Lecture 8: Population modelling 4 Flashcards
Applying population models to conservation
- On the uses of models in conservation
– general insights vs specific problems
– viability and interventions - Matrices and perturbation analyses
– Sensitivity and elasticity - Life-stage Simulation Analysis (LSA)
- Population Viability Analysis (PVA)
- Minimum Viable Populations
- Other uses of models
– hunting and overexploitation
– causes of decline
Using modelling in conservation
- General insights
– Fisher’s fundamental theorem (ARH lectures)
– Matrix models - sensitivity and life history (see later)
– Logistic model (density dependent growth) and maximum sustainable yield (Schaefer 1954) - System-specific insights
- Forecasting & projection (is there a
problem?)
^ what will happen vs. what would
happen if … ?
– Impacts of interventions
^ what is best course of action?
– Viability
^ probability of persistence over a time-frame
- Forecasting & projection (is there a
Forecasting and projection
e.g. what will happen in 50 years?
Forecasting
- dangerous – best avoided
see Brook et al. 2000; Coulson et al. 2001
Better to project:
Projecting and ‘perturbing’ projections
* with matrix models, we can also easily compute the relative influence of changing different matrix elements
- e.g., does it make more difference to target conservation at increasing fecundity of adults
or increasing juvenile survival?
* these parameters are known as:
- sensitivity (the effect of a small, absolute change in any matrix element)
- elasticity (the effect of a small, relative change in any matrix element)
- For longer-lived spp., elasticity of adult survival tends to be higher than for elasticities associated with
reproduction (fecundity, juvenile survival) - For shorter-lived spp. The reverse is true
(see notes)
General insights and casual threats
See Tiger data from Chapron et al 2008
^ Used data on life-history parameters
* Because tigers have slow life- histories (take a long time to mature to adults) , vulnerability to poaching is higher than for other solitary, big cats
* Even good juvenile survival must be accompanied by high adult survival for population stability
* Concluded that poaching is likely to be a bigger threat than prey depletion
Intervention analyses
*Life-stage simulation analysis
– recognise that vital rates are uncertain
– draw matrix elements from probability distributions
– estimate (gamma)
– re-do multiple times to generate distribution of gamma
* can plot gamma against vital rates to see which are most important (and hence target interventions)
* or adjust parameter distributions to reflect specific interventions and determine consequences
Population Viability Analysis (PVA)
- PVA is a process (Boyce 1992)
– evaluation of data
– modelling of population
– determination of probability of persistence for some arbitrary period into the future
– often relate to conservation interventions - assessing options
- Related to MVP (min. Viable pop.)
– minimum number required to ensure some probability of persistence over a given time horizon - PVA is a process (Boyce 1992)
– evaluation of data
– modelling of population
– determination of probability of persistence for some arbitrary period into the future
– often relate to conservation interventions - assessing options
- Related to MVP (min. Viable pop.)
– minimum number required to ensure some probability of persistence over a given time horizon
PVA models
- formerly often used off-the-shelf software - e.g. VORTEX, RAMAS
- increasingly bespoke
*remains controversial
– e.g. Brook et al. (2000) – 21 long-term data sets half for parameterisation, half for validation 5 PVA packages predictively accurate and in broad agreement
- Coulson et al. (2001) – noted demands on data “The 21 data sets used were the only long- term studies we identifed that presented data of sufficient duration and quality to be suitable for retrospective testing” – Brook et al. (2000) also, that extinctions often result from catastrophes
– but catastrophes are hard to predict
MVPs and guidelines
- Misuse of PVA:
- various papers → Traill et al. (2010) empirical (case-specific) and theoretical (genetic) estimates of MVP
- median estimates consistently in the order of 5,000 individuals – but risks any number below this being dismissed as ‘too likely to go extinct’
see: https://www.americanscientist.org/issues/pub/a-magic-number
Misuse of PVA example
Clements et al 2011
“Practitioners of conservation triage may want to prioritize resources on the Sumatran rhinoceros instead of the Javan rhinoceros (–1.36 vs –2.10, respectively).”
Misuse of PVA is controversial
- 5,000 not a magic number! (Flather et al. 2011a)
- no clear variation between taxa as there is so much variation within taxa
huge variation even in estimates of MVP for a single species
e.g. grizzly: 9 estimates ranging from MVP = 400 to 44,000!
- no clear variation between taxa as there is so much variation within taxa
- generalised MVPs do not account for threats
- genetic evidence for consistency is weak (Flather et al. 2011b)
- why do we even need a guideline for MVP (cf. Caughley’s declining population paradigm)?
comparing relative threat based on distance from a generalised number
- highly unlikely to apply
- dubious enterprise
- does nothing to direct conservation at threats
> 5,000 individuals would write off many conservation projects worldwide
– including the Lord Howe woodhen, generally viewed as a success!
With no signs of inbreeding depression!
PVA key points
- Increasingly carefully tailored to specific situations
– individual based
– Bayesian analyses of parameters to make use of diverse information sources - Recent models have successfully informed management of, e.g.
– disease (e.g. Haydon et al. 2002 feral dog disease transmission reduction by vaccination)
– hunting and conservation interventions (e.g. Fordham et al. 2008 soft shell turtle eggs eaten by pigs)
Summary
- Population models are very widely used in conservation biology
- They can deliver general insights … and insights into specific scenarios
- Perhaps most prominent and useful
– evaluating the impacts of management interventions - Open to misuse and misinterpretation
– so all conservation biologists must understand them and their limitations! - Consider the uses of population modelling throughout the course
– how do we know what we know?
– what role have models played?
Further research recommendations
look for examples of population modelling of:
– species you find interesting
– conservation scenarios
Think about general vs specific insights
Model complexity / simplicity
