new rarefaction/extrapolation method to other measures of
biodiversity, including Shannon diversity (the exponential of entropy) andSimpson diversity (the inverse Simpson concentration).
(This is a joint work with Lou Jost)

[3]Semivarying coefficient models for capture-recapture data: Colony size
estimation for the little penguin
Richard Huggins(Department of Mathematics and Statistics, The University of Melbourne)
To accommodate seasonal effects that change from year to year into models
for the size of an open population we consider a time-varying coefficientmodel. We fit this model to a capture-recapture data set collected on the littlepenguin in south-eastern Australia over a 25 year period, using Jolly-Sebertype estimators and nonparametric P-spline techniques. The time-varying
coefficient model identified strong changes in the seasonal pattern across theyears which we further examine using functional data analysis techniques.
(Joint work with Jakub Stoklosa of The University of New South Wales andPeter Dann from the Phillip Island Nature Parks)

[4]Estimation of Markov transition probabilities for ecological communities
using dynamic site occupancy models
Keiichi Fukaya(The Institute of Statistical Mathematics)
Dynamics of ecological communities, especially of sessile organisms, haveoften been described by Markov models where changes in relative abundanceof species are summarized by transition probability matrices. Transition
probabilities can be estimated from time series data of species occupancy
states collected at fixed sampling points, although a naive estimation oftransition probabilities may not be robust when resampling error exists.
Here I present a model based approach for the estimation of transition
probabilities of the communities in which transition probabilities areestimated in the framework of multistate dynamic site occupancy model. The
model explicitly describes both transitions among occupancy states andobservation processes and hence takes the effect of resampling error in theestimation of transition probabilities into account. I also show that theresampling error rate can be estimated without any additional data otherthan the annual (or seasonal) data of occupancy states, suggesting theusefulness of this framework to estimate transition probabilities withlimited field data.

[5]Investigating species interactions in a fish community
Hideyasu Shimadzu, Maria Dornelas, Peter A. Henderson, Anne E.
Magurran(School of Biology, University of St Andrews)
Understanding the interactions between species within an ecologicalcommunity is a key challenge in biodiversity research. Here, we focus onmonthly time series records of an exceptionally well-documented estuarine
fish assemblage in the Bristol Channel. Given the multi-species time series
data, we have developed a model for a multivariate feedback system in which
the outputs can be the inputs and vice versa. The model assumes linear
interactions between species as a tractable approximation. To examine theextent to which the abundance of a given species is driven by the otherspecies, we have analysed the model in the spectrum domain and calculated
the contribution ratio of each species at each frequency.
Theresultsuggests that our modelling approach dealing with an ecological community
as a multivariate feedback system provides new insights into species
interactions. We demonstrate how it enables further analysis into
ecologically relevant groups of species that underpin the functioning of thesystem as a whole.

[6]A new probability model for cylindrical data
Kunio Shimizu(Department of Mathematics, Keio University)
Environmental data such as wind speed and direction, ozone concentration
and wind direction, and earthquakes magnitudes and successive turning
angles of the epicenter are considered data on the cylinder, i.e. the dada
consist of a combination of linear and circular observations. In the literature,probability distributions are introduced by Mardia and Sutton (1978, JRSS),Johnson and Wehrly (1978, JASA) and Kato and Shimizu (2008, JSPI) as
models for cylindrical data. In this talk, we give a new probability
distribution which is derived as a conditional distribution of a mixture oftrivariate normal distributions. The distribution should be called thePearson Type VII distribution on the cylinder, and includes theMardia-Sutton and Kato-Shimizu cylindrical distributions as special cases.
We study some distributional properties such as marginal and conditional
distributions, modality, circular-linear correlation, regression, and so on.
(This is joint work with Shonosuke Sugasawa, The University of Tokyo, andShogo Kato, The Institute of Statistical Mathematics.)

[7]Influence diagnostics in regression and time series models of circulardata.
Shuangzhe Liu(Department of Mathematics and Statistics, University of Canberra)
Distributional studies, regression and time series models have played
important roles in statistical analysis of circular data. In this paper, weconsider a likelihood approach to identify possible influential observations incircular data for these models. We use the maximum likelihood estimationand influence diagnostics methods. The observed information matrices andnormal curvatures are derived. Simulated and real data examples are thenprovided to illustrate our approach and results to be useful.