sydney harbour: innovative environmental data science in australia's most iconic waterway

INNOVATIVE ENVIRONMENTAL DATA SCIENCE IN AUSTRALIAS MOST ICONIC WATERWAY

LUKE HEDGE

THE UNIVERSITY OF NEW SOUTH WALES

DRLUKEHEDGE

SYDNEY HARBOUR

50 % | WORLDS COASTLINE ALTEREDShutterstock | pokki1

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A SYSTEMATIC SCIENTIFIC REVIEW

>20, 000 journal articles searched310 publications found

four universitiestwo government agenciesone national museum

15 scientist authors

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APPLIED RESEARCH200 Publications

110 Publications

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92 Publications 91 Publications 7 Publications 28 Publications

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APPLIED RESEARCH BASIC RESEARCH


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ECOLOGY

CHEMISTRY

BIOLOGY

MANAGEMENT

OCEANOGRAPHY

GEOLOGY

FISHERIES


WHERE ARE THE HABITATS ?WHERE ARE THE IMPACTS ? WHERE DO THEY OVERLAP ?

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Species distribution models

A model that relates environmental predictors to known species locations across a landscapeElith et al (2009) Annu. Rev. Ecol. Evol. Syst. 2009.

To provide understanding or prediction

2 D. I. WARTON AND L. C. SHEPHERD

Fig. 1. (a) Example presence-only dataatlas records of where the tree species An-gophora costata has been reported to be present, west of Sydney, Australia. The studyregion is shaded. (b) A map of minimum temperature (C) over the study region. Vari-ables such as this are used to model how intensity of A. costata presence relates to theenvironment. (c) A species distribution model, modeling the association between A. costataand a suite of environmental variables. This is the fitted intensity function for A. costatarecords per km2, modeled as a quadratic function of four environmental variables using apoint process model as in Section 4.

example is given in Figure 1(a). This figure gives all locations where a par-ticular tree species (Angophora costata) has been reported by park rangerssince 1972, within 100 km of the Greater Blue Mountains World HeritageArea, near Sydney, Australia. Note that this does not consist of all loca-tions where an Angophora costata tree is foundrather it is the locationswhere the species has been reported to be found. We would like to use thesepresence points, together with maps of explanatory variables describing theenvironment (often referred to in ecology as environmental variables), topredict the location of A. costata and how it varies as a function of explana-tory variables (Figure 1).

Presence-only data are used extensively in ecology to model species distribu-tionswhile the term presence-only data was rarely used before the 1990s,ISI Web of Science reports that it was used in 343 publications from 2005to 2008. The use of presence-only data in modeling is a relatively recentdevelopment, presumably aided by the movement toward electronic recordkeeping and recent advances in Geographic Information Systems. One rea-son for the current widespread usage of presence-only data is that often thisis the best available information concerning the distribution of a species, asthere is often little or no information on species distribution being availablefrom systematic surveys [Elith and Leathwick (2007)].

Species distribution models, sometimes referred to as habitat models orhabitat classification models [Zarnetske, Edwards and Moisen (2007)], are

perform well in characterizing the natural distributions of species (within their current range)

Occurrence points Enviro predictor Model prediction

Warton and Sheppard (2010) Annals App. Stat.

Elith et al (2009) Annu. Rev. Ecol. Evol. Syst. 2009.

Warton and Aarts (2013) J. Anim. Ecol.

useful ecological insight and strong predictive capability

Ecological Applications, 24(1), 2014, pp. 7183 2014 by the Ecological Society of America

Prediction of fishing effort distributionsusing boosted regression trees

CANDAN U. SOYKAN,1,2,3 TOMOHARU EGUCHI,1 SUZANNE KOHIN,2 AND HEIDI DEWAR2

1Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service,National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, California 92037 USA

2Fisheries Resources Division, Southwest Fisheries Science Center, National Marine Fisheries Service,National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, California 92037 USA

