Comparing the PIF approach to a pixel-based approach for birds in Alberta

Comparing the PIF approach to a pixel-based approach for birds in Alberta

This talk is an earlier draft of the research paper:

Sólymos, P., Toms, J. D., Matsuoka, S. M., Cumming, S. G., Barker, N. K. S., Thogmartin, W. E., Stralberg, D., Crosby, A. D., Dénes, F. V., Haché, S., Mahon, C. L., Schmiegelow, F. K. A., and Bayne, E. M., 2020. Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada. Condor, https://doi.org/10.1093/condor/duaa007

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Peter Solymos

July 12, 2018
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Transcript

  1. 1.

    At the end of the road Comparing the PIF approach

    to a pixel-based approach for birds in Alberta Péter Sólymos & Boreal Avian Modelling Project Team PIF Science – July 12, 2018 1
  2. 2.

    Measure what you manage PIF population size estimator is sample

    based The approach involves several key assumptions: • about detectability • and sample representation 2
  3. 3.

    Adjustment factors Detection distance: • MDD based on a field

    study & expert assessment • Found to be most influential on estimates [1] • Pop size can be 5x higher when using EDR [2] • MDDs were revised in 2013 [1] Thogmartin 2010 Ecol Mod 221:173 [2] Matsuoka et al. 2012 Auk 129:268 Distance from observer P(detection | availability) MDD 1 0 3
  4. 4.

    Adjustment factors Detection distance: • MDD based on a field

    study & expert assessment • Found to be most influential on estimates [1] • Pop size can be 5x higher when using EDR [2] • MDDs were revised in 2013 [1] Thogmartin 2010 Ecol Mod 221:173 [2] Matsuoka et al. 2012 Auk 129:268 Distance from observer P(detection | availability) EDR MDD 1 0 4
  5. 5.

    Adjustment factors Time-of-day adjustment: • Empirically derived • Found to

    be less influential on estimates • Pop size can be 1.2x higher when using P(avail) [1] 5 Time-of-day (stop #) Mean count / max Tadj = Max / Mean 0 1 0
  6. 6.

    Adjustment factors Time-of-day adjustment: • Empirically derived • Found to

    be less influential on estimates • Pop size can be 1.2x higher when using P(avail) [1] [1] Sólymos et al. in press Condor Time-of-day (stop #) Mean count / max Tadj = Max / Mean 1/P(avail) P(availability | presence) 1 0 1 0 P max = 0.66 < 1 6
  7. 7.
  8. 8.

    Adjustment factors Pair adjustment: • Originally defaulted to 2 •

    Species specific values used since 2013 These adjustment factors are not fully effective, but they do go some way towards addressing potential bias in the population estimates 8
  9. 9.

    Other potential sources of bias • Habitat representation • Roadside

    count Assumptions were necessary to extrapolate from BBS counts to unsampled areas Comparison to Atlas data indicated that assumptions were reasonable. 9
  10. 10.

    BCR 6 “any land-cover-based roadside bias in the bird data

    of the BBS is likely minimal” [1] 10 [1] Veech et al. 2017 Condor 119:607
  11. 11.

    BCR 6 “any land-cover-based roadside bias in the bird data

    of the BBS is likely minimal” [1] “anthropogenic disturbance is overrepresented in southern strata, and forest fires are underrepresented in almost all strata.” [2] [1] Veech et al. 2017 Condor 119:607 [2] Van Wilgenburg et al. 2015 ACE 10:5 11
  12. 12.

    Land cover Availability % Roadside % Deciduous forest <80 yr

    13 23 Deciduous forest ≥80 yr 10 3 Mixedwood forest <80 yr 3 3 Mixedwood forest ≥80 yr 3 2 Coniferous forest <80 yr 3 1 Coniferous forest ≥80 yr 4 2 Pine dominated forest <80 yr 6 5 Pine dominated forest ≥80 yr 4 3 Black spruce forest <80 yr 9 3 Black spruce forest ≥80 yr 11 2 Larch dominated wetland <80 yr 2 0 Larch dominated wetland ≥80 yr 2 0 Grasses and herbs 0 1 Shrubs 2 1 Swamp 4 1 Shrubby wetlands 8 1 Non-treed wetlands 2 0 Cultivation 11 45 Urban and industrial 1 3 BCR 6 “at the northern limit of BBS coverage where roadless areas predominate and roads typically sample a geographically biased portion of the landscape” [1] [1] Rosenberg and Blancher 2005 12 Roadside surveys do not sample land covers in proportion to their availabilities in N Alberta
  13. 13.

