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Abstract

  1. Top of page
  2. Abstract
  3. SIMULATION
  4. EMPIRICAL OBSERVATIONS
  5. Acknowledgments
  6. REFERENCES

Managers of wildlife reserves have a range of tools available to them when considering the best way to provide visitor access while avoiding as many of the negative effects of human disturbance as possible. However, managers lack guidelines as to whether conservation interests are best met by spreading visitors thinly throughout a reserve or by aggregating them in a small area. Here I describe how relationships between disturbance impact and disturbance pressure (the dose–response curve) can be used to address this issue. I generate a spatial simulation of two different models of visitor distribution (one more aggregated than the other) and explicitly model disturbance impact for a variety of dose–response curves. I show that the optimal visitor distribution is likely to depend on the sensitivity of the species and the overall visitor pressure. Importantly, I find that in certain circumstances optimal management can shift from one management option to the other if visitor numbers cross a certain threshold. I use published relationships predicting nesting success of Common Guillemots Uria aalge and Black-legged Kittiwakes Rissa tridactyla to assess optimal management at three nature reserves in Scotland. Optimal management for Guillemots depends on the number of people and the distance between the people and the birds. At sites with high disturbance pressures, management should aim to aggregate visitors in as small an area as possible, whereas at sites with lower disturbance pressure, an even distribution of visitors is favoured. Kittiwake models were not generally accurate, and consequently only site-specific guidelines could be generated, where an even distribution was favoured.

Human disturbance is recognized as an important concern in the conservation of many species (de la Torre et al. 2000, Nisbet 2000, Williams et al. 2002). However, providing access to charismatic wildlife is often desirable; it potentially yields conservation revenue (Gray et al. 2003) and also increases the public appreciation of, and support for, conservation (Hendee 1972, Bogner 1998, 1999). This conflict can be managed in several ways. Most guidelines concentrate on managing the distance between wildlife and visitors (Galicia & Baldassarre 1997, Williams et al. 2002, Müllner et al. 2004), but the practicalities of identifying such distances are so complex that their use has been questioned on theoretical and empirical grounds (Gill et al. 2001, Beale & Monaghan 2004a). Other managers limit the number of visitors permitted to enter a reserve each day (e.g. Harris & Wanless 1995), though the effectiveness of this in minimizing disturbance impacts is likely to be small unless total visitor numbers are reduced (Beale & Monaghan 2005).

Instead of restricting access in these ways, Fernández-Juricic et al. (2004) discuss the possibility of manipulating the distribution of visitors within a reserve. This can be achieved relatively simply (e.g. by creating paths or placing information boards), and the use of such methods could result in the increased or decreased aggregation of visitors (Pearce-Higgins & Yalden 1997, Sutherland 2000). Aggregation into a small area is likely to result in locally increased disturbance impacts but allows the rest of the area to remain undisturbed (Pearce-Higgins & Yalden 1997, Pearce-Higgins et al. 2007), and an even spread of visitors ensures that birds in the whole area experience similar low exposure to people. However, although such management is relatively simple and is used in a large number of situations, Fernández-Juricic et al. (2004) reported no studies that dealt with this idea and did not themselves address the question of how to calculate the optimum visitor distribution for a particular species or reserve. With such a small scientific basis upon which to advise managers on visitor access, any additional tools offer important practical advances.

The management of visitor access is particularly important for colonial birds, where large numbers of people visit birds at their nesting grounds (Anderson 1988, Harris & Wanless 1995, Nisbet 2000). In such species, spatial relationships between indices of human disturbance pressure and nesting success have often been published (Nisbet 2000, Beale & Monaghan 2004b). If these dose–response curves are robust, the shape of this relationship may allow empirical assessment of the optimal spatial distribution of visitors and the likely effectiveness of any management implemented. Here, I first formulate a spatially explicit model colony and associated visitor distributions which I use to explore the effects of different dose–response curves on overall visitor impact. I then describe some empirical observations that allow these models to be accurately parameterized and explore the implications for visitor management.

