Detecting Diversity: Paradigm Shifts in Estimation of Species Distribution and Abundance

Transcription

1 Detecting Diversity: Paradigm Shifts in Estimation of Species Distribution and Abundance Steve Beissinger Dept. of Environmental Science, Policy & Management & Museum of Vertebrate Zoology U.C. Berkeley

2 Pre-1940 MVZ Specimen Locality Records Published transects Joseph Grinnell and students collecting birds and mammals, and writing field notes

3 Pre-1940 MVZ Specimen Locality Records ~ 3 times more species per site observed than collected Field Notes Published transects ~ 3 times more species per site observed than collected

4 Grinnell Resurvey Project: Status and Future Bird resurveys Lassen Tahoe 2008 Yosemite White/Inyo Southern Sierra Mojave, Joshua Tree and San Jacinto Sierra and Coastal range resurveys ( ) Modeling past & future change ( ) Current resurveys in the CA desert and Central Valley ( )

5 Global Biodiversity Monitoring: Sampling for Trends, Searching for Causality Trend Metrics: Species level: - Occurrence (Presence or Absence ) of organisms - Abundance of organisms Community level: - Occurrence (Presence or Absence ) of organisms - Abundance of organisms Ecosystem and higher levels - Occurrence (Presence or Absence ) of pixels/process

6 Are Range Shifts Congruent Across Sierra Nevada Taxa? * * (Rapacciuolo, Maher,, & Beissinger Global Change Biology 20: )

7 Do Species Traits Predict Range Shifts? Mobility Dispersal Ability Migratory Ecology Body Size Reproductive Capacity Fecundity Longevity Ecological Generalization Diet Breadth Habitat Breadth Supports Predictions Contradicts Predictions MacLean and Beissinger unpubl. ms

8 Factors Affecting the Probability of Detecting a Species Species A Species B Group Detection Factors Survey Site Individual Species Effort Observer Method Time of day/year Habitat Noise Elevation Sex Age Distance Behavior Life history Rarity 0 Mean probability of detection 1 Cumulative effects on detectability Iknayan, Tingley, Furnas, & Beissinger TREE 29:97-106

9 Mechanisms that Cause Different Types Of Zero Observations in Count Surveys and their Effects Denes, Silveira & Beissinger Methods in Ecology and Evolution. DOI: / X.12333

10 Occupancy Models (MacKenzie et al. Ecology 2002, 2004; book in 2006) Investigate patterns in occupancy Ψ using detectionnondetection data [h = 0,0,1 1,0,1]. Recognize that an observed absence may be the result of a true absence or a nondetection. Depend on repeated surveys at a site over a short (closed) time period to determine presence or absence. L Single Season Occupancy T T 1 2 s = t t t 1 t= 1 t= 1 n. nt n. nt ( ψ, p h, h,.. h ) ψ p ( 1 p ) ψ ( 1 p ) + ( ψ ) Multi-Season Dynamics Ψ t = Ψ t-1 (1 ε t-1 ) + (1 Ψ t-1 ) δ t-1 N n.

11 Factors Affecting Detectability of 43 Bird Species 8 Candidate Detection Models 1. (.) 2. Era (Grinnell vs. Us) 3. Julian day 4. Observer 5. Era + Julian Day 6. Era * Julian Day 7. Observer + Julian Day 8. Observer + Julian Day + Julian Day * Era Detection models with Cumulative AIC weight Era 0.89 Julian Day 0.71 Observer 0.34 Tingley & Beissinger. Unpublished data.

12 Modeling Approaches for Estimating Abundance of Unmarked Animal Populations (Denes, Silveira & Beissinger MEE)

13 Multi-Species Occupancy and Abundance Models (MSOM and MSAM) MSOM: MSAM: Iknayan, Tingley, Furnas, & Beissinger TREE 29:97-106

14 Modeling Community Change in Faces the Same Detection Problem Tingley & Beissinger Ecology 94:

15 Species Distribution Models use climate to project occupancy. How well do they perform for 18 mammal species we resurveyed? Golden-mantled ground squirrel

16 SDM Model Performance Increased with High Quality Data that was Corrected for Detectability (Smith et al Ecography 36: )

17 Count Metacommunity (100 Bird Species) Slopes for Standardized Effects Δ Temperature Δ Precipitation Land Use Intensity Proportion per Bar Count 50 x = 0.07 x = 0.31 x = Proportion per Bar Count Proportion per Bar Colonization beta Colonization beta Colonization beta x = x = x = Count Proportion per Bar Count Proportion per Bar Count Proportion per Bar Extinction beta Extinction beta Extinction beta

18 Some Concluding Thoughts We are at the start of a statistical shift to the Detecting Diversity Paradigm Big Data and Citizen Science will make huge contributions to future monitoring of global of biodiversity This necessitates developing SDMs that account for both false negatives and false positives More than ever, we will need boots on the ground to do systematic surveys ( Little Data ) We are rapidly losing decades of Little Data that has already been collected

19 Someday your data will be historic. How will you preserve it? Thanks! And to NSF, National Geographic Society, CA Energy Commission, MVZ, and NPS for funding.

