Table 1. Summary of key results.

	Objective 1: Maximize the expected probability of detection	Objective 2: Satisfy a prescribed detection target
Key variables	Budget to fixed cost ratio, B/c	Scaled budget, B′
	Coefficient of variation, θ = σ/µ	Scaled fixed cost, c′
		Target probability of failed detection, Qc
Main results	When the detection rate is highly variable it is optimal to have more, shorter surveys than when it is relatively constant.	The solution is relatively insensitive to changes in the coefficient of variation, θ.
		A tougher management objective (lower Qc) results in fewer, longer surveys.
	For rare or cryptic species it is optimal to have fewer, longer surveys than for common species.
	The analytic approximation derived for each objective function performed well.
Minimum survey effort	When detection rate varies, more effort is required to ensure management objectives are met.
Treefrog surveys (B = 10 hours, Qc = 0.95)	When the expected abundance is1, 3 surveys are optimal.	When the expected abundance is1, 2 surveys are optimal.
	When the expected abundance is3, 4 surveys are optimal.	When the expected abundance is3, 4 surveys are optimal.
	Correlation between time-steps only affected the solution when the correlation coefficient was quite large.
Plant surveys Atriplex semibaccata µ = 0.55, σ = 0.60 Lomandra longifolia µ = 0.56, σ = 0.64	The predicted optimal number of quadrats ranged between 1 (for both species when budget B = 5 and fixed cost c = 1) and 11 quadrats (for L. longifolia when B = 15 and c = 0.25).	The predicted optimal number of quadrats ranged between 1 quadrat (for both species when B = 5) and 16 quadrats (for both species when B = 15 and c = 0.25)
(Qc = 0.95)	The predicted number of quadrats was very close to the empirically-derived optima.	The predicted and observed optimal numbers of quadrats did not correspond as closely but were still strongly correlated.