    BAM Team 13 Steering Committee Erin Bayne (U of A),

    Steve Cumming (U. Laval), Samantha Song (ECCC), Fiona Schmiegelow (U of A) Project Staff, Students, and Contributing Scientists Nicole Barker (Coordinating Scientist), Trish Fontaine (Spatial Database Manager), Peter Solymos (Statistical Ecologist), Diana Stralberg (Ecologist), Lionel Leston (PDF), Alberto Suarez Esteban (PDF), Tara Stehelin (PhD Student), Alana Westwood (Contributing Scientist), Samuel Haché (Contributing Scientist, CWS), Lisa Mahon (Contributing Scientist, CWS), Steven Van Wilgenburg (Contributing Scientist, CWS), Judith Toms (Contributing Scientist, CWS), Steve Matsuoka (Contributing Scientist, USGS Alaska). Technical Committee • Marcel Darveau, Ducks Unlimited Canada & Université Laval • André Desrochers, Université Laval • Pierre Drapeau, Université de Québec à Montréal • Charles Francis, Environment and Climate Change Canada • Colleen Handel, United States Geological Survey • Keith Hobson, Environment and Climate Change Canada & University of Western Ontario • Craig Machtans, Environment and Climate Change Canada • Julienne Morissette, Ducks Unlimited Canada • Gerald Niemi, University of Minnesota - Duluth • Rob Rempel, Ontario Ministry of Natural Resources • Stuart Slattery, Ducks Unlimited Canada • Phil Taylor, Acadia University/BSC • Lisa Venier, Canadian Forest Service • Pierre Vernier, University of Alberta • Marc-André Villard, Université de Moncton
  14. 14.

    Funding partners 14 Grants Environment and Climate Change Canada (Canadian

    Wildlife Service, Migratory Birds Program) Joint Oil Sands Monitoring Institutional/Infrastructure Support University of Alberta Université Laval Past Funders Alberta Biodiversity Monitoring Institute, Alberta Conservation Association, Alberta Innovates Technology Futures, Alberta Land-use Framework (Gov. Alberta), Alberta Research Council Inc., Alberta Pacific Forest Industries Inc., Boreal Ecosystems Assessment for Conservation Networks (BEACONs), Canada Foundation for Innovation, Canada Research Chairs, Canadian Boreal Initiative, Canada Foundation for Innovation, Climate Change and Emissions Management Corporation, Ducks Unlimited Canada, EC Habitat Stewardship Funds, Environmental Studies Research Fund, Fonds Québécois de la recherche sur la nature et les technologies, Forest Products Association of Canada, Geomatics for Informed Decisions (GEOIDE), Killam Trusts, Ministère des Ressources naturelles et de la Faune (MRNF) Quebec, National Fish & Wildlife Foundation, NSERC, Sustainable Forest Management Network, US Landscape Conservation Cooperatives, USFWS Neotropical Migratory Bird Conservation Act Grants Program, Vanier Canada Graduate Scholarships
  15. 15.