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21 SDMs over-predicted presence more than absence (Smith et al Ecography 36: ) False absence: known occurrence predicted to be absence by model False presence: an absence is predicted to be present.

22 Land Use, Climate Change and Bird Resurveys Resurveys ( vs ): 2 km transect, 10 pt. counts,70 sites Climate Change ( vs ): PRISM 1 km buffer Land Use Intensity (Sanderson et al. 2002): 1 km buffer Δ Temperature Δ Precipitation (x = 0.29ºC, ) (x = 4.6mm, ) Land Use Intensity (x = 0.29, )

23 Winners and Losers from Climate (1 C warming & 100 mm drying) and Land Use Change Δ Occupancy from Climate Lose, Win Lose, Lose BUSH STJA OCWA CORA NUWO OSFL RCSP Win, Win MOCH LOSH WTSW LAGO SWTH RBSA AMGO TOSO HAFL LASP AUWA KILL BLPH DUFL HEWA RWBL WEME OATI GCKI HETH WIWR BUOR YWAR CATH WETA PISI RSHA PSFL EUST BRCR SOSP WIWA HUVI HOFI MOQU ATFL RBNU VASW WEBL NAWA BGGN BTPI CHSP WCSP SPSA ACWO BARS BEKI AKES BRBL HOWR CALT MGWA WEKI PUFI MODO LAZB BEWR WEWP AMCR TRES YBCH RTHA VGSW ALHU CAVI CLSW DEJU PUMA HAWO WESJ COHA LEGO AMRO ANHU WBNU TUVU BTYW WAVI BHCO WREN SPTO PIWO CBCH NOFL CAQU DOWO BHGR Win, Lose Δ Occupancy from Land Use Double Whammy: r = 0.63, P < 0.001

24 Multiple-Season Occupancy Models to Estimate Colonization and Extinction (MacKenzie et al. 2002, 2006) Ψ t = Ψ t-1 (1 ε t-1 ) + (1 Ψ t-1 ) δ t-1 Occupied Undetected Not extinct Unoccupied Colonized { } Detection history

25 Inference About Turnover based on Naïve Site Occupancy History (1 = present and 0 = absent) for two eras (historic = h and modern = m) derived from its probability of detection (D). Occupancy history Persistence (1,1) Colonization (0,1) Extinction (1,0) Unoccupied (0,0) 1, ,1 1-D h D h 0 0 1,0 1-D m 0 D m 0 0,0 (1-D h )*(1-D m ) D h *(1-D m ) (1-D h )*D m D h D m

26 Inference About Occupancy based on Naïve Site Occupancy, False Absence and False Detections (z = 1 = present and z = 0 = absent) for two eras (historic = h and modern = m) derived from its probability of detection (D). fersistence (1,1) Colonization (0,1) Extinction (1,0) Unoccupied (0,0) True state Unoccupied (z=0) ,1 1-D h D h 0 0 1,0 1-D m 0 D m 0 0,0 (1-D h )*(1-D m ) D h *(1-D m ) (1-D h )*D m D h D m

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28 Multiple-Season Occupancy Models to Estimate Colonization and Extinction (MacKenzie et al. 2002, 2006) Ψ t = Ψ t-1 (1 ε t-1 ) + (1 Ψ t-1 ) δ t-1 Occupied Undetected Not extinct Unoccupied Colonized { } Detection history

29 Change in Occupancy (Modern Historic) for 100 Coast Range Bird Species Over twice as many species decreased (n = 32) in occupancy by >0.1 as those that increased (n = 15) in occupancy by > Number of Species Mean = Change in Occupancy (Modern Historic)

30 Extinction beta Consistency of Metacommunity Responses: Slopes of Colonization vs. Extinction Δ Temperature Δ Precipitation Land Use Intensity r = -0.26, P < Colonization beta r = -0.06, P < Colonization beta r = -0.30, P < Colonization beta

31 How Does This Affect Avian Communities? Changes in Avian Species Richness = historic = modern Richness significantly decreased over time (GLM, p < 0.05) Tingley & Beissinger Ecology 94:

32 How Does This Affect Communities? Turnover in Avian Species Composition Tingley & Beissinger Ecology 94:

33 Species distribution models (SDMs) overpredict presence more often than absence. Mean = 0.35 Mean = 0.14 False positive rate False negative rate SDMs that had good predictive performance for historic data did not perform as well in projecting contemporary occupancy.

34 Parameterizations of Occupancy Models for Inferring Range Shifts Model Sites Modeled Parameters Example Equations Range Change Inference Single Season Unpaired ψ, p logit( ψ)= β 0 + β 1 era + β 2 gradient + β 3 gradient 2 ; logit( p)= β 4 Probability ψ era= 0 ψ era=1 0 Gradient Multi- Season Paired ψ 0, ε, γ, p logit ( ψ ) 0 = β + ( ε ) = β3 ( γ ) = β5 ( p) = β7 0 2 β1 covariate + β2 gradient ; logit + β4 covariate; logit + β6 covariate; logit Probability ε γ Covariate Tingley and Beissinger TREE 24: