In this paper I present a spreadsheet that implements the spatial surveillance prioritization methodology developed by Hauser and McCarthy (2009). They couch surveillance planning within a cost-benefit framework to identify both how much investment is justifiable to detect a weed and how that investment should be distributed across a heterogeneous landscape. The methodology partitions the landscape into homogenous sites with the optimal allocation depending on the probability that the weed is present, the ease of weed detection, and the benefits of weed detection at each site. A surveillance plan can be calculated for an arbitrarily large number of sites, limited only by the spreadsheet's row capacity.
The hunting of geese and other waterfowl is an activity that circulates millions of dollars each year in North America. The Atlantic population of Canada Geese (Branta Canadensis) has historically been a major target for hunters throughout the eastern parts of the United States and Canada, although numbers declined significantly in the 1990s. Resident (non-migratory) populations of geese and migratory populations during the non-breeding season can damage crops and cause public nuisance complaints. Thus, management of goose populations can be a balance between providing high harvest opportunity while not allowing populations to get so large as to cause damage. We investigate optimal control of the Atlantic population of Canada Geese. We seek to maximize harvest, while maintaining the population within acceptable upper and lower bounds. Control is obtained by setting the harvest rate on breeding adult birds each year. The optimal harvest strategy is informed by the population state, and must incorporate a range of uncertainties regarding population dynamics and the ability to control the population. The first uncertainty is environmental variation. This is unpredictable and uncontrollable, and represented by stochasticity in breeding productivity from year to year. The second uncertainty considered in this paper is a limited ability to control the population. Given a finite number of hunters, there may be an upper limit on the total number of birds that can be harvested each year. We explore a range of limits to total annual harvest. Structural uncertainty is the third uncertainty considered. Models constructed to represent population dynamics are not a perfect description of the true dynamics. In particular, there is disagreement about the strength of density dependence underlying Canada Goose dynamics. We pose two reasonable but contrasting models of density dependence in this study. Both models of population dynamics include age structure. Canada Geese, like other goose populations, exhibit life-history attributes that differ by age. Thus, a structured model may be necessary to fully capture the dynamics of this population. Annual harvest decisions are made using the estimated number of birds in each age group. While age-structured harvest has been investigated in the past, the objective has usually been only to maximize yield. In this study we have the additional goal of maintaining population size within set bounds. Stochastic dynamic programming has rarely been used to optimize the harvest of structured populations, probably due to the increased dimension of the state space required to describe population structure. Simulations of the optimal harvest under each model show a range of strategies. Under the density independent model, annual harvest is maximized by holding the population size as close as possible to the upper acceptable limit, while ensuring that stochastic fluctuations rarely exceed this limit. When there is limited control, however, the population is optimally maintained at a lower level, to ensure that it does not grow beyond harvest capacity and continue indefinitely with an unacceptably large abundance. Under the density dependent model, the maximum sustainable yield may be obtained by keeping population size at some level between the minimum and maximum acceptable thresholds. Limits to control do not significantly change this optimal population size, although the amplitude of fluctuations may increase under very limited control. This result depends critically on the fact that the population size that achieves maximum sustainable harvest falls within the desired bounds. If this were not the case, the limits to control could play a more central role. It is clear that the strength of density dependence and constraints on harvest significantly affect the optimal harvest strategy for this population. Model discrimination might be achieved in the long term, while continuing to meet management goals, by adopting an adaptive management strategy.
Abstract Adaptive management is a framework for resolving key uncertainties while managing complex ecological systems. Its use has been prominent in fisheries research and wildlife harvesting; however, its application to other areas of environmental management remains somewhat limited. Indeed, adaptive management has not been used to guide and inform metapopulation restoration, despite considerable uncertainty surrounding such actions. In this study, we determined how best to learn about the colonization rate when managing metapopulations under an adaptive management framework. We developed a mainland–island metapopulation model based on the threatened bay checkerspot butterfly ( Euphydryas editha bayensis ) and assessed three management approaches: adding new patches, adding area to existing patches, and doing nothing. Using stochastic dynamic programming, we found the optimal passive and active adaptive management strategies by monitoring colonization of vacant patches. Under a passive adaptive strategy, increasing patch area was best when the expected colonization rate was below a threshold; otherwise, adding new patches was optimal. Under an active adaptive strategy, it was best to add patches only when we were reasonably confident that the colonization rate was high. This research provides a framework for managing mainland–island metapopulations in the face of uncertainty while learning about the dynamics of these complex systems.
Human perception of plant leaf and flower colour can influence species management. Colour and colour contrast may influence the detectability of invasive or rare species during surveys. Quantitative, repeatable measures of plant colour are required for comparison across studies and generalisation across species. We present a standard method for measuring plant leaf and flower colour traits using images taken with digital cameras. We demonstrate the method by quantifying the colour of and colour difference between the flowers of eleven grassland species near Falls Creek, Australia, as part of an invasive species detection experiment. The reliability of the method was tested by measuring the leaf colour of five residential garden shrub species in Ballarat, Australia using five different types of digital camera. Flowers and leaves had overlapping but distinct colour distributions. Calculated colour differences corresponded well with qualitative comparisons. Estimates of proportional cover of yellow flowers identified using colour measurements correlated well with estimates obtained by measuring and counting individual flowers. Digital SLR and mirrorless cameras were superior to phone cameras and point-and-shoot cameras for producing reliable measurements, particularly under variable lighting conditions. The analysis of digital images taken with digital cameras is a practicable method for quantifying plant flower and leaf colour in the field or lab. Quantitative, repeatable measurements allow for comparisons between species and generalisations across species and studies. This allows plant colour to be related to human perception and preferences and, ultimately, species management.
Abstract We need to monitor wildlife populations to determine whether management goals are achieved and to improve future decisions. Therefore, it is important to evaluate the cost and accuracy of monitoring strategies in the context of management. Using a computer simulation of a harvested population, we tested the relative performance of three survey methods: aerial survey, pellet‐group counts and hunters' observations, to inform about the management of Swedish moose Alces alces populations. Where more than one survey method was used in a single year, we used Bayes' theorem to combine information and estimate population size. We used two measures of performance: the fraction of time in which the population had an ‘undesirable’ size and inter‐annual variation in harvest. Furthermore, we traded these performance measures against their cost. An annual aerial survey was the most costly monitoring method (27,000€) and maintained the population within the desired range 72% of the time. The least expensive monitoring strategy (hunters' observations; 1,600€) maintained the population within a desired range of 66% of the time. A combination of two relatively inexpensive survey methods (i.e. pellet‐group counts and hunters' observations; at an expense of 10,000€) maintained the population within the desired range in 76% of the simulated years. Thus, a combination of annual pellet‐group counts and hunters' observations performed better than annual aerial surveys, but was considerably less expensive. Furthermore, the annual combination of pellet‐group counts and hunters' observations also performed best regarding the inter‐annual harvest variation. Management actions only maintained the population within the desired range 81% of the time, even when population size was observed without error, mainly due to variable net growth rates. In wildlife management systems, where a variety of monitoring methods are used, the overall performance generally improves with monitoring expenditure, but very few studies explicitly account for expenditure. However, our study shows that combinations of inexpensive methods can reduce monitoring costs substantially while yielding an equal or an increased performance.
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