ISM Symposium on
Environmental Statistics
2015

Date & Time: Feb. 24(Tue.), 2015
Venue: The Institute of Statistical Mathematics,
10-3 Midori-cho Tachikawa, Tokyo 190-8562, Japan

Aim of Symposium
In order to enhance the understanding of the global environment, statisticalscience is extremely important. Centered around the topic of directional
statistics, we are holding a symposium in order to better develop research on
statistical theory which can be applied to solve specific issues in the fields ofenvironmental and ecological data.

Host Organization:
The Institute of Statistical Mathematics

Joint Hosting:The Graduate University for Advanced Studies 【SOKENDAI】
NPO Japan Institute of Environmental Statistics

Organizing Committee:
Koji Kanefuji (ISM)
Alan Welsh (ANU)
Kenichiro Shimatani (ISM)
Atsushi Yoshimoto (ISM)
Kunio Shimizu (ISM)Program

9:50-10:00 Opening Address: Yoshinori Kawasaki(ISM)[Section 1]Chair:Shuhei Mano(ISM)
10:00-10:45 Spatio-temporal point patterns, Periodicities and earthquakes
Pierre Dutilleul (McGill University)
10:45-11:15 Modeling of the typhoon trajectory pattern and its variations onannual and longer time scales
Shinya Nakano (The Institute of Statistical Mathematics)
11:15-12:00 Statistical modelling of wet and dry spell frequencies overLangat river basin, Malaysia
Chua Kuan Chin (Universiti Tunku Abdul Rahman)
Lunch[Section 2]Chair:Shinto Eguchi(ISM)
13:00-13:45 Exploring co-occurrence of closely-related guild members in a
fragmented landscape subject to rapid transformation
David B. Lindenmayer, Alan Welsh, Wade Blanchard, Philip
Tennant and Christine Donnelly (The Australian NationalUniversity)
13:45-14:15 Ecological statistics beginning with intensive field observations
Kenichiro Shimatani (The Institute of Statistical Mathematics)
14:15-15:00 Study of rain induced landslides prediction and casualty
prevention in Malaysia
Swee Peng Koay, Habibah Lateh and Pei Shan Fam(Universiti Sains Malaysia)
Coffee Break[Section 3]Chair:Satoshi Kuriki(ISM)
15:15-16:00 Circular regression and animal movement
Louis-Paul Rivest (Université Laval)
16:00-16:30 Atractable, parsimonious and highly flexible model forcylindrical data
Toshihiro Abe (Tokyo University of Science)
16:30-17:00 Deforestation modeling based on statistical and machinelearning approaches
Ryuei Nishii (Institute of Mathematics for Industry,Kyushu University)
17:00-17:10 Closing Address:Kunio Shimizu(ISM)[1] Spatio-temporal point patterns, Periodicities and earthquakes
Pierre Dutilleul(McGill University，Canada)
Earthquakes represent, at the same time, a life-threatening phenomenon
and a fascinating subject of investigation for many scientists, including
seismologists and statisticians. While bearing in mind the seismologicalperspective, the statistical approach will prevail in this talk, and with moreor less adjustment, the methodological aspects that will be discussed could
be applied to other environmental phenomena such as volcanic eruptions
and extreme climatic events (e.g. droughts, flooding).
The location of an earthquake in space and time, through the latitude,
longitude and depth of its hypocenter in 3D and the date and clock time ofrupture, respectively, makes it a “point” in a spatio-temporal point pattern,observed over a given territory and decades/years/months; the association ofa magnitude with each earthquake will mark the point pattern. The frontier
between point pattern analysis and time series analysis is not hermetic(Brillinger, 1994). Therefore, I shall move from one framework to the otherand vice versa in my talk, (i) searching for periodicities in earthquake
occurrence and (ii) attempting to understand the effects of earthquake
catalog declustering algorithms based on ETAS models (Ogata, 1998;Zhuang et al., 2002, 2011) on the characteristics of the resulting time seriesof earthquake numbers (e.g. on a monthly basis); declustering is aimed at
separating dependent earthquakes (aftershocks, foreshocks) fromindependent background event occurrences.
Formally, I shall show how the declustering of an earthquake spatialpoint pattern affects the mean and autocovariance functions of the timeseries of earthquake numbers. Empirically, I shall use central California
earthquake datasets to illustrate differences in results of periodicity analysis
with complete vs. declustered catalogs depending on the magnitude range.
Two methods of periodicity analysis will be compared: the “Schusterspectrum”, which belongs more to the point-pattern approach (Schuster,1897; Ader and Avouac, 2013), and the “multi-frequential periodogram
analysis” (MFPA; Dutilleul, 2001), for which this is a first documented
application to seismological time series.
The periodicity analysis of central California earthquakes is joint workwith Roland Bürgmann and Christopher Johnson from University ofCalifornia in Berkeley.
References
Ader, T. J., and J. P. Avouac (2013), Detecting periodicities and declusteringin earthquake catalogs using the Schuster spectrum, application toHimalayan seismicity, Earth and Planetary Science Letters, 383,
26-36.
Brillinger, D. R. (1994), Time series, point processes, and hybrids, Canadian
Journal of Statistics, 22, 177-206.
Dutilleul, P. (2001), Multi-frequential periodogram analysis and thedetection of periodic components in time series, Communications in
Statistics - Theory and Methods, 30, 1063-1098.
Ogata, Y. (1998), Space-time point-process models for earthquake
occurrences, Annals of the Institute of Statistical Mathematics, 50,379-402.
Schuster, A. (1897), On lunar and solar periodicities of earthquakes,