SIMULATION

  1. Top of page
  2. Abstract
  3. SIMULATION
  4. EMPIRICAL OBSERVATIONS
  5. Acknowledgments
  6. REFERENCES

Formulation

There are a limited number of shapes that the relationship between disturbance pressure (visitor number per unit time/distance to birds, following Beale & Monaghan 2004b) and disturbance effects may take: depending on the mechanisms driving the relationship and the overall visitor pressure, visitor impact may have a linear, an exponential, a logarithmic or a sigmoid relationship with actual disturbance pressure (Fig. 1). The effects of management that increases or decreases the degree of visitor aggregation can be explored for each possible dose–response relationship by generating a simple model system (see Supplementary material online). In this system, I assume that a colony consists of 100 evenly distributed nests on a grid of 10 × 10 points, each one unit distance apart, and I distribute visitors either evenly around all four edges of the colony (at a distance of one unit from the nearest birds) or only along one edge (Fig. 2). Thus, the average distance between visitors and nests over the colony as a whole is largely similar in both scenarios (with n = 100, spread visitors are on average 6.82 units from each nest, whilst aggregated visitors are on average 6.79 units away), but the disturbance pressure in certain areas of the colony is markedly different between the two simulations. To keep the simulation simple, I assumed that all visitors were present for the same time period. Total visitor numbers per unit time (n) were allowed to vary between four and 100. To approximate the appropriate shapes of possible dose–response curves I assumed the following dose–response relationships between disturbance pressure (VP) [a compound variable consisting of visitor number per unit time (n)/distance to nest (Beale & Monaghan 2004b)] and disturbance impact (I):

image

Figure 1. Types of dose–response curves described in this paper. Solid line, linear; dotted line, logarithmic; dashed line, exponential; dashed and dotted line, sigmoidal (steepness = 1 in all cases).

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image

Figure 2. The simulated colony and the disturbance pressure on each nest with 100 visitors under (a) a spread visitor scenario and (b) an aggregated visitor scenario. Filled dots are nest locations, with size proportional to disturbance pressure; crosses are visitor locations. For clarity, the 100 visitors in the aggregated scenario are illustrated by only 33 thicker crosses: all analyses used 100 evenly spaced visitors as described in the text.

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(A) linear:

  • I = 0.026 × VP;

(B) exponential:

  • I = 2 × (1/(1 + e(−0.2×steepness×(VP−38.5))));

(C) logarithmic:

  • I = 2 × (1/(1 + e(−0.15×steepness×(VP)))) − 1;

(D) sigmoid

  • I = 1/(1 + e(−0.5×steepness×(VP−step)));

where ‘steepness’ is a parameter adjusting the maximum slope of the curve and ‘step’ the position on the VP axis of the maximum slope in the sigmoid curve. The average impact on each of the 100 nests within the colony can then be estimated by calculating the disturbance pressure at each nest (n/average distance between visitors and focal nest) applying the appropriate dose–response curve to estimate the impact and then averaging over the colony as a whole. By varying the parameters ‘step’ and ‘steepness’ it was also possible to simulate different shapes of the curves. A function to calculate this for all values of n has been provided as supplementary material and can be run in the free software package R (R Development Core Team 2005) downloadable from http://www.r-project.org/. By comparing the average impact under aggregated or spread visitor distributions for each shape of the dose–response curve (i.e. the relationships above between disturbance pressure and disturbance impact) and a sample of ‘steepness’ and ‘step’ parameters, it is a simple matter to assess which management protocol is favoured under what conditions (Fig. 3).

image

Figure 3. Disturbance impact under two scenarios. Solid line, aggregated visitor distribution; dashed line, the spread equivalent. The top row of figures show results when steepness is 1, the bottom row when steepness is 4. Note that when steepness is low, lowest impact is always found in the spread scenario but as steepness increases the preferred management becomes dependent on visitor numbers in sigmoid and logarithmic dose–response curves.

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Results

When steepness is relatively low (in this simulation, below around 2), an even spread of visitors is always favoured irrespective of the shape of the dose–response curve. However, when steepness is higher and the maximum rate of increase in the sigmoid relationship occurs below the mean visitor impact levels, the situation is more complex. In this case, if the dose–response curve is linear or exponential it is still best to ensure that most individuals receive low disturbance pressure by spreading visitors around the reserve. However, in the case of the logarithmic and sigmoid relationships, the optimal distribution depends on the number of people as only nests subjected to very low disturbance levels experience a low impact and low disturbance levels are only achieved at low visitor numbers (when spreading is favoured as above) or through increased aggregation of higher visitor numbers. In practice, it is doubtful if the difference between impacts under the two scenarios when the relationship is logarithmic would result in biologically meaningful differences. It is, however, apparent that the optimal visitor distribution may vary even within a reserve if visitor numbers change significantly.