    Data partners 15 Individuals K. Aitken, A. Ajmi, B. Andres,

    J. Ball, E. Bayne, P . Belagus, S. Bennett, R. Berger, M. Betts, J. Bielech, A. Bismanis, R. Brown, M. Cadman, D. Collister, M. Cranny, S. Cumming, L. Darling, M. Darveau, C. De La Mare, A. Desrochers, T. Diamond, M. Donnelly, C. Downs, P . Drapeau, C. Duane, B. Dube, D. Dye, R. Eccles, P . Farrington, R. Fernandes, M. Flamme, D. Fortin, K. Foster, M. Gill, T. Gotthardt, N. Guldager, R. Hall, C. Handel, S. Hannon, B. Harrison, C. Harwood, J. Herbers, K. Hobson, M.-A. Hudson, L. Imbeau, P . Johnstone, V. Keenan, K. Koch, M. Laker, S. Lapointe, R. Latifovic, R. Lauzon, M. Leblanc, L. Ledrew, J. Lemaitre, D. Lepage, B. MacCallum, P . MacDonell, C. Machtans, C. McIntyre, M. McGovern, D. McKenney, S. Mason, L. Morgantini, L. Morton, G. Niemi, T. Nudds, P . Papadol, M. Phinney, D. Phoenix, D. Pinaud, D. Player, D. Price, R. Rempel, A. Rosaasen, S. Running, R. Russell, C. Savignac, J. Schieck, F . Schmiegelow, D. Shaw, P . Sinclair, A. Smith, S. Song, C. Spytz, D. Swanson, S. Swanson, P . Taylor, S. Van Wilgenburg, P . Vernier, M.-A. Villard, D. Whitaker, T. Wild, J. Witiw, S. Wyshynski, M. Yaremko, as well as the hundred of volunteers collecting Breeding Bird Survey (BBS) data. Institutions Acadia University; Alaska Bird Observatory; Alaska Natural Heritage Program; Alberta Biodiversity Monitoring Institute; Alberta Pacific Forest Industries Inc.; AMEC Earth & Environmental; AREVA Resources Canada Inc.; Avian Knowledge Network; AXYS Environmental Consulting Ltd.; Bighorn Wildlife Technologies Ltd.; Bird Studies Canada; Breeding Bird Survey (coordinated in Canada by Environment Canada); BC Breeding Bird Atlas; Canadian Natural Resources Ltd.; Canfor Corporation; Daishowa Marubeni International Ltd; Canada Centre for Remote Sensing and Canadian Forest Service, Natural Resources Canada; Canadian Wildlife Service and Science & Technology Branch, Environment Canada; Global Land Cover Facility; Golder Associates Ltd.; Government of British Columbia; Government of Yukon; Hinton Wood Products; Hydro-Québec Équipement; Kluane Ecosystem Monitoring Project; Komex International Ltd.; Louisiana Pacific Canada Ltd.; Manitoba Breeding Bird Atlas; Manitoba Hydro; Manitoba Model Forest Inc.; Manning Diversified Forest Products Ltd.; Maritimes Breeding Bird Atlas; Matrix Solutions Inc. Environment & Engineering; MEG Energy Corp.; Mirkwood Ecological Consultants Ltd.; NatureCounts; Nature Serve; Numerical Terradynamic Simulation Group; Ontario Breeding Bird Atlas; Ontario Ministry of Natural Resources; OPTI Canada Inc.; PanCanadian Petroleum Limited; Parks Canada (Mountain National Parks Avian Monitoring Database); Petro Canada; Principal Wildlife Resource Consulting; Regroupement QuébecOiseaux; Rio Alto Resources International Inc.; Saskatchewan Environment; Shell Canada Ltd.; Suncor Energy Inc.; Tembec Industries Inc.; Tolko Industries Ltd.; U.S. Army; U.S. Fish and Wildlife Service; U.S. Geological Survey, Alaska Science Center; U.S. National Park Service; Université de Moncton; Université du Québec à Montréal; Université du Québec en Abitibi-Témiscamingue; Université Laval; University of Alaska, Fairbanks; University of Alberta; University of British Columbia; University of Guelph; University of New Brunswick; University of Northern British Columbia; URSUS Ecosystem Management Ltd.; West Fraser Timber Co. Ltd.; Weyerhaeuser Company Ltd.; Wildlife Resource Consulting Services MB Inc.
  16. 16.
  17. 17.

    Roadside count assumption To what extent it is: • numerical

    response, • habitat use / behaviour, or • detectability? Yip et al. 2017 Condor 119:73 17
  18. 18.

    Ball et al. 2016 ACE 11:10 “Pixel” based approaches CAWA

    Stralberg et al. 2015 Ecol Appl 21:1112 Spatial proximity Land use and disturbance Climate 18 Thogmartin et al. 2004 Ecol Appl 14:1766
  19. 19.

    My goal today Compare sample based (PIF approach) and model

    based (“pixel” approach) population size estimates in BCR 6 Alberta Understand how and why population size estimates differ Suggest future improvements to continental scale population size estimation 1 2 3 19
  20. 20.

    N PIF E[Y 1 ] P T adj Area 1

    πMDD2 = Roadside count Pair adjustment Time adjustment Detection distance Area of region 20
  21. 21.

    N PIF N PIX E[Y 1 ] E[Y 0 ]

    P T adj Area 1 πMDD2 = Roadside count Pair adjustment Time adjustment Detection distance = P 1/p 3min 1 πEDR2 Area Area of region 21
  22. 22.

    N PIF N PIX E[Y 1 ] E[Y 0 ]

    P T adj Area 1 πMDD2 = P 1/p 3min 1 πEDR2 Area 22
  23. 23.

    N PIF N PIX E[Y 1 ] E[Y 0 ]

    P T adj Area 1 πMDD2 = P 1/p 3min 1 πEDR2 Area 23 We estimate # of singing individuals Area of region is the same (BCR 6 Alberta)
  24. 24.

    N PIF N PIX E[Y 1 ] E[Y 0 ]

    T adj 1 πMDD2 = 1/p 3min 1 πEDR2 24
  25. 25.

    N PIF N PIX E[Y 1 ] E[Y 0 ]

    T adj 1 πMDD2 = 1/p 3min 1 πEDR2 Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0 ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF 25
  26. 26.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF 26 Σ D k w k Σ D k a k N PIF ~ N true H adj H adj = N PIX N PIF = 1 H adj …
  27. 27.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF 27 Absolute abundance increased >10x for some species Others became less abundant without pair adjustment
  28. 28.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF 28 Ranking has changed for some sensitive species, but not for others Absolute changes do not always translate into change in rank without pair adjustment
  29. 30.