Abstract. Concerns about bycatch of protected species have become a dominant factorshaping fisheries management. However, efforts to mitigate bycatch are often hindered by alack of data on the distributions of fishing effort and protected species. One approach toovercoming this problem has been to overlay the distribution of past fishing effort with knownlocations of protected species, often obtained through satellite telemetry and occurrence data,to identify potential bycatch hotspots. This approach, however, generates static bycatch riskmaps, calling into question their ability to forecast into the future, particularly when dealingwith spatiotemporally dynamic fisheries and highly migratory bycatch species. In this study,we use boosted regression trees to model the spatiotemporal distribution of fishing effort fortwo distinct fisheries in the North Pacific Ocean, the albacore (Thunnus alalunga) troll fisheryand the California drift gillnet fishery that targets swordfish (Xiphias gladius). Our resultssuggest that it is possible to accurately predict fishing effort using ,10 readily availablepredictor variables (cross-validated correlations between model predictions and observed data;0.6). Although the two fisheries are quite different in their gears and fishing areas, theirrespective models had high predictive ability, even when input data sets were restricted to afraction of the full time series. The implications for conservation and management areencouraging: Across a range of target species, fishing methods, and spatial scales, even arelatively short time series of fisheries data may suffice to accurately predict the location offishing effort into the future. In combination with species distribution modeling of bycatchspecies, this approach holds promise as a mitigation tool when observer data are limited. Evenin data-rich regions, modeling fishing effort and bycatch may provide more accurate estimatesof bycatch risk than partial observer coverage for fisheries and bycatch species that are heavilyinfluenced by dynamic oceanographic conditions.

Key words: albacore; bycatch mitigation; dynamic oceanographic conditions; fisheries management;marine spatial planning; species distribution modeling; swordfish.

INTRODUCTION

Fisheries bycatch, the unintentional capture of non-

target species during fishing operations, threatens the

survival of a number of vulnerable marine species

(Lewison et al. 2004, Zydelis et al. 2009) and has

become a key factor in shaping management decisions.

Although bycatch research has increased dramatically

over the past few decades, the limited data available for

most fisheries still hinders management and conserva-

tion efforts (Soykan et al. 2008). Specifically, high

quality data on fisheries bycatch collected by trained

observers are unavailable or limited for most interna-

tional and many U.S. fisheries. The prospects for future

expansion of data collection efforts are likewise modest

or negligible given the costs and logistics associated with

such efforts. Although such obstacles impede direct

assessment of bycatch rates, obtaining estimates of

bycatch rates and predictions of potential interactions is

critical for efforts to reduce bycatch and determine the

impact of fisheries on many marine protected species.

Researchers have thus begun to explore indirect

methods for estimating fisheries bycatch. One approach

involves port-based interviews to gather baseline data on

fishing effort and bycatch in artisanal fleets (Moore et al.

2010). This approach has the advantage of being rapid

and cost effective, but relies on honest, accurate

responses by the fishers. A second approach, which is

increasing in popularity, involves the estimation of

bycatch species distributions and their overlap with

fishing effort to assess threats, identify hotspots, and

guide decision-making (Cuthbert et al. 2005, Golds-

worthy and Page 2007, Hamel et al. 2008, McClellan et

al. 2009, Zhou et al. 2009).

The study by Cuthbert et al. (2005) provides an

illustrative example of the overlap approach. First, the

Manuscript received 21 May 2012; revised 9 April 2013;accepted 17 April 2013; final version received 8 May 2013.Corresponding Editor: S. S. Heppell.

3 Present address: National Audubon Society 220 Montgom-ery Street, Suite 1000, San Francisco, California 94104 USA.E-mail: [email protected]

71

data due to cloud cover or gaps in satellite coverage.

Missing data could skew a comparison of temporally

static vs. dynamic predictor variables. We assessed the

effects of missing data by examining the relationship

between VI scores and missing data. For each of the

satellite-derived oceanographic variables we correlated

its VI score (based on yearly BRT models built for this

analysis) with the percentage of non-zero fishing effort

records from that year that had a value for the variable.

We used non-zero fishing effort records because records

with zero fishing effort contributed less to model

development with these data sets, which were dominated

by zero fishing effort records (good BRT models can be

developed with presence-only information [Elith et al.

2008]).

RESULTS

The DGN fishing effort data set spanned 11.668 oflatitude and 78 of longitude, comprising 547 100 3 100

grid cells (Fig. 1A). Fishing effort in each of these cells

was recorded monthly for 20 years, resulting in a total of

131 280 records, of which 12 577 (;9.6%) had non-zerofishing effort. The AT fishing effort data set spanned 228of latitude and 448 of longitude, comprising 911 18 3 18grid cells (Fig. 1B). Fishing effort in each of these cells

was recorded monthly for 20 years, resulting in a total of

218 640 records, of which 12 346 (;5.6%) had non-zerofishing effort.

