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A generic method for estimating and smoothing multispecies biodiversity indices, robust to intermittent data

Freeman, Stephen N. and Isaac, Nicholas J.B. and Besbeas, Panagiotis and Dennis, Emily B. and Morgan, Byron J. T. (2019) A generic method for estimating and smoothing multispecies biodiversity indices, robust to intermittent data. Technical report. University of Kent, Kent, UK (Submitted) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Abstract

Biodiversity indicators provide a powerful and convenient way to summarise extensive, complex ecological data sets and are important in influencing government policy on biodiversity and conservation. Typically, component data consist of time-varying indices for each of a number of different species. There currently exists a wide range of different biodiversity indicators, but their derivation from these indices varies and they suffer from a range of statistical shortcomings. In this paper we describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy11probability of the contributing individual species. Our formulation is flexible and applicable to a wide range of taxa. It possesses a number of desirable features, including: 1) it provides a natural way to incorporate the sporadic unavailability of data; 2) it can incorporate variation between years and species in the precision with which the individual species’ indices are estimated, and 3) it allows the direct incorporation of smoothing over time. Furthermore, the same algorithm can be adopted for cases based on count (abundance) or ‘presence-absence’ (geographical range or distribution) data - only the subsequent interpretation differs. Model fitting is straightforward in either Bayesian or classical implementations, the latter following from efficient hidden Markov modelling. The procedure removes the need for bootstrapping, which can be prohibitive when huge volumes of data are available. We illustrate these desirable properties through the use of simulated data, and by applying our method to a suite of national-scale data sets from the UK.

Item Type: Monograph (Technical report)
Uncontrolled keywords: Bats, Butterflies, Dragonflies, Hidden Markov models, hierarchical models, State-space models
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QL Zoology
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Byron Morgan
Date Deposited: 16 May 2019 11:23 UTC
Last Modified: 03 Jun 2019 09:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73938 (The current URI for this page, for reference purposes)
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