    30 Canonical correspondence analysis using abundance in land cover types

    Open, developed Treed wetlands Upland forests Mature – Young Coniferous – Deciduous
  30. 31.

    31 Canonical correspondence analysis using abundance in land cover types

    Species associated with open habitats tend to have higher PIF population estimates
  31. 32.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF N PIX > N PIF for ¾ of the species OBS = Observed log ratio of PIX/PIF estimates EXP = Expected log ratio = R + T + A + H EXP is a little bit higher, but not too much 32 OBS EXP
  32. 33.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF The roadside count component is almost symmetric around 0, large variation across species 33
  33. 34.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF The time component is positive for most species, variation is relatively small 34
  34. 35.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF Area component is positive for all species, higher than time adjustment, smaller variation across species 35
  35. 36.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF 36 Habitat component is centered at 0, moderate variation across species
  36. 37.

    Roadside count Habitat adjustment Time adjustment Area sampled E[Y 0

    ] E[Y 1 ] 1 p 3min T adj MDD2 EDR2 = 1 H adj N PIX N PIF OBS = a + b 1 R + b 2 T + b 3 A + b 4 H + error Ideally, a = 0, b = 1, error = 0, so that OBS = R + T + A + H 37
  37. 38.

    OBS = a + b 1 R + b 2

    T + b 3 A + b 4 H + error Ideally, a = 0, b = 1, error = 0, so that OBS = R + T + A + H • Road count & Habitat related biases dominate • EDR vs. MDD was thought to be most influential – now only 3rd • Habitat adjustment values are underestimated • There is still something left to be explained (~10%) • Interactions were not important 38 Model terms Estimate SE P-value % variance a –0.141 0.170P(a = 0) = 0.411 b 1 (roadside count) 0.951 0.054P(b1 = 1) = 0.364 58.5 b 2 (time adjustment) 0.895 0.150P(b2 = 1) = 0.368 3.8 b 3 (area adjustment) 1.003 0.124P(b3 = 1) = 0.981 9.2 b 4 (habitat adjustment) 1.208 0.092P(b4 = 1) = 0.024 18.6 error σ2 = 0.256 9.9
  38. 39.

    OBS = a + b 1 R + b 2

    T + b 3 A + b 4 H + error Ideally, a = 0, b = 1, error = 0, so that OBS = R + T + A + H • Road count & Habitat related biases dominate • EDR vs. MDD was thought to be most influential – now only 3rd • Habitat adjustment values are underestimated • There is still something left to be explained (~10%) • Interactions were not important 39 Model terms Estimate SE P-value % variance a –0.141 0.170P(a = 0) = 0.411 b 1 (roadside count) 0.951 0.054P(b1 = 1) = 0.364 58.5 b 2 (time adjustment) 0.895 0.150P(b2 = 1) = 0.368 3.8 b 3 (area adjustment) 1.003 0.124P(b3 = 1) = 0.981 9.2 b 4 (habitat adjustment) 1.208 0.092P(b4 = 1) = 0.024 18.6 error σ2 = 0.256 9.9
  39. 40.

    OBS = a + b 1 R + b 2

    T + b 3 A + b 4 H + error Ideally, a = 0, b = 1, error = 0, so that OBS = R + T + A + H Model terms Estimate SE P-value % variance a –0.141 0.170P(a = 0) = 0.411 b 1 (roadside count) 0.951 0.054P(b1 = 1) = 0.364 58.5 b 2 (time adjustment) 0.895 0.150P(b2 = 1) = 0.368 3.8 b 3 (area adjustment) 1.003 0.124P(b3 = 1) = 0.981 9.2 b 4 (habitat adjustment) 1.208 0.092P(b4 = 1) = 0.024 18.6 error σ2 = 0.256 9.9 • Road count & Habitat related biases dominate • EDR vs. MDD was thought to be most influential – now only 3rd • Habitat adjustment values are underestimated • There is still something left to be explained (~10%) • Interactions were not important 40
  40. 41.

    Summary Spatially explicit predictions lead to altered ranking Forest species

    were under-, generalists were over- estimated by PIF Differences were driven by the habitat & roadside count assumptions (70%) The usual suspects (time & area) contributed to 13% 41 1 2 3 4
  41. 42.

    Adjusted adjustments BBS data, same methodology, some adjustments: 1. Use

    EDR instead of MDD where applicable (or provide a ‘lower bound’ in tables so that estimates can be recalculated if needed) 2. Consider adjusting time component (set max as 0.66 instead of 1) 42 Time adjustment Area sampled
  42. 43.

    Different methodology Other data sources, revised methodology, with adjustments: 1.

    Stratify based on broad land cover (open/forest) 2. Incorporate off-road data Need to better understand & attribute mechanisms contributing to roadside count bias 43 Roadside count Habitat adjustment