unstructuredio/SCORE-Bench · Datasets at Hugging Face

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25°S 30°S NE Australia 35°S SE Australia Great Australian Bight Bass Strait 40°S 250 km Tasman Sea 130°E 135°E 140°E 145°E 150°E 155°E
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FIGURE 1 Map of sample sites (red) and net primary production (NPP) around southern and eastern Australia
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& Dunstan, 2010). These covariates included oceanographic variables such as seafloor water temperature, salinity and dissolved oxygen, carbon flux to the seafloor, mean annual and seasonal variation of net primary productivity at the sea surface, as well as geographical variables latitude, longitude and depth.
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2 \| METHODS
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2.1 \| Samples
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All samples were collected using an identical 4-m-wide beam trawl with a 25-mm mesh net on soft-sediment substrata in several expeditions to the Great Australian Bight and the eastern continental margin of Australia on the RV Investigator (Table 1, Figure 1). The GAB expeditions included two that formed part of the Great Australian Bight Deepwater Marine Program (IN2015_C01 and IN2017_C01), a partnership of CSIRO and Chevron Australia, and a third that was part of the Great Australian Bight (GAB) Research Program, a
collaboration of CSIRO, BP Australia, the South Australian Research and Development Institute, University of Adelaide and Flinders University. The eastern Australian samples were collected as part of the “Sampling the Abyss” voyage (IN2017_V03) that collected samples every 1.5° of latitude from 42° to 23°S. On all voyages, sites were mapped (bathymetry and backscatter) prior to deployment using a Kongsberg EM 122 multibeam sonar. Samples were sorted, weighed and preserved (95% ethanol and/or formalin) on-board into broad taxonomic groups, but subsequently sent to taxonomic experts for post-voyage identification.
For this study, we have restricted samples to those that were collected at seafloor depths between 1,900 and 5,000 m to ensure inter-regional comparability. The depth of 1,900 m was chosen as the lower limit so as to include two samples from the GAB, whose mean depth along the tow was slightly shallower than the target depth of 2,000 m. We restricted the taxonomic scope to the following megafauna groups that were identified by the same experts (see acknowledgements) across voyages: hexactinellid and demospongid sponges, anthozoans, barnacles, decapods, pycnogonids,
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WILEY