The cause of this complex pattern of response is the compound nature of the visitor impact parameter, determined by both visitor numbers and the distance between these visitors and the nests. When visitors are spread around the entire reserve, each nest receives a moderate level of visitor disturbance; when visitors are aggregated the range of disturbance levels experienced by individual nests increases. If steepness is low (i.e. if birds show relatively weak disturbance responses or poorly defined threshold tolerances) and visitors are spread around the reserve, each nests receives only moderate disturbance impacts. If aggregation is increased, however, some nests now experience higher disturbance and others lower disturbance than before (though the mean disturbance remains similar), the effects of which depend on the shape of the dose–response curve as described above

EMPIRICAL OBSERVATIONS

  1. Top of page
  2. Abstract
  3. SIMULATION
  4. EMPIRICAL OBSERVATIONS
  5. Acknowledgments
  6. REFERENCES

Methods

In order to explore the results of the simulation in the field, I used a combination of new data on Black-legged Kittiwakes Rissa tridactyla and Common Guillemots Uria aalge from two colonies on the Orkney Islands, northern Scotland, and results from previously published experimental work at St. Abb's Head National Nature Reserve (NNR), Scottish Borders (Beale & Monaghan 2004b). From Beale and Monaghan (2004b) it is possible to estimate the shape of the dose–response curve over measured visitor levels for both seabird species at St. Abb's Head (sigmoid and exponential for Guillemots and Kittiwakes, respectively). Before using the published disturbance relationships to determine visitor management strategies, it is important to assess their generality. Consequently, I first assessed how generally applicable the published dose–response curves are by using them to predict nesting success in two different colonies in Orkney: Mull Head (east Mainland) and Marwick Head (west Mainland). Nesting success of Kittiwakes and Guillemots was measured in 2003 according to Joint Nature Conservation Committee monitoring guidelines (Walsh et al. 1995). Using site visits and photographs of the monitoring plots, I estimated the parameters identified in Beale and Monaghan (2004b) as important in determining nesting success in each species. In June 2004 I measured human visitor patterns in the same way as used to generate the original models. Assessment of visitor numbers was not possible during 2003, but the local recorder was confident that the distribution had varied little between years (D. Paice, pers. com.).

For both colonies, I calculated the average value of each parameter and used these means to estimate the mean nesting success for the monitoring plot. I further ranked the nests within each monitoring plot according to the disturbance pressure (people minutes per hour divided by distance to the nest), and produced separate estimates of nesting success for the top (high disturbance) and bottom (low disturbance) thirds of the ranked list. Where the disturbance pressure was the same for several nests and a division required, I selected nests from the tied rank at random. As there may be considerable variation between years and sites in seabird nesting success (Murphy & Schauer 1994), absolute values of the predictions offer a less stringent test of the relationship than the relative changes in predicted and actual values. Consequently, I assessed relationship accuracy by comparing both absolute precision and the magnitude and direction of changes within each colony.

Results

Guillemot nesting success predicted by the published dose–response curve was remarkably accurate at both Orkney sites, particularly for Marwick Head, though in fact there was relatively little variation in disturbance pressure between the most and least disturbed sites (Fig. 4). The small degree of variation in disturbance pressure resulted in total overlap of 95% confidence intervals within distance classes indicating that these divisions were not statistically different. Nevertheless, the models successfully predicted the observed direction of change in mean nesting success at Marwick Head, despite nesting success increasing with disturbance pressure. This contrary pattern is likely to be due to a negative correlation between disturbance pressure and the number of Guillemots neighbouring the nests (a factor known to reduce nesting success, perhaps due to increased probabilities of eggs being knocked from ledges: Beale & Monaghan 2004b), something not observed at Mull Head (Marwick r2 = −0.226, n = 115, P = 0.015, Mull r2 = 0.149, n = 109, P = 0.123). It seems therefore that the Guillemot dose–response curve is likely to be generally applicable. By contrast, predictions of Kittiwake nesting success based on the models in Beale and Monaghan (2004b) were not able to predict nesting success in the new colonies accurately, so the dose–response curve may be site-specific in this species.

image

Figure 4. Predicted (asterisks) and actual (diamonds) nesting success (± 95% confidence limits) for Guillemots nesting in two Orkney colonies in 2003. Sites are divided into three groups based on the ranked level of disturbance at each nest: high, average or low. Note that predictions accurately mirror direction of change at both sites.

