Dr Ian Renner

Senior Lecturer

School of Mathematical and Physical Sciences

Career Summary

Biography

I earned my PhD in 2013 from the University of New South Wales, and continued in a post-doctorate position there before assuming a position as lecturer with the University of Newcastle. My research interests currently focus on species distribution models for presence-only data, a fast-advancing field. One contribution I have made has been to unify the literature by establishing the equivalence of two methods: point process models and MAXENT. Leveraging off of this equivalence, I have proposed an approach called PPM-LASSO, which inherits the clarity of interpretation and implementation, the flexibility, and the ability to critically evaluate model fits from point process models, and extends the benefits of predictive performance gained from affixing LASSO penalties, which have contributed to MAXENT's popularity. I maintain the R package ppmlasso to fit such models.

Qualifications

  • PhD (Statistics), University of New South Wales
  • Bachelor of Science (Mathematics), Valparaiso University - Valparaiso
  • Master of Statistics, University of Utah - USA

Keywords

  • ecological statistics
  • species distribution models

Fields of Research

Code Description Percentage
010405 Statistical Theory 30
010401 Applied Statistics 40
010499 Statistics not elsewhere classified 30

Professional Experience

UON Appointment

Title Organisation / Department
Senior Lecturer University of Newcastle
School of Mathematical and Physical Sciences
Australia

Professional appointment

Dates Title Organisation / Department
26/03/2013 - 31/01/2014 Research Assistant The University of New South Wales
School of Mathematical Sciences
Australia
1/08/2007 - 31/12/2008 Associate Lecturer The University of New South Wales
School of Mathematical Sciences
Australia

Awards

Prize

Year Award
2011 JB Douglas Award
Statistical Society of Australia Inc.
2011 Runner-up for best student talk
Statistical Society of Australia Inc.
2010 EJG Pitman Prize
Statistical Society of Australia Inc.

Teaching

Code Course Role Duration
STAT1070 Statistics for the Sciences
Faculty of Science and Information Technology, The University of Newcastle | Australia
Course Coordinator 23/02/2015 - 5/06/2015
STAT1070 Statistics for the Sciences
Faculty of Science and Information Technology, The University of Newcastle | Australia
Lecturer 3/03/2014 - 13/06/2014
STAT1070 Statistics for the Sciences
Faculty of Science and Information Technology, The University of Newcastle | Australia
Course Coordinator 28/07/2014 - 7/11/2014
STAT1070 Statistics for the Sciences
Faculty of Science and Information Technology, The University of Newcastle | Australia
Lecturer 27/07/2015 - 6/11/2015
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Journal article (10 outputs)

Year Citation Altmetrics Link
2019 Renner IW, Louvrier J, Gimenez O, 'Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalized likelihood maximization', Methods in Ecology and Evolution, (2019)

© 2019 The Authors. Methods in Ecology and Evolution © 2019 British Ecological Society The increase in availability of species datasets means that approaches to species distributi... [more]

© 2019 The Authors. Methods in Ecology and Evolution © 2019 British Ecological Society The increase in availability of species datasets means that approaches to species distribution modelling that incorporate multiple datasets are in greater demand. Recent methodological developments in this area have led to combined likelihood approaches, in which a log-likelihood comprised of the sum of the log-likelihood components of each data source is maximized. Often, these approaches make use of at least one presence-only dataset and use the log-likelihood of an inhomogeneous Poisson point process model in the combined likelihood construction. While these advancements have been shown to improve predictive performance, they do not currently address challenges in presence-only modelling such as checking and correcting for violations of the independence assumption of a Poisson point process model or more general challenges in species distribution modelling such as overfitting. In this paper, we present an extension of the combined likelihood framework which accommodates alternative presence-only likelihoods in the presence of spatial dependence as well as lasso-type penalties to account for potential overfitting. We compare the proposed combined penalized likelihood approach to the standard combined likelihood approach via simulation and apply the method to modelling the distribution of the Eurasian lynx in the Jura Mountains in eastern France. The simulations show that the proposed combined penalized likelihood approach has better predictive performance than the standard approach when spatial dependence is present in the data. The lynx analysis shows that the predicted maps vary significantly between the model fitted with the proposed combined penalized approach accounting for spatial dependence and the model fitted with the standard combined likelihood. This work highlights the benefits of careful consideration of the presence-only components of the combined likelihood formulation, and allows greater flexibility and ability to accommodate real datasets.