Diversity and Distributions


O'HARA ET AL.
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TABLE 1 Beam trawl sample location and modelled environmental data
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Survey Stn Region Location Latitude Longitude Mid depth (m) Date (day/month/year) Area (m²) Temp. (°C) Salinity (‰) Oxygen (ml/L) C flux (g m⁻² year⁻¹)
IN2015_C01 016 GAB OR02, Area25 −36.069 132.637 4,607 31/10/15 5,666 0.953 34.710 4.708 0.735
IN2015_C01 026 GAB OR07, Area19 −35.794 131.711 4,517 2/11/15 10,927 0.971 34.712 4.709 0.718
IN2015_C01 036 GAB SZ02, Area20 −35.555 132.283 2,242 5/11/15 17,687 2.112 34.725 4.014 1.266
IN2015_C01 064 GAB OR13, Area05 −34.074 129.182 2,726 13/11/15 15,221 1.787 34.733 4.245 0.974
IN2015_C01 079 GAB SZ08, Area08 −34.341 129.942 2,079 16/11/15 7,129 2.245 34.708 3.988 1.192
IN2015_C01 080 GAB SZ08, Area08 −34.408 130.024 2,114 17/11/15 7,916 2.214 34.712 4.002 1.197
IN2015_C02 137 GAB Transect 5 −35.550 134.082 1,961 5/12/15 11,564 2.286 34.707 3.893 1.433
IN2015_C02 141 GAB Transect 5 −35.818 134.109 2,826 5/12/15 12,341 1.761 34.732 4.214 1.067
IN2015_C02 151 GAB Transect 4 −35.798 132.693 2,725 6/12/15 12,867 1.766 34.734 4.201 1.054
IN2015_C02 155 GAB Transect 4 −35.722 132.681 1,933 7/12/15 10,814 2.286 34.714 3.907 1.423
IN2015_C02 207 GAB Transect 3 −35.352 131.077 2,014 9/12/15 14,282 2.288 34.709 3.943 1.282
IN2015_C02 216 GAB Transect 3 −35.262 131.042 3,021 10/12/15 12,056 1.723 34.734 4.234 0.950
IN2015_C02 227 GAB Transect 1 −35.009 130.317 2,839 11/12/15 9,508 1.726 34.734 4.238 0.938
IN2015_C02 274 GAB Transect 2 −35.165 130.665 3,002 12/12/15 11,273 1.647 34.734 4.279 0.910
IN2015_C02 276 GAB Transect 2 −34.853 130.687 2,004 12/12/15 10,440 2.299 34.705 3.949 1.279
IN2015_C02 449 GAB Transect 1 −34.625 130.280 2,037 18/12/15 14,233 2.305 34.703 3.954 1.251
IN2017_C01 175 GAB OR21 −35.815 132.021 4,090 15/04/17 22,871 1.156 34.721 4.570 0.763
IN2017_C01 178 GAB OR21 −35.716 131.656 3,883 16/04/17 21,483 1.242 34.724 4.531 0.777
IN2017_C01 179 GAB OR21 −35.814 131.703 4,684 17/04/17 29,787 0.925 34.709 4.715 0.706
IN2017_C01 182 GAB OR26 −35.487 130.378 4,961 17/04/17 18,706 0.852 34.704 4.814 0.664
IN2017_C01 192 GAB OR11, Area07 −34.550 129.403 3,793 20/04/17 18,320 1.258 34.725 4.508 0.745
IN2017_C01 197 GAB OR11, Area07 −34.447 129.532 3,292 21/04/17 14,740 1.481 34.732 4.383 0.827
IN2017_C01 198 GAB OR11, Area07 −34.549 129.602 3,464 21/04/17 15,875 1.392 34.730 4.455 0.796
IN2017_V03 004 SE Freycinet MP −41.731 149.120 2,785 18/05/17 29,584 1.746 34.735 4.277 1.998
IN2017_V03 006 SE Freycinet MP −41.626 149.552 4,037 18/05/17 30,344 1.041 34.715 4.571 1.260
IN2017_V03 014 SE Flinders MP −40.464 149.102 2,392 20/05/17 15,348 1.988 34.721 4.051 2.352
IN2017_V03 015 SE Flinders MP −40.473 149.397 4,126 20/05/17 10,572 1.045 34.714 4.587 1.198
IN2017_V03 022 SE Bass Strait −39.462 149.276 2,726 22/05/17 11,756 1.788 34.731 4.204 2.049
IN2017_V03 030 SE Bass Strait −39.552 149.553 4,165 23/05/17 29,312 1.077 34.714 4.563 1.181
IN2017_V03 032 SE East Gippsland MP −38.479 150.184 3,851 24/05/17 11,580 1.132 34.718 4.501 1.290
IN2017_V03 035 SE East Gippsland MP −37.792 150.382 2,459 25/05/17 15,352 1.985 34.720 4.108 2.264
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(Continues)
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2.1.2. Weak Identification Aspects
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In particular, Martínez-Iriarte et al. (2020) develops an asymptotic theory framework based on fixed- smoothing asymptotics for the test statistics in order to account for the estimation uncertainty in the underlying LRV estimators. Consider the following long-run variance estimator
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V f f (θ)= limT→∞ Var( 1/√T∑ T t=1 f(Y t ,θ) ).
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(2.8)
Therefore, a non-parametric estimator of the LRV takes the quadratic form below
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V^ f f (θ)= 1/T∑ T t=1 ∑ T s=1 ω h ( t/T,t/T ) [f(Y t ,θ)−fˉ (Y t ,θ)] [f(Y t ,θ)−fˉ (Y t ,θ)]′
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(2.9)
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fˉ (Y t ,θ)= 1/T ∑ T s=1 f(Y t ,θ),
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(2.10)
such that ω(.,.) is a weighting function, and h is the smoothing parameter indicating the amount of nonparametric smoothing. For example, we can estimate the kernel density the following way
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ω h (t/T , t/T) = k ((t − s)/hT)
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25°S 30°S NE Australia 35°S SE Australia Great Australian Bight Bass Strait 40°S 250 km Tasman Sea 130°E 135°E 140°E 145°E 150°E 155°E

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FIGURE 1 Map of sample sites (red) and net primary production (NPP) around southern and eastern Australia

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& Dunstan, 2010). These covariates included oceanographic variables such as seafloor water temperature, salinity and dissolved oxygen, carbon flux to the seafloor, mean annual and seasonal variation of net primary productivity at the sea surface, as well as geographical variables latitude, longitude and depth.