Proceedings of the Royal Society of London, 61, 455-465.
Zhuang, J., Y. Ogata, and D. Vere-Jones (2002), Stochastic declustering ofspace-time earthquake occurrences, Journal of the American
Statistical Association, 97, 369-382.
Zhuang, J., M. J. Werner, S. Hainzl, D. Harte, and S. Zhou (2011), Basicmodels of seismicity: Spatiotemporal models, Community Online
Resourcefor
StatisticalSeismicity
Analysis,doi:10.5078/corssa-07487583. Available at http://www.corssa.org.

[2] Modeling of the typhoon trajectory pattern and its variations on annual
and longer time scales
Shin'ya Nakano(The Institute of Statistical Mathematics)
A typhoon is an intense tropical cyclone forming in the western northPacific region. Since a typhoon can cause a serious disaster in East Asia, it iscrucial to evaluate risks of typhoon hazards. In principle, It would bepossible to evaluate such risks by compiling a large number of the simulation
results for various possible scenarios. However, numerical fluid dynamics
simulations that could satisfactorily reproduce a realistic typhoon trajectorywould require prohibitive computational cost for each run. In order to enable
us to assess the risks due to typhoons with low computational cost, we aim atdeveloping a new probabilistic model which can generate a variety ofsurrogate typhoon trajectories with high reality. Since the trajectory patternof typhoons has variations on annual and longer time scales, the model
should be designed so as to consider these long-term variations to allow
detailed assessment of the risks due to typhoons. In order to reproduce suchfeatures of the typhoon trajectories, we employ a Gaussian process
regression and obtain the typical typhoon velocity as a function of latitude,longitude, day of year, and year. The characteristics of the long term changeof the typical typhoon velocity and their implications are discussed on thebasis of the results.

[3] Statistical modelling of wet and dry spell frequencies over Langat riverbasin, Malaysia
Chua Kuan Chin(Universiti Tunku Abdul Rahman，Malaysia)
Recent water crisis that occurred in Selangor state of Malaysia has raised
the importance of understanding the rainfall characteristic. By having ableto model a rainfall processes, runoff of water and natural disaster due toheavy rainfall are able to be supervised. A frequency analysis is the mostcommonly applied method in hydrology data, while Normal, Log-normal,Gamma, Gumbel and Weibull distribution are among the important
probability distributions commonly used in hydrology analysis. In this study,
the analysis of distributions of wet and dry spells based on daily rainfall isinvestigated and it is fitted to the daily rainfall data for the period 1995-2005.
The candidate distributions are Hurwitz-Lerch Zeta distribution,
Eggemberger-Polya distribution, Logarithmic distribution, TruncatedPoisson distribution and Geometric distribution. The parameters ofdistributions are estimated by Maximum Likelihood Estimation using
simulated annealing optimization method. The distributions are then plottedand compared to the histogram of dry and wet spell of the daily rainfall.
Model selection technique such as AIC is employed to study the fittings ofthose distributions.