DOI 10.1111/2041-210X.13297
2019 Norberg A, Abrego N, Blanchet FG, Adler FR, Anderson BJ, Anttila J, et al., 'A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels', ECOLOGICAL MONOGRAPHS, 89 (2019) [C1]
DOI 10.1002/ecm.1370
Citations Scopus - 6Web of Science - 5
2019 De Solan T, Renner I, Cheylan M, Geniez P, Barnagaud J-Y, 'Opportunistic records reveal Mediterranean reptiles' scale-dependent responses to anthropogenic land use', ECOGRAPHY, 42 608-620 (2019) [C1]
DOI 10.1111/ecog.04122
2019 Bonnet-Lebrun AS, Karamanlidis AA, de Gabriel Hernando M, Renner I, Gimenez O, 'Identifying priority conservation areas for a recovering brown bear population in Greece using citizen science data', Animal Conservation, (2019)

© 2019 The Zoological Society of London Understanding the processes related to wildlife recoveries is not only essential in solving human ¿ wildlife conflicts, but also for identi... [more]

© 2019 The Zoological Society of London Understanding the processes related to wildlife recoveries is not only essential in solving human ¿ wildlife conflicts, but also for identifying priority conservation areas and in turn, for effective conservation planning. We used data from a citizen science program to study spatial aspects of the demographic and genetic recovery of brown bears in Greece and to identify new areas for their conservation. We visually compared our data with an estimation of the past distribution of brown bears in Greece and used a point process approach to model habitat suitability. We then compared our results with the current distribution of brown bear records and with that of protected areas. Our results indicate that in the last 15¿years bears may have increased their range by as much as 100%, by occupying mainly anthropogenic landscapes and areas with suitable habitat that are currently not legally protected, thus creating a new conservation reality for the species in Greece. This development dictates the re-evaluation of the national management and conservation priorities for brown bears in Greece by focusing in establishing new protected areas that will safeguard their recovery. Our conservation approach is a swift and cheap way of identifying priority conservation areas, while gaining important insights on spatial aspects of population recovery. It will help prioritize conservation actions for brown bears in Greece and may serve as a model conservation approach to countries facing similar financial and logistic constraints in the monitoring of local biodiversity or facing challenges in managing rapid population recoveries. Our conservation approach appeared to be particularly suited to identifying priority areas for conservation in areas with recovering wildlife populations and may therefore be used as an ¿early-warning¿ conservation system.

DOI 10.1111/acv.12522
2018 Khan AA, Davies AJ, Whitehead NJ, McGee M, Al-Omary MS, Baker D, et al., 'Targeting elevated left ventricular end-diastolic pressure following primary percutaneous coronary intervention for ST-segment elevation myocardial infarction - a phase one safety and feasibility study.', European heart journal. Acute cardiovascular care, 2048872618819657 (2018)
DOI 10.1177/2048872618819657
Co-authors Andrew Boyle, John Attia, Tazeen Majeed
2017 Khan AA, Al-Omary M, Renner I, Haque EU, Ekmejian A, Hussain M, et al., 'Echocardiographic assessment of pulmonary artery systolic pressure following treadmill stress testing', EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, 18 1278-1282 (2017) [C1]
DOI 10.1093/ehjci/jew308
Citations Scopus - 2Web of Science - 2
2017 Khan AA, Ashraf A, Singh R, Rahim A, Rostom W, Hussain M, et al., 'Incidence, time of occurrence and response to heart failure therapy in patients with anthracycline cardiotoxicity', INTERNAL MEDICINE JOURNAL, 47 104-109 (2017) [C1]
DOI 10.1111/imj.13305
Citations Scopus - 5Web of Science - 4
2015 Renner IW, Elith J, Baddeley A, Fithian W, Hastie T, Phillips SJ, et al., 'Point process models for presence-only analysis', Methods in Ecology and Evolution, 6 366-379 (2015) [C1]

© 2015 British Ecological Society. Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has consider... [more]

© 2015 British Ecological Society. Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence-only data. Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudo-absences or 'background points') objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; and ways forward regarding difficult issues such as accounting for sampling bias. Point process models are related to some common approaches to presence-only species distribution modelling, which means that a variety of different software tools can be used to fit these models, including maxent or generalised linear modelling software.

