Ecological and anthropogenic covariates ... - GANGAPEDIA

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Identifying ecological and anthropogenic covariates affecting Gharial encounter- rates (habitat-use) To identify factors affecting the encounter rates of gharials in each of the segments, I used Classification and Regression Trees (CART) (Breiman, 1984). The CART method is highly simple, flexible, robust, nonparametric, and hence distribution free; and can accommodate a lack of statistical independence between explanatory covariates and nested nonlinearity (De’ath & Fabricius 2000). Heterogeneity within data is hierarchically partitioned such that variation within data is reduced to the extent possible at each split. Heterogeneity (deviance) refers to the lack of fit between the split in the tree model and the response variable values. Heterogeneity in the response variable is partitioned, at each level in the tree-building process, by selecting the covariates which minimize this variation at that level. I used models with the lowest Residual Mean Deviance and number of terminal nodes (tree complexity) as measures of model selection. Availability of basking sites emerged as the main variable explaining variability in Gharial use of river segments. We can conclude in general that wherever basking sites were present, gharials were able to use them in spite of other factors. However, subject to availability of basking sites, water depth emerged as important for Gharial use of river segments. Separating spatial and covariate effects The Poisson distribution is the most natural choice for modeling count data, such as gharial abundance per site. Rivers are also connected systems and habitat use by Gharials is likely to be influenced by attributes of adjacent sites or sampling units upstream or downstream of the sampling unit.. Therefore we need to take into account 19
the effects of covariates measured at the site as well spatial continuity across sampling units or spatial adjacency. We used Bayesian spatial count regression models for analyzing the effects of two ecological covariates and spatial adjacency on habitat-use (encounter-rates). While frequentist methods treat model parameters as unknown constants, Bayesian analysis consider them as random variables (Ellison 1996). The Poisson distribution is the most natural choice for modeling count data, such as gharial abundance per site. Although the Poisson model is generally used for abundance, animals may not be distributed completely randomly in space, and as there are a large number of unoccupied sites (zeros) as well as clusters of animals at other sites. For site (i), gharial count [i] ~ Intercept + slope * basking site [i] + spatial effect term[i] Or gharial count [i] ~ Intercept + slope * depth [i] + spatial effect term[i] Here, gharial count (site-specific count) is assumed to come from a Zero-inflated Poisson or Negative Binomial distribution (negative binomial distribution can be used for clumped data). Intercept and slope follow uninformative or flat prior normal distributions with zero mean and low precision (high variance). The zeroes of the dataset are separately treated as Bernoulli outcomes with a probability p0 for the proportion of zeroes in the data. The remaining counts are treated as following a Poisson distribution, with overall mean of counts equal to 1 – prob(zero count) * site- specific count. Ecological datasets tend to contain a large proportion of zero values (Clarke & Green 1988), and such data do not readily fit standard distributions (e.g. normal, Poisson, binomial, negative-binomial and beta). These are referred to as ‘zero inflated’ 20
Page 1 and 2: Ecological and anthropogenic covari
Page 3 and 4: variable data like river discharge,
Page 5 and 6: Table of Contents EXECUTIVE SUMMARY
Page 7 and 8: In the face of increasing proposals
Page 9 and 10: Chapter 1 Ecological and Anthropoge
Page 11 and 12: Introduction Humans live disproport
Page 13 and 14: Despite the release of over 5000 yo
Page 15 and 16: its 523 km run, the river passes th
Page 17 and 18: sites considered favourble based on
Page 19 and 20: correction factor for the float mea
Page 21 and 22: Variable (type) Measurement Details
Page 23: interpolation deriving from regiona
Page 27 and 28: We compared Zero-Inflated Poisson (
Page 29 and 30: Assessing changes in habitat availa
Page 31 and 32: Figure 6: Box-and-whiskers plots co
Page 33 and 34: Occasion 1: Residual mean deviance
Page 35 and 36: Occasion 3: Residual mean deviance:
Page 37 and 38: Discussion Humans have preferential
Page 39 and 40: Tucker et al. (1997) report that pr
Page 41 and 42: once caught in the net are wary of
Page 43 and 44: Bustard, HR and LAK Singh. 1977. St
Page 45 and 46: Hutton, J. 1989. Movements, home ra
Page 47 and 48: Oliver, M.A., R. Webster. 1990. Kri
Page 49 and 50: Chapter 2 Photographic identificati
Page 51 and 52: Hussain, 2009); and is especially a
Page 53 and 54: data also enables the estimation of
Page 55 and 56: A 600km stretch of the Chambal Rive
Page 57 and 58: Marked animals were accorded no exc
Page 59 and 60: Single-crested region Figure 2: Lat
Page 61 and 62: A B C Capture: 07.02.2010 Figure 1.
Page 63 and 64: (Nichols, 1992). Since gharials are
Page 65 and 66: and Ross, JP. 2007. Gavialis ganget
Page 67 and 68: Mazzoil M., McCulloch S.D., Defran
Page 69 and 70: GENERAL CONCLUSION Humans have pref
Page 71 and 72: APPENDIX - 1 Figure 1: Scatter plot
Page 73 and 74: Figure 2: Scatter plots representin
Page 75 and 76:
Figure 3: Scatter plots representin
Page 77 and 78:
Figure 4: Scatter plots representin
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