[4] Exploring co-occurrence of closely-related guild members in a fragmented
landscape subject to rapid transformation
David B. Lindenmayer, Alan Welsh, Wade Blanchard, Philip Tennant,Christine Donnelly (The Australian National University, Australia)
We use data from a 15-year experimental study, to explore intra-guild
co-occurrence of six closely-related and functionally-similar sets of birds
within 55 woodland fragments. Areas surrounding these remnants areundergoing transformation from grazed paddocks to Pinus radiata
plantations, leading to a novel assemblage of forest and woodland birds. We
therefore sought to determine if the occurrence of a given species in a guild
influenced the occurrence of other closely-related species in that guild, andthrough this relationship whether there was evidence of co-occurrence
between species.
After controlling for environmental variables which can affect species
occurrence like time since commencement of landscape transformation,
patch size and vegetation type, we found the occurrence of a given species
was influenced by the occurrence of a closely-related species in the sameguild. Kinds of resulting co-occurrence varied among bird guilds andincluded: (1) positive co-occurrence in which occurrence of one species withinwoodland patches positive affected the occurrence of another closely-related
species in the same guild (e.g. Eastern and Crimson Rosellas); and (2)negative co-occurrence in which the occurrence of one species was negatively
associated with the occurrence of another within the same guild (e.g. Willie
Wagtail and Grey Fantail).
We also identified interactions between patch size and abundance within
members of two bird guilds. For example, results of our modelling for mean
conditional abundance showed that the Eastern Rosella increased withincreasing numbers of the Crimson Rosella in large patches, but decreased
with increasing numbers of the Crimson Rosella in small patches.
Our results provide empirical evidence of co-occurrence among closely
related and functionally similar species in a rapidly transforming landscape.
Our findings underscore the complexity of biotic responses to landscapetransformation

[5] Ecological statistics beginning with intensive field observations
Ichiro ken Shimatani（ISM）
In this presentation, I am talking examples of ecological studies thatheavily relied on intensive field observations and statistical modeling. In one
case about long-term forest dynamics, I was one of the intensive fieldworkers, and the study also utilized old historical records. The statisticalanalyses were elementary applications of Bayesian modeling. In one caseabout long-term population dynamics of large ocean mammals (seal), thestudy began with field surveys by volunteer students and two miracle (!) fieldzoologists 40 years ago. The up-to-date Bayesian modeling (integratedpopulation model, IPM) was applied to 40-years census data, and detailrecords during a few months uninhabited island stays by the miracle
zoologists were also used in the Bayesian formulation. We discuss long-term
trends of the population and applications to marine ecosystem management.

[6] Study of rain induced landslides prediction and casualty prevention inMalaysia
Swee Peng Koay(Universiti Sains Malaysia), Habibah Lateh(UniversitiSains Malaysia), Pei Shan Fam, (Universiti Sains Malaysia)
In Malaysia, landslides occur more often than before due to climate change.
Unlike Japan, most of the landslides are rain induced slope failures. The
Malaysian Government has to allocate millions of Malaysian Ringgit forslope monitoring in the budget every year.
However, there are thousands of slopes which are classified as high risk
slopes in Malaysia. Installing site monitoring devices, such as extensometer,piezometer and inclinometer in these slopes to monitor the movement of thesoil in the slopes, are too costly and almost impossible.
In our study, we propose Accumulated Rainfall vs. Rainfall Intensity
prediction method to predict the slope failure by referring to the predicted
rain data from radar and the rain volume from rain gauges. The critical line
which determines if the slope is in danger, is generated by simulator withwell-surveyed the soil property in the slope and compared with historical
data. By establishing such predicting system, the slope failure warning
information can be obtained and disseminated to the surroundings via ITsystems and siren.
Besides establishing the early warning dissemination system, educating
school children and the community by giving knowledge on landslides, suchas landslide's definition, how and why does the slope failure happen andwhen will it fail, to raise the risk awareness on landslides will reduce
landslides casualty, especially in rural area.