DOI 10.1111/2041-210X.12352
Citations Scopus - 97Web of Science - 92
2013 Renner IW, Warton DI, 'Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology', Biometrics, 69 274-281 (2013) [C1]

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 maxi... [more]

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. © 2013, The International Biometric Society.

DOI 10.1111/j.1541-0420.2012.01824.x
Citations Scopus - 179Web of Science - 171
2013 Warton DI, Renner IW, Ramp D, 'Model-based control of observer bias for the analysis of presence-only data in ecology', PLoS ONE, 8 (2013) [C1]

Presence-only data, where information is available concerning species presence but not species absence, are subject to bias due to observers being more likely to visit and record ... [more]

Presence-only data, where information is available concerning species presence but not species absence, are subject to bias due to observers being more likely to visit and record sightings at some locations than others (hereafter "observer bias"). In this paper, we describe and evaluate a model-based approach to accounting for observer bias directly - by modelling presence locations as a function of known observer bias variables (such as accessibility variables) in addition to environmental variables, then conditioning on a common level of bias to make predictions of species occurrence free of such observer bias. We implement this idea using point process models with a LASSO penalty, a new presence-only method related to maximum entropy modelling, that implicitly addresses the "pseudo-absence problem" of where to locate pseudo-absences (and how many). The proposed method of bias-correction is evaluated using systematically collected presence/absence data for 62 plant species endemic to the Blue Mountains near Sydney, Australia. It is shown that modelling and controlling for observer bias significantly improves the accuracy of predictions made using presence-only data, and usually improves predictions as compared to pseudo-absence or "inventory" methods of bias correction based on absences from non-target species. Future research will consider the potential for improving the proposed bias-correction approach by estimating the observer bias simultaneously across multiple species. © 2013 Warton et al.

DOI 10.1371/journal.pone.0079168
Citations Scopus - 49Web of Science - 44
Show 7 more journal articles

Software / Code (1 outputs)

Year Citation Altmetrics Link
2015 Renner IW, 'ppmlasso (R package)', 1.1, CRAN (2015) [G1]
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Grants and Funding

Summary

Number of grants 3
Total funding $12,838

Click on a grant title below to expand the full details for that specific grant.


20152 grants / $5,438

Early-Mid Career Researcher Visiting Fellowship Award$3,438

Funded for research visit to work with Olivier Gimenez at the Centre National de la Recherche Scientifique (CNRS) in Montpellier, France.

Funding body: Faculty of Science and Information Technology, The University of Newcastle | Australia

Funding body Faculty of Science and Information Technology, The University of Newcastle | Australia
Scheme Early-Mid Career Researcher Visiting Fellowship Scheme
Role Lead
Funding Start 2015
Funding Finish 2015
GNo
Type Of Funding Internal
Category INTE
UON N

PVC Conference Assistance Grant Scheme$2,000

Funded trip to Hobart, Australia for the International Biometric Society (Australasian Region) conference "Biometrics by the Harbour".

Funding body: Faculty of Science and Information Technology, The University of Newcastle | Australia

Funding body Faculty of Science and Information Technology, The University of Newcastle | Australia
Scheme PVC Conference Assistance Grant
Role Lead
Funding Start 2015
Funding Finish 2015
GNo
Type Of Funding Internal
Category INTE
UON N

20141 grants / $7,400

New Staff Grant$7,400

Funding for new staff members. 

Funding body: Faculty of Science and Information Technology, The University of Newcastle | Australia

Funding body Faculty of Science and Information Technology, The University of Newcastle | Australia
Scheme New Staff Grant
Role Lead
Funding Start 2014
Funding Finish 2014
GNo
Type Of Funding Internal
Category INTE
UON N
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Research Supervision

Number of supervisions

Completed0
Current1

Current Supervision

Commenced Level of Study Research Title Program Supervisor Type
2017 PhD Statistical Modelling of Species and Community Distributions: from Bridging the Gap between Statistics and Ecology to Conservation Stakeholders Challenges PhD (Statistics), Faculty of Science, The University of Newcastle Principal Supervisor
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Dr Ian Renner

Position

Senior Lecturer
School of Mathematical and Physical Sciences
Faculty of Science

Contact Details

Email ian.renner@newcastle.edu.au
Phone (02) 4921 5547

Office

Room V238
Building Mathematics Building
Location Callaghan
University Drive
Callaghan, NSW 2308
Australia
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