The primary uses of monitoring data in species conservation should be to answer scientific questions or to assess the efficacy of certain management practices (Nichols & Williams 2006). Inferences based on naïve estimates of abundance are likely to impact which steps are taken in terms of interpreting results and applying management action. For example, had we implemented a management practice to improve breeding habitat within agricultural sections in 2005, we would have drawn the wrong conclusion about the impact of management on the average number of birds expected in each section. Likewise, had we not accounted for spatial error we might not have detected changes in sections under management or identified portions of the landscape that warranted further attention. Rather, our habitat effects would have told us that abundance was largely similar across patches and that the expected number of birds within a section was fairly uniform across the landscape. Therefore, in order to make progress in terms of creating new management plans for this species (or for any low density species), a rigorous modelling approach is necessary to make better sense of the data.
Mountain plover abundance
Our estimates of detection rate were, in general, lower than those found elsewhere: 0·94–0·65 in Oklahoma, USA (McConnell et al. 2009), 0·38 in Colorado, USA (Wunder, Knopf & Pague 2003). The overall pattern in our results mirrored those of other surveys designed to increase the number of detections within sampling locations (e.g. McConnell et al. 2009). Other studies of this species have shown that increasing the number of visits to the same site does reduce detection error (Dreitz, Lukacs & Knopf 2006). While such strategies can reduce variation in detection error, this reduction usually comes at the cost of increasing variation in abundance estimates (based on simulations, not shown). The amount of effort invested in revisits could also explain why we found a very weak effect of time on detection error. Dreitz, Lukacs & Knopf (2006) sampled some sites as many as 12 times. In our study, effort remained fixed at three to four visits because of personnel and financial constraints. Tipton, Dreitz & Doherty (2008) found similar results for this species using fixed and comparatively low amounts of sampling effort. Thus, studies designed to monitor low density species should weigh the statistical trade-off between the number of sites and visits, as well as the financial trade-offs between costs associated with increased effort and costs of making decisions based on incorrect inferences (Field et al. 2004; Field, Tyre & Possingham 2005a). Within the limits of our data, our model-based estimates suggested that mountain plover relative abundance was higher in Nebraska than previously suggested in Bly, Snyder & VerCauteren (2008). Furthermore, past claims about the size of this population in Nebraska were probably confounded by chronic undersampling.
It would appear that mountain plover abundance did not respond to the types of agricultural landuse we considered. Our estimated effect of agricultural landuse was similar to that found elsewhere (Dreitz, Lukacs & Knopf 2006; Tipton, Dreitz & Doherty 2008; Tipton, Doherty & Dreitz 2009). There was also a great deal of variability in terms of mean relative abundance across the mountain plover’s range. Our estimates of relative abundance on a per hectare basis were ∼0·20–0·30 birds ha–1. Others have found abundances as high as 1·56 birds ha–1 (assuming 1·6 ha patches) in Colorado (Dreitz, Lukacs & Knopf 2006) and as low as 0·003 birds ha–1 in Oklahoma (McConnell et al. 2009). One possible explanation for this heterogeneity could be related to the methods of sampling and detection correction applied across the various surveys. Our study and that of Dreitz, Lukacs & Knopf (2006) both used a multiple revisit method of sampling and similar statistical methods. Plumb, Knopf & Anderson (2005) and Wunder, Knopf & Pague (2003) used distance sampling which requires a different sampling strategy and different statistical approach. Finally, McConnell et al. (2009) used a point count sampling approach and post hoc detection correction method, and Tipton, Doherty & Dreitz (2009) used a probability based sampling approach and a design based method of deriving abundance. Therefore, comparing multiple studies suggests that we may not clearly know whether abundance differences are attributable to a biological signal or whether these differences are attributable to variation in field and statistical methods.
Without spatially explicit predictions we would have to estimate total mountain plover abundance in Nebraska by assuming that our model could be used to predict abundance at unobserved locations (i.e. extrapolation), which could lead to unreliable estimates. Instead, we estimated the spatial structure of abundance using the data and constructed predictions at unobserved locations between observed locations (i.e. interpolation). In many of the studies previously mentioned, total population estimates were arrived at using extrapolated estimates of density. However, our total population estimates for Nebraska, based on interpolated values, are less than those for Colorado (Wunder, Knopf & Pague 2003; Tipton, Doherty & Dreitz 2009) and Montana (Dinsmore, White & Knopf 2003), and are generally more uncertain. This was to be expected because ignoring spatial autocorrelation can result in biased parameter estimates and misleading inferences (Legendre 1993; Dormann 2007; Beale et al. 2010).