[7] Circular regression and animal movement
Louis-Paul Rivest (Université Laval，Canada)
In ecology, data on animal’s movement is easily collected using GPS
tracking devices. This information is merged with data on an animal’senvironment, available in satellite photos for instance, using a GIS(Geographic Information System). To investigate how an animal’senvironment influences its motion, one constructs predictive models for yt,
the angle of an animal’s movement at time t. The explanatory variables are
yt-1, the direction of displacement at the previous time step, the directions attime t, xt, of targets of interest, such as a lair or a patch rich in nutriment,
and the distances zt to these targets. In ecology, these models are known asbiased correlated random walks, see Benhamou (2014). As the dependent
variable is circular, standard regression models do not apply andmultivariate generalizations of the circular-circular regression model ofRivest (1997) and of the circular-linear model of Presnell et al. (1998) areintroduced. Likelihood inference methods for these models are developed.
Generalizations of the models for the movement angle yt that include hidden
states to account for an animal changes in behavior (Langrock et al., 2012)
are also introduced. The statistical challenges encountered when fitting
them to movement data will be discussed. This is joint work with A. Nicosia,T. Duchesne and D. Fortin of Université Laval.
References
Benhamou, S. (2014) Of scales and stationarity in animal movements.
Ecology Letters, 17, 261–272.
Langrock, R., King, R., Matthiopulous, J., Thomas, L., Fortin, D., andMorales, J. (2012). Flexible and practical modeling of animal telemetry
data: hidden Markov models and extensions. Ecology, 93, 2336-2342
Presnell, B., Morrison, S.P. & Littell, R.C. (1998) Projected multivariate
linear models for directional data. Journal of the American StatisticalAssociation, 93, 1068–1077.
Rivest, L.P. (1997) A decentred predictor for circular-circular regression.
Biometrika, 84, 717–726.

[8] A tractable, parsimonious and highly flexible model for cylindrical data
Toshihiro Abe(Tokyo University of Science)
Cylindrical data are observations that consist of a directional part (a set ofangles), which is often of a circular nature (a single angle), and a linear part(mostly a positive real number). This explains the alternative terminology ofdirectional-linear or circular-linear data. Such data occur frequently innatural sciences; typical examples are wind direction and anotherclimatological variable such as wind speed or air temperature, the direction
an animal moves and the distance moved, or wave direction and wave height.
In this talk, we consider cylindrical distributions obtained by combining thesine-skewed von Mises distribution (circular part) with the Weibull
distribution (linear part). This new model, the Weibull-sine-skewed vonMises (WeiSSVM) distribution, enjoys numerous advantages: simple
normalizing
constantand
hence
very
tractable
density,parameter-parsimony
andinterpretability,
maximum
entropycharacterization, good circular-linear dependence structure, easy random
number generation thanks to known marginal/conditional distributions, andflexibility illustrated via excellent fitting abilities. We also introduce othernew circular-linear models, based on the same idea by making use of theGamma and Generalized Gamma distributions. Our flexible models areapplied in analyses of two cylindrical data sets. We conclude the talk by
showing a straightforward extension to the directional-linear cylindrical
distributions.

[9] Deforestation modeling based on statistical and machine learning
approaches
Ryuei Nishii (Institute of Mathematics for Industry, Kyushu University,Japan)
Deforestation is caused by various factors. In the literature, the impact ofhuman activities as well as geographic circumstances on forests has been
extensively discussed. We have studied statistical models for prediction offorest area ratio by covariates: human population density and relief energy
observed in a grid-cell system. Parametric non-linear regression functions of
the covariates were used for predicting forest coverage ratio and cubic spline
functions were also used for detection of small fluctuation of regressionfunctions. Furthermore, zero-one inflated distributions were proposed forclassification of each site into one of three categories: completely-deforested,fully-forest-covered or partly-deforested areas. These methods took thespatial dependency into the modeling, which is not an easy task.
Our aim here is to substitute the previous statistical approach for machinelearning approach based on SVM (support vector machine) and SVR (supportvector regression). SVM will be used for classification of each site into one of
the above-mentioned categories, and SVR for prediction of the forest
coverage ratio. The proposed approach implements a neighbors' effect into
the modeling easily. By our numerical study, it will be shown that theperformance of the machine learning methods is comparable or superior tothat of the statistical methods.