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2 | METHODS

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2.1 | Samples

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All samples were collected using an identical 4-m-wide beam trawl with a 25-mm mesh net on soft-sediment substrata in several expeditions to the Great Australian Bight and the eastern continental margin of Australia on the RV Investigator (Table 1, Figure 1). The GAB expeditions included two that formed part of the Great Australian Bight Deepwater Marine Program (IN2015_C01 and IN2017_C01), a partnership of CSIRO and Chevron Australia, and a third that was part of the Great Australian Bight (GAB) Research Program, a

collaboration of CSIRO, BP Australia, the South Australian Research and Development Institute, University of Adelaide and Flinders University. The eastern Australian samples were collected as part of the “Sampling the Abyss” voyage (IN2017_V03) that collected samples every 1.5° of latitude from 42° to 23°S. On all voyages, sites were mapped (bathymetry and backscatter) prior to deployment using a Kongsberg EM 122 multibeam sonar. Samples were sorted, weighed and preserved (95% ethanol and/or formalin) on-board into broad taxonomic groups, but subsequently sent to taxonomic experts for post-voyage identification.

For this study, we have restricted samples to those that were collected at seafloor depths between 1,900 and 5,000 m to ensure inter-regional comparability. The depth of 1,900 m was chosen as the lower limit so as to include two samples from the GAB, whose mean depth along the tow was slightly shallower than the target depth of 2,000 m. We restricted the taxonomic scope to the following megafauna groups that were identified by the same experts (see acknowledgements) across voyages: hexactinellid and demospongid sponges, anthozoans, barnacles, decapods, pycnogonids,

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4 |

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WILEY

Diversity and Distributions

O'HARA ET AL.

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--------------------------------------------------- Unstructured Caption Begin

TABLE 1 Beam trawl sample location and modelled environmental data

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Survey Stn Region Location Latitude Longitude Mid depth (m) Date (day/month/year) Area (m²) Temp. (°C) Salinity (‰) Oxygen (ml/L) C flux (g m⁻² year⁻¹)

IN2015_C01 016 GAB OR02, Area25 −36.069 132.637 4,607 31/10/15 5,666 0.953 34.710 4.708 0.735

IN2015_C01 026 GAB OR07, Area19 −35.794 131.711 4,517 2/11/15 10,927 0.971 34.712 4.709 0.718

IN2015_C01 036 GAB SZ02, Area20 −35.555 132.283 2,242 5/11/15 17,687 2.112 34.725 4.014 1.266

IN2015_C01 064 GAB OR13, Area05 −34.074 129.182 2,726 13/11/15 15,221 1.787 34.733 4.245 0.974

IN2015_C01 079 GAB SZ08, Area08 −34.341 129.942 2,079 16/11/15 7,129 2.245 34.708 3.988 1.192

IN2015_C01 080 GAB SZ08, Area08 −34.408 130.024 2,114 17/11/15 7,916 2.214 34.712 4.002 1.197

IN2015_C02 137 GAB Transect 5 −35.550 134.082 1,961 5/12/15 11,564 2.286 34.707 3.893 1.433

IN2015_C02 141 GAB Transect 5 −35.818 134.109 2,826 5/12/15 12,341 1.761 34.732 4.214 1.067

IN2015_C02 151 GAB Transect 4 −35.798 132.693 2,725 6/12/15 12,867 1.766 34.734 4.201 1.054

IN2015_C02 155 GAB Transect 4 −35.722 132.681 1,933 7/12/15 10,814 2.286 34.714 3.907 1.423

IN2015_C02 207 GAB Transect 3 −35.352 131.077 2,014 9/12/15 14,282 2.288 34.709 3.943 1.282

IN2015_C02 216 GAB Transect 3 −35.262 131.042 3,021 10/12/15 12,056 1.723 34.734 4.234 0.950

IN2015_C02 227 GAB Transect 1 −35.009 130.317 2,839 11/12/15 9,508 1.726 34.734 4.238 0.938