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Given these patterns, it is possible to assess optimal management for Guillemots at each site. Clearly, as predictions for Kittiwakes were unreliable away from St. Abb's Head, it is not possible to provide general management guidelines for this species. At St. Abb's Head, however, an exponential dose–response curve implies that management that aims to spread visitors around the reserve as much as possible will always be the preferred solution. The sigmoid dose–response curve for Guillemots indicates that optimal management is dependent on the position and maximum rate of increase in the dose–response curve relative to the current disturbance pressure at each site. From Beale and Monaghan (2004b) it is evident that the maximum rate of increase occurs at a disturbance pressure of c. 0.62 people minutes/h/m and the current mean at St. Abb's Head is approximately 0.5. Consequently, the current levels at this site fall within the exponential stage of the sigmoid relationship, where an even spread of visitors is again favoured. In Orkney, at Mull Head, mean disturbance pressure was approximately 0.3 people minutes/h/m, and again an even spread of visitors would be optimal. Only at Marwick Head, where mean disturbance pressure reached 2 people minutes/h/m, was increased aggregation optimal.

Discussion

Before discussing in detail the implications of these results it is perhaps interesting to explore the biological mechanisms that can lead to the different dose–response curves discussed above. In the context of the empirical observations described here, it is important to note that although human disturbance does lead to reduced nesting success in Guillemots, most nest failures are not a simple direct consequence of disturbance (Beale & Monaghan 2004b). In this species and at the sites visited, visitors do not flush birds directly from nests (Beale & Monaghan 2004b) and the daily number of nest failures is not related to the daily number of visitors (Beale & Monaghan 2005). The mechanism that leads to failure must be indirect and may relate to physiological costs associated with human disturbance and is discussed in more detail below (Beale & Monaghan 2004b).

In the more general case, it is important to note that an exponential or sigmoid dose–response curve is probably the most appropriate shape of the dose–response curve, although over measured values of visitor pressure linear and exponential may also be possible. If disturbance impact is measured in terms of nest failure, for example, once a nest and any replacement clutches have failed, the impact cannot continue to grow, so a maximum must exist. It is, however, quite possible that the visitor levels necessary to cause total failure of all susceptible nests, or even a slowing in the rate of failures, are practically unobtainable and a linear or exponential dose–response curve is appropriate. This also indicates the most likely biological mechanism that could result in a logarithmic dose–response curve: responses are a linear function of disturbance until all susceptible birds have failed and a plateau is reached. This may be the case if birds have a fixed desertion probability that varies as a linear function of disturbance pressure. More complex are the exponential-type dose–response curves. In this case, birds are relatively unresponsive to small levels of disturbance, but over a certain threshold impacts start to increase dramatically. Such patterns could be the result of purely behavioural processes: if birds assess risk on the basis of a threshold rather than a simple linear increase, an exponential dose–response curve is likely. They could also be caused mechanistically, however, if the proximate cause of nest failure is related to a physiological process such as increased heart rate (Nimon et al. 1995, Wilson & Culik 1995, Beale & Monaghan 2004b). If birds abandon nesting attempts when reserves are depleted below a certain threshold (Coulson & Johnson 1993, Cadiou & Monnat 1996), and the rate of depletion is affected by disturbance pressure (Nimon et al. 1995), birds are unlikely to be affected by disturbance until disturbance pressure exceeds that required to tip the physiological threshold to abandonment. Clearly in the case of a sigmoid relationship, an exponential process and a logarithmic process are combined to give the final result, and mechanisms at each stage are likely to be similar to those described above.

