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37 Cards in this Set
- Front
- Back
PVA (population viability analysis) |
adaptive framework for incorporating mathematical and statistical models, risk assessment and uncertainty, extinction probability, and management options |
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what is PVA used for? |
to put conservation science and modelling into practice |
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Step one of PVA |
Objective start with general question (eg. what is the best way to protect the eastern ribbon snake in NS) |
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Natural history (of eastern ribbon snake in NS) |
semi-aquatic low movement rate over-wintering |
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Steph 2 of PVA |
model structure |
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Model structure |
choose specific model after youve a good understanding of natural history, available data, and time frame of study (eg. stochastic age structured demographic model) make a list of model assumptions and keep it updated |
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Step 3 of PVA |
Model parameters |
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model parameters |
estimate model parameters from available data (eg. mean and standard deviation of lambda) |
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step 4 of PVA |
build/improve model |
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build/improve model |
run the model many times under different scenarios does it capture population dynamics reasonably well does it need improvement |
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step 5 of PVA |
extinction/ recovery |
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extinction/recovery |
what is the probability of extinction over an appropriate time-scale (eg. a 50% chance of extinction over the next 50 years with no management intervention) |
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step 6 of PVA |
sensitivity analysis |
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sensitivity analysis |
determine which parameters need to be estimated more carefully (eg. model is very sensitive to juvenile mortality rate) if possible acquire more data on the parameters |
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if more data is collected on the parameters |
feedback to step 3 |
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step 7 of PVA |
ranking the options |
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ranking the options |
use a cost-benefit analysis to fin the optimal management solution (eg. a 20% chance of extinction costs $100 000 to implement, a %5 chance of extinction costs $1 million) model and rank each possible scenario explicitly |
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step 8 of PVA |
implementation |
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implemenation |
present management options to stakeholders (eg. government, NGOs, communities) |
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Step 9 of PVA |
monitoring |
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monitoring |
long-term monitoring of management success (eg. annual surveys of snake populations in each county) changes in range size? short-term responses may be transient |
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step 10 of PVA |
evaluation |
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evaluation |
revision of model structure and parameter estimates if necessary (eg. refine estimates of standard deviation of lambda) as you adjust the model, your management plan may change - called adaptive management |
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Example 1 |
African elephants |
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African elephant: a population viability analysis for African elephant: how big should reserves be |
extinction probability calculated by PVA for different sized reserves helps in setting up effective reserves on semi-arid land |
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Example 2 |
Marsh fritillary butterfly |
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Marsh fritillary butterfly |
minimum viable metapopulation size, extinction debt, and the conservation of a declining species |
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Limits of PVA |
data quality a model cannot identify reasons for decline- need to establish these independently errors and uncertainties are magnified with each time step into the future rare or unforseen events can change the populations trajectory socio-economic factors are hard to predict, but often play a key role |
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Summing up PVA |
PVA = adaptive management tool that incorporates population models, sensitivity analysis and management decisions allows assessment of extinction risks over time requires continuous monitoring, updating and re-evaluation modelling populations is only one (important) step towards effective conservation |
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Types of models |
conceptual mathematical statistical |
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conceptual model |
a schematic or verbal description of a process |
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mathematical model |
a formal mathematical representation of a process |
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statistical model |
a mathematical description of a data set |
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what do we do with conservation models |
observing general patterns finding general laws applying tools to specific problems modelling population extinctions harvesting of resources coservation and resource management |
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examples of conservation models |
individual based models reserve-siting models (optimize resources) metapopulation models genetic bottleneck models |
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decision making involves more than a good knowledge of species ecology |
interest groups: socio-economic demands policy makers: political constraints biologists: biological background modellers: quantitative rigour |
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3 golden rules |
always be clear about your question and objective always be aware of assumptions and uncertainties and communicate them together with the results do not allow yourself to get detached from the real world |