IN2015_C02 274 GAB Transect 2 −35.165 130.665 3,002 12/12/15 11,273 1.647 34.734 4.279 0.910

IN2015_C02 276 GAB Transect 2 −34.853 130.687 2,004 12/12/15 10,440 2.299 34.705 3.949 1.279

IN2015_C02 449 GAB Transect 1 −34.625 130.280 2,037 18/12/15 14,233 2.305 34.703 3.954 1.251

IN2017_C01 175 GAB OR21 −35.815 132.021 4,090 15/04/17 22,871 1.156 34.721 4.570 0.763

IN2017_C01 178 GAB OR21 −35.716 131.656 3,883 16/04/17 21,483 1.242 34.724 4.531 0.777

IN2017_C01 179 GAB OR21 −35.814 131.703 4,684 17/04/17 29,787 0.925 34.709 4.715 0.706

IN2017_C01 182 GAB OR26 −35.487 130.378 4,961 17/04/17 18,706 0.852 34.704 4.814 0.664

IN2017_C01 192 GAB OR11, Area07 −34.550 129.403 3,793 20/04/17 18,320 1.258 34.725 4.508 0.745

IN2017_C01 197 GAB OR11, Area07 −34.447 129.532 3,292 21/04/17 14,740 1.481 34.732 4.383 0.827

IN2017_C01 198 GAB OR11, Area07 −34.549 129.602 3,464 21/04/17 15,875 1.392 34.730 4.455 0.796

IN2017_V03 004 SE Freycinet MP −41.731 149.120 2,785 18/05/17 29,584 1.746 34.735 4.277 1.998

IN2017_V03 006 SE Freycinet MP −41.626 149.552 4,037 18/05/17 30,344 1.041 34.715 4.571 1.260

IN2017_V03 014 SE Flinders MP −40.464 149.102 2,392 20/05/17 15,348 1.988 34.721 4.051 2.352

IN2017_V03 015 SE Flinders MP −40.473 149.397 4,126 20/05/17 10,572 1.045 34.714 4.587 1.198

IN2017_V03 022 SE Bass Strait −39.462 149.276 2,726 22/05/17 11,756 1.788 34.731 4.204 2.049

IN2017_V03 030 SE Bass Strait −39.552 149.553 4,165 23/05/17 29,312 1.077 34.714 4.563 1.181

IN2017_V03 032 SE East Gippsland MP −38.479 150.184 3,851 24/05/17 11,580 1.132 34.718 4.501 1.290

IN2017_V03 035 SE East Gippsland MP −37.792 150.382 2,459 25/05/17 15,352 1.985 34.720 4.108 2.264

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(Continues)

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--------------------------------------------------- Unstructured Title Begin

2.1.2. Weak Identification Aspects

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In particular, Martínez-Iriarte et al. (2020) develops an asymptotic theory framework based on fixed- smoothing asymptotics for the test statistics in order to account for the estimation uncertainty in the underlying LRV estimators. Consider the following long-run variance estimator

--------------------------------------------------- Unstructured Formula Begin

V f f (θ)= limT→∞ Var( 1/√T∑ T t=1 f(Y t ,θ) ).

--------------------------------------------------- Unstructured Formula End

(2.8)

Therefore, a non-parametric estimator of the LRV takes the quadratic form below

--------------------------------------------------- Unstructured Formula Begin

V^ f f (θ)= 1/T∑ T t=1 ∑ T s=1 ω h ( t/T,t/T ) [f(Y t ,θ)−fˉ (Y t ,θ)] [f(Y t ,θ)−fˉ (Y t ,θ)]′

--------------------------------------------------- Unstructured Formula End

(2.9)

--------------------------------------------------- Unstructured Formula Begin

fˉ (Y t ,θ)= 1/T ∑ T s=1 f(Y t ,θ),

--------------------------------------------------- Unstructured Formula End

(2.10)

such that ω(.,.) is a weighting function, and h is the smoothing parameter indicating the amount of nonparametric smoothing. For example, we can estimate the kernel density the following way

--------------------------------------------------- Unstructured Formula Begin

ω h (t/T , t/T) = k ((t − s)/hT)

--------------------------------------------------- Unstructured Formula End