It is important to examine what impact differences between the real world situation and the model system may have on the generality of the results. First, it is obvious that real seabird colonies are not distributed on an evenly spaced grid and are often located in areas where access around the entire periphery of the reserve is not possible. The results of this simulation, however, suggest that what is important is not so much the absolute distribution of either people or birds, but the relative variation in visitor pressure that nests receive under scenarios of increased or decreased spatial aggregation of visitors. Whatever the distribution of nests and visitors, increasing aggregation will increase the range of visitor pressures experienced by nests within the colony, and it was this variation in range that results suggested should lead to a change in management option from always spreading visitors around to increasing aggregation under certain circumstances. The potential for such a threshold to exist in the field therefore is clear. By contrast, the numerical conditions (i.e. the necessary but not sufficient conditions where steepness > 2 and step < 22) that lead to this threshold are strongly dependent on the assumptions made in the simulation. A different form of sigmoid curve and a different range of visitor numbers and pressure would result in significantly different numerical conditions, but no change in the overall rules of thumb. It is clear, for example, that only when the dose–response curve is approximately logarithmic or sigmoid can visitor aggregation be favoured. In practice it seems unlikely that the benefits of aggregation when the dose–response curve is logarithmic are biologically meaningful, so considering only the case of a sigmoid relationship, it can be seen that only when the maximum rate of increase is below the mean visitor levels can aggregation be preferred, but meeting this condition is not sufficient to identify the preferred management as the maximum rate of increase also needs to be known.

In general, results from the simulations suggest that the optimal visitor management depends on the sensitivity of the species, the shape of the dose–response curve and on the levels of visitor disturbance that are likely to be experienced at any site. It is not possible therefore to determine a general rule to explain whether visitors to a nature reserve should be aggregated into one area or spread as thinly as possible across the area as a whole. However, empirical observations of the shape of the dose–response curve, coupled with observations of visitor distributions within a nature reserve, can allow a site- and context-specific solution to be determined. If, however, visitor numbers change substantially from the levels present when this is determined, it is important to reassess this management. Moreover, if visitor pressure is fairly low and the birds being visited are not suspected of being particularly susceptible to disturbance, it is likely that in most cases spreading visitors as thinly as possible is likely to be optimal. In practice, management aimed at aggregating visitors into one small area is, perhaps, more easily achievable than spreading visitors more thinly (Sutherland 2000), and some thought about how this may be achieved is necessary but is impossible without further research into how people behave on nature reserves (e.g. Underhill-Day & Liley 2007). It may be, for example, that visitors aim to spend a fixed period of time in a reserve, in which case building a good network of paths that encourage visitors to keep wondering what is around the corner rather than lingering in one site for the whole time would achieve this end. Until further research on practical ways to manage visitor access is completed, however, such suggestions are mere speculation.

In conclusion, the empirical observations and simulation results suggest that unless a species of seabird (and probably other colonial animals visited during the breeding season) is very strongly sensitive to disturbance or in rare cases where visitor pressure is very high, maintaining as even a spread of visitors as possible is likely to be the most generally preferable visitor management strategy. It is interesting that this contrasts rather markedly with many current visitor management strategies where efforts are made to contain visitors in one small area (Higham 1998, Nisbet 2000). It is also important to note that the reductions in impact that may result from optimal management could be relatively small and potentially not biologically meaningful: a disturbance-induced decrease in overall nesting success of around 10% from the expected nesting success in the absence of visitors was estimated at St. Abb's Head (Beale & Monaghan 2005), so without reducing overall visitor numbers any benefits gained from spreading visitors further around the reserve are likely to be minimal. Therefore, only when disturbance really does have an important impact on demographic processes is any attempt to manage the spatial distribution of visitors likely to have significant conservation benefits.

Acknowledgments

  1. Top of page
  2. Abstract
  3. SIMULATION
  4. EMPIRICAL OBSERVATIONS
  5. Acknowledgments
  6. REFERENCES

I thank the National Trust for Scotland for access to St Abb's Head NNR and particularly to K. Rideout for help with the logistics of working at this site. I thank Orkney Islands Council for access to Mull Head and the RSPB for access to Marwick Head. SNH approved research proposals for working within the NNR. D. Paice and R. Mavor provided valuable data on seabird breeding success in Orkney. Discussions with P. Monaghan, D. Haydon and W. Sutherland helped formulate the ideas in this paper, and the comments from N. Burton and V. Keller helped improve the manuscript. The work was supported by a scholarship from the University of Glasgow during fieldwork for this project.

REFERENCES

  1. Top of page
  2. Abstract
  3. SIMULATION
  4. EMPIRICAL OBSERVATIONS
  5. Acknowledgments
  6. REFERENCES
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