Our study was unable to tease apart the broader reasons for the spatial structuring in our data. Spatial structuring could be driven by either extrinsic (e.g. environmental or geomorphic) factors or intrinsic (e.g. behavioural or phenotypic) factors. Because habitat variables did not adequately account for the variability in our data it is likely that much of the intrinsic and extrinsic structuring was swept into the spatial autocorrelation term. Therefore, it could be that the temporal variation in our spatial predictions was caused by interacting static and dynamic spatial processes. Additionally, including a spatial error term, as we did, does not necessarily mean that we accounted for all of the spatial variation in our data. For instance, Wintle & Bardos (2006) show that including a spatial autocorrelation term in a model reduces residual spatial error in data with intrinsic structuring, but not completely. Thus, it is possible that there may be lingering spatial variation in our data that we did not account for. The way to solve this would be to include additional linear and non-linear spatially indexed covariates in our abundance models and utilize the Bayesian model averaging approach to compare the performance of those additional predictors.
Despite this, we can view our relative spatial predictions as measures of habitat quality if we assume that the number of birds in a section is proportional to the number and quality of habitat patches in a section (Fretwell & Lucas 1970). Treating our corrected mean estimate of relative abundance as a Poisson random variable representing birds per section (our unit of prediction), we could expect to find one to two mountain plovers in each section, but could occasionally find a maximum of five. When we consider the spatial heterogeneity in mountain plover habitat use, as measured by our map, we might expect to see as many as ten birds in some sections and almost none in others. This is interesting biologically, because our mean abundance estimate suggests that, in the absence of a spatial process, mountain plovers should be spacing themselves so that their densities are fairly low (around 1–2 birds per section). This would only work if there were one or two high quality patches in each section. If we drew inferences from only this value we might have expected that an increase in the abundance of mountain plovers would lead to more sections being occupied by individuals. In terms of management, this would be an important aspect of mountain plover biology to understand because it should inform the scale at which management is applied. In the case of mountain plovers, this would suggest that a larger area was necessary to increase the population.
However, this is not what our results indicate. Our spatial predictions show that the number of mountain plovers in some sections increased. We might expect this result if something were improving the number of high quality patches within each section. One potential explanation for this within-section increase could be that mountain plovers were selecting nesting sites in response to nest success (Greenwood 1980). Our modelling results indicated that nest management had little effect on our estimates of relative abundance. This is likely to be due to the fact that few of the sites where nests were marked corresponded to survey locations. However, the survey locations with the highest predicted numbers of mountain plovers in our 2007 map overlap the regions where the highest densities of nests were found between 2004 and 2007 (B. Bly, unpublished data). If we compare the average predicted abundances between sections that contained marked nests (1·51) and those that did not (0·89) we see that plover abundance is slightly higher in the managed sections. Bly, Snyder & VerCauteren (2008) also found that the observed number of mountain plover nests in these sections had increased from 49 nests in 2005 to 112 nests by 2007. Average nest success rates in this region are high (∼75%) and similar between years (B. Bly, M. Post van der Burg, A. Tyre, L. Snyder, J. Jorgensen & T. Vercauteren, unpublished data). However, we are still unsure whether these sections contain some as yet unobserved habitat characteristic that could be attracting breeding plovers, thus giving the impression that the increase in abundance is due to management efforts.
Knowing this information would be particularly useful in a largely homogeneous landscape like our study area. Our analysis suggested that small-scale conservation programmes, which tend to be more expensive in terms of money and labour, might be more beneficial for some subsets of the continental population of mountain plovers. This might be particularly true when preferred habitats, such as prairie dog Cynomys ludovicianus colonies, are nearly absent from the landscape. The population level benefit of management could also vary with regional shifts in preferred tillage techniques.
Variation in the value of management strategies across a species’ range raises questions of where to put conservation effort, rather than how to implement it across the landscape. Conservation practices certainly accrue greater benefit when they are coordinated at larger scales (Kark et al. 2009). The types of conservation practices for migratory species like mountain plovers appear fragmented from one region to another. Federal and regional agencies can make recommendations, but have no regulatory authority to compel state agencies or funding bodies to direct funds to species where the greatest local benefit would accrue. Our study does not provide ‘rules-of-thumb’ for low density population management, but it does provide a modelling framework to study spatial variation that can be used to target management across the landscape. Considering the variability in approaches and methods for surveys and analysis of population trends, we suggest that a more synthetic study of these methodologies is warranted.