An Ecological Trap for Ecologists: Zero-Modified Models Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Tzeng Yih Lam, OSU Manuela Huso, OSU Doug Maguire, OSU ^ Possible

Ecological Trap [ē-k ə - ˈ lä-ji-k ə l ˈ trap] A preference of falsely attractive habitat and a general avoidance of high-quality but less-attractive habitats. Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Wikipedia

Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Cues & The Solutions Zero-Inflated Models Hurdle Models Expected Count – Observed Count Conclusions ‘The Cues’: For rare species data, the marginal count frequency distribution contains large number of zeros, Poisson and/or NB GLM have poor fit. ‘The Solutions’: Zero-modified Models: A general class of finite mixture models that account for excessive zeros, Zero-Inflated Models (ZI) 1, Hurdle Models (H) 2. Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Lambert (1992); 2 Mullahy (1986)

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Cues & The Solutions Zero-Inflated Models Hurdle Models Expected Count – Observed Count Conclusions Zero-Inflated Models (Poisson; ZIP) Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Lambert (1992)  Two States: Perfect and Imperfect States,  Finite Mixture Model (FMM) with 1 latent structure: An observation belongs to either state.  Specify it as Zero-Inflated Negative Binomial (ZINB). Probability of Belonging to Perfect State

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Cues & The Solutions Zero-Inflated Models Hurdle Models Expected Count – Observed Count Conclusions Hurdle Models (Poisson; HPOIS) Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Mullahy (1986) 2 Baughman (2007)  Under FMM framework comparable to ZI models, Hurdle Models with 2 latent structures 2 : An observation either cross the ‘hurdle’ or not, All observations are in the Imperfect State.  Specify it as Hurdle Negative Binomial (HNB). Probability of Crossing the Hurdle

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Questions The Simulation Study The Bias & AICc Other Preliminary Key Findings Some Plausible Explanations Given known data generating process (dgp): (1)Is there any bias when the data is fitted to different ZI and H model specifications? (2)Is/Are there any universally best fit models? Western Mensurationists’ Meeting 2009 Tzeng Yih Lam

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Questions The Simulation Study The Bias & AICc Other Preliminary Key Findings Some Plausible Explanation 4 Factors (1)LAMBDA ( λ ): (2)INFLA ( p ): (3)RATIO ( Var/Mean ): (4)SAMPLE : Western Mensurationists’ Meeting 2009 Tzeng Yih Lam  For each of the 27 dgp (LAMBDA × INFLA × RATIO), generate 1000 sets of SAMPLE random count,  Fit each set to six model specifications: POIS, NB, ZIP, ZINB, HPOIS, HNB,  Calculate mean %RBIAS for each parameter: λ, p, π and compute AICc.

Western Mensurationists’ Meeting 2009 Tzeng Yih Lam SAMPLE = 100

Western Mensurationists’ Meeting 2009 Tzeng Yih Lam SAMPLE = 100 Bias at LAMBDA = 0.3

Western Mensurationists’ Meeting 2009 Tzeng Yih Lam SAMPLE = 100 Bias with ZIP & HPOIS

Western Mensurationists’ Meeting 2009 Tzeng Yih Lam SAMPLE = 100 Bias with ZINB & HNB

Western Mensurationists’ Meeting 2009 Tzeng Yih Lam POIS NB HNB NB ZINB HNB ZIP ZINB HPOIS HNB ZIP ZINB HPOIS HNB NB ZINB HNB ZIP ZINB HPOIS HNB ZIP ZINB HPOIS HNB NB ZIP ZINB HPOIS HNB SAMPLE = 100 Lowest AICc

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Questions The Simulation Study The Bias & AICc Other Preliminary Key Findings Some Plausible Explanations (1)Variance of estimated λ is the highest when LAMBDA = 0.3, (2)Variance of estimated λ decreases with increasing LAMBDA but it increases with increasing RATIO and/or INFLA, (3)Probability in Perfect State, p, from ZI models has largest (+ve and –ve) bias and variance at LAMBDA = 0.3, (4)Overdispersion parameter, θ, requires ≥ 250 SAMPLE to achieve negligible bias. Western Mensurationists’ Meeting 2009 Tzeng Yih Lam

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Questions The Simulation Study The Bias & AICc Other Preliminary Key Findings Some Plausible Explanations Maximum Likelihood Theory Ingredient = a simulated set of count Optimize the parameter estimates to match the marginal count distribution. Large bias and variance of λ and p at LAMBDA = 0.3, When there are either too many zeros or ones, Binomial GLM seems to be unstable Min and Agresti (2005)  unstable estimates of p  unstable estimates of λ and θ. There might not be enough information when LAMBDA = 0.3. ZI models estimation are based on EM algorithm, H models separately maximize the likelihood functions of π and λ. Western Mensurationists’ Meeting 2009 Tzeng Yih Lam

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Perfect State in Ecology Context: It is a set of habitat conditions that do not host the interested species, Imperfect State in Ecology Context: It is a set of habitat conditions that host the interested species but one may not find the species there, This does not directly differentiate sink & source, saturated & unsaturated habitat, fundamental & realized niche etc. Zero Structural Random Accidental Stochastic Sampling True False

A: “Did you smoke any cigarette last week?” B: “No” ; 0 A: “Are you a smoker?” B: “Yes” Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Zero-Inflated Models have 2 states: Perfect and Imperfect States Main Assumption: You do not know the observation belong to which state. Hurdle models have 1 state: Imperfect State Zero-Inflated Models Hurdle Models

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam A priori knowledge such as species habitat range, will likely influence the model choice In ecology, scale matters … Grains Extent POIS NB ZIP ZINB HPOIS HNB

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Modeling of rare species habitat association Cunningham & Lindenmayer (2005) and many others…

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity, Extent & Grains Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam What Do the Ecologists Need To Do?

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity, Extent & Grains Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam The Great Escape (1963) Capt. Hilts (The Cooler King) 1961 British 650cc Triumphs

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity, Extent & Grains Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Escape from defining the types of zeros: There is no restriction on threshold for mixing & hurdle, Change current threshold from 0  1, Change perfect to near-perfect state (ZI models), changing ecological implication of the models. N-mixture models (Royle 2004) Escape from using ZI & H models : If one is uncomfortable with two-states processes, Small Area Estimation Rao(2003), Extreme Value Model Coles(2001).

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Acknowledgement Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States Acknowledgement A Priori Knowledge Rarity, Extent & Grains Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Hayes Family Foundation Funds for Silviculture Alternatives Dilworth Awards, OSU Doug Maguire

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Acknowledgement Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States Acknowledgement A Priori Knowledge Rarity, Extent & Grains Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam Thank You for Listening! Any *Err… Question?

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Questions The Simulation Study The Bias & AICc Other Preliminary Key Findings Some Plausible Explanations AICc, Information theory. More flexible model parameterization will have better fit. Sample size is an issue for ZINB and HNB models for fitting. Western Mensurationists’ Meeting 2009 Tzeng Yih Lam

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory The Questions The Simulation Study The Bias & AICc Other Preliminary Key Findings Some Plausible Explanations Western Mensurationists’ Meeting 2009 Tzeng Yih Lam

Cues for Using Zero-Modified Models Cues for Zero-Modified Models Possible Trap #1 Possible Trap #2 Discussions Count & Normal Theory Perfect & Imperfect States A Priori Knowledge Rarity Conclusions Western Mensurationists’ Meeting 2009 Tzeng Yih Lam A priori knowledge such as species habitat range, and extent and grains will likely influence the model choice.