For the DGN fishery, SSHV was involved in four of

the 10 strongest interactions between predictor vari-

ables, while latitude, month, and year each were

involved in three (Appendix: Table A1). For the AT

fishery, DistCoast was involved in four of the 10

strongest interactions between predictor variables, while

depth, latitude, and SST each were involved in three

(Table A2). For the majority of pairs of the predictor

variables, collinearity among them was low for both

data sets. For the DGN fishery, four pairs had

correlation coefficients with absolute values .0.5, wherethe largest correlation was found between latitude and

longitude (0.93). For the AT fishery, seven pairs hadcorrelation coefficients with absolute values .0.5, wherethe largest correlations were found between EKE and

UGEO (0.78) and between latitude and SST (0.78;Appendix: Tables A3 and A4).

BRT model performance

Using stringent across-year cross-validation, the

boosted regression tree models effectively predicted

fishing effort for the DGN and AT fisheries (Table 2).

The DGN model explained 58.7% of the deviance in thedata, had a mean correlation between predicted and

observed data of 0.589, and had low false positive and

false negative error rates (11.4% and 1.7% respectively).The AT model explained 65.6% of the deviance in thedata, had a mean correlation between predicted and

observed data of 0.579, had a false positive error rate of

8.0%, and a false negative error rate of 2.1%.An examination of the relationships between envi-

ronmental variables and fishing effort showed a range of

patterns for the DGN model (Fig. 2). The model showed

a peak in effort early in the time series, followed by an

initial rapid decline that was then followed by a more

gradual decline in effort over the years. Seasonally,

FIG. 1. Maps of cumulative fishing effort: (A) West Coast drift gillnet (DGN; measured as number of gear sets) and (B) NorthPacific albacore troll (AT; measured as number of days fished) fisheries. Individual grid cells are 1003100 for the drift gillnet fisheryand 18318 for the albacore troll fishery. The drift gillnet fishery data cover the period 19812001, and the albacore troll fishery datacover the period 19912010. Grid cells with fewer than three total sets or days fished have been censored for confidentiality.

January 2014 75PREDICTING FISHING EFFORT DISTRIBUTIONS

City of SydneyRose Bay

Lane Cove River

Manly

Sydney Institute of Marine Science

Sydney Harbour573 surveys6 months12 000 events15 personnel

predictors

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4 D. I. WARTON AND L. C. SHEPHERD

converge to the point process slope estimates (Section 3). These two keyresults have important ramifications for species distribution modeling inecology (Section 5), in particular, we provide a solution to the problem ofhow to select pseudo-absences. We illustrate our results for the A. costatadata of Figure 1(a) (Section 4).

2. Poisson point process models for presence-only data. Presence-onlydata are a set y= {y1, . . . , yn} of point locations in a two-dimensional regionA, where the locations where presences are recorded (the yi) are out of thecontrol of the researcher, as is the total number of presence points n. We alsoobserve a map of values over the entire region A for each of k explanatoryvariables, and we denote the values of these variables at yi as (xi1, . . . , xik).

We propose analyzing y= {y1, . . . , yn} as a point process, hence, we jointlymodel number of presence points n and their location (yi). This has notpreviously been proposed for the analysis of presence-only data, despitethe extensive literature on the analysis of presence-only data. We considerinhomogeneous Poisson point process models [Cressie (1993); Diggle (2003)],which make the following two assumptions:

1. The locations of the n point events (y1, . . . , yn) are independent.2. The intensity at point yi [(yi), denoted as i for convenience], the lim-

iting expected number of presences per unit area [Cressie (1993)], canbe modeled as a function of the k explanatory variables. We assume alog-linear specification:

log(i) = 0 +k

j=1

xijj ,(2.1)

although note that the linearity assumption can be relaxed in the usualway (e.g., using quadratic terms or splines). The parameters of the modelfor the i are stored in the vector = (0, 1, . . . , k).

Note that the process being modeled here is locations where an organism hasbeen reported rather than locations where individuals of the organism occur.Hence, the independence assumption would only be violated by interactionsbetween records of sightings rather than by interactions between individ-ual organisms per se. The atlas data of Figure 1 consist of 721 A. costatarecords accumulated over a period of 35 years in a region of 86,000 km2, soindependence of records seems a reasonable assumption in this case, giventhe rarity of event reporting. Nevertheless, the methods we review here canbe generalized to handle dependence between point events [Baddeley andTurner (2005)].

model

explanation | correlation

MaxEntBoostingGLM | GAMRandom Forrest

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Jessica Merrett


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Scalibregmidae sp. Orbiniidae sp. Ostracod sp. 1 Ostracod sp. 3

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Syllidae sp. Amphipoda sp. 1 Nereididae sp. Nemertean sp.

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Brendan Lanham

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DRLUKEHEDGE

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sydney harbour: innovative environmental data science in australia's most iconic waterway

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