A REVIEW OF THE POPULATION ESTIMATION APPROACH OF THE NORTH AMERICAN LANDBIRD CONSERVATION PLAN

As part of their development of a continental plan for monitoring landbirds (Rich et al. 2004), Partners in Flight (PIF) applied a new method to make preliminary estimates of population size for all 448 species of landbirds present in the continental United States and Canada (Table 1). Estimation of the global population size of North American landbirds was intended to (1) identify the degree of vulnerability of each species, (2) provide estimates of the current population size for each species, and (3) provide a starting point for estimating population sizes in states, provinces, territories, and Bird Conservation Regions (Rich et al. 2004). A method proposed by Rosenberg and Blancher (2005) was used to derive population estimates from available survey data. To enhance the credibility of these estimates, PIF organized a review of the methodology used to estimate North American landbird population sizes. A planning committee selected members from the ornithological and biometrical communities (hereafter “the panel”), with the aim of selecting individuals from academia, state natural-resource agencies, and the U.S. and Canadian federal governments, including the Canadian Wildlife Service, the U.S. Geological Survey, and the U.S. Department of Agriculture Forest Service. The panel addressed three questions: (1) Were the methods of population estimation proposed by PIF reasonable? (2) What actions could be taken to improve the data or analyses on which the PIF population estimates were based? and (3) How should the PIF population estimates be interpreted? Collecting reliable, useable information on the status of bird populations is a critical step in developing and updating bird conservation plans. Such efforts often involve setting population goals, using models to predict changes in bird population size as a function of habitat (and other) variables, developing plans to modify habitats through management, and using survey data to monitor progress toward goals. Unfortunately, integrating our present sources of information on bird populations into this system is complicated by the nature of the data collected by surveys; most large-scale surveys collect indices of population size rather than unbiased estimates of population size. An index is a statistic (e.g., point count or relative abundance measure) that is assumed to be correlated with the actual quantity of interest (e.g., population size or density). Understanding the relationship between counts and population sizes at sample sites by estimating the proportion of animals counted (detection rate) has been an important focus of wildlife statistics (e.g., Nichols et al. 2000, Buckland et al. 2001). For bird surveys, indices often are not based on probabilistic samples, which introduces an additional source of uncertainty (e.g., count locations may not sample all possible locations representatively). For the North American Breeding Bird Survey (BBS), for example, data from roadside point counts are frequently criticized because they may be poor indices of the number of birds at count locations and may not be representative of bird populations within regions because of the nature of roadside counts (Bibby et al. 1992). Historically, these factors were often ignored in analyses that made strong but unstated assumptions about the consistency of indices and randomness of samples. Modern analyses of BBS indices attempt to limit the influence of inconsistent indices by controlling for site-specific differences in detection (e.g., through observable covariates; Link and Sauer 1998); no analyses presently control for the roadside nature of the sample. Comparisons of indices of abundance among species also may be flawed if species differ in their detectability; and if detection rates also differ among habitats, use of survey data in bird-habitat models used in developing population and habitat objectives may be invalid. Each of these difficulties in the use of index data can potentially result in inappropriate conservation decisions. Any analysis of index data thus can be criticized by postulating differences in detection rates among treatments. These concerns have motivated conservationists to avoid direct use of relative abundance indices in conservation planning, and instead to include in their plans estimates of species-specific population sizes that incorporate estimates of detection rates. These population size estimates are then used in models as parts of objective functions for setting goals for the number of birds in relation to available habitat, or used to predict the total amount of habitat that must be conserved or created to support species-specific numerical population goals. This interest in making estimates of population numbers and density by habitat has previously led to development of several national and continental estimates of bird population sizes. For instance, McAtee (1931) estimated that there were 2.6 billion breeding landbirds in the contiguous United States, and Wing (1956) estimated that 5.6 billion birds were present in the United States in summer and 3.75 billion in winter. The American Ornithologists' Union (1975) once suggested that as many as 10 billion birds were present in the contiguous United States in each breeding season, with a fall population of 20 billion. Estimates of population size as opposed to indices may be especially useful in conservation because they resonate with the public; they impart meaning not generally found in indices, contributing to impressive statements about the magnitude of conservation problems. For instance, the National Audubon Society (1997) estimated that 100 million birds per year are killed by free-roaming cats (Felis catus); this number alone imparts an importance to the issue not found by suggesting, for instance, that cats kill the equivalent of one bird per highway kilometer per day (Lepczyk et al. 2004). Among the various other sources of bird mortality in North America, total anthropogenic sources of mortality have been estimated to be 400–1,600 million birds killed per year (Table 2). Conservation plans for various species often include numerical population objectives. For instance, the 1986 North American Waterfowl Management Plan (Canadian Wildlife Service and U.S. Fish and Wildlife Service 1986) advocated a goal of a continental breeding population of 62 million ducks during years with average environmental conditions, a number expected to support a fall flight of 100 million birds, and it is these numerical population objectives that have been credited for much of the success of the plan (Donovan et al. 1999). To date, however, the few estimates of total bird populations for any geographic region have been highly speculative and variable. Rich et al. (2004, appendix B) and Rosenberg and Blancher (2005) described their method for estimating species-specific population size from survey data (hereafter referred to as the “Rosenberg and Blancher” approach). Two procedures were used, one for birds largely restricted to the United States and Canada south of the Arctic and the other for birds present in the Canadian Arctic. Survey data available for estimating population sizes for these two areas differ in several important aspects. For the United States and sub-Arctic Canada, North American BBS data were used, whereas for the Canadian Arctic, data from the Breeding Bird Census (BBC) and Northwest Territories-Nunavut Bird Checklist Survey (Checklist) were used. For the BBS data, counts from acceptable routes were averaged for the 1990s for each species recorded on a route. For regions where BBS routes were infrequently run (boreal forest portions of Canada), routes from other decades were included. Numbers of birds by species were averaged for every route in geopolitical regions formed by the intersection of state-province-territory and Bird Conservation Region boundaries. Averages from neighboring regions were assigned if the geopolitical region was not sampled by BBS. Averages from each geopolitical region were divided by the area presumed to be covered by a BBS route (25.1 km2) and multiplied by the area of the region. Bird Conservation Region indices were calculated by summing over all geopolitical regions within a Bird Conservation Region. These Bird Conservation Region-wide indices were converted to population estimates after multiplying the indices by three adjustments (Rosenberg and Blancher's adjustment factors). where Y is a BBS count reported for route j in year i for a particular species, n is the number of years of acceptable route counts during 1990–1999 (≤10), m is the number of routes in the geopolitical region, g is the number of geographic strata, area is the area of the geopolitical region, de is the effective detection distance, countpeak is a smoothed estimate of the maximum count, derived from a sixth-order polynomial fit, X is a BBS count summed over each stop in the mid-1990s through 2001 period, and rte*yrs is the number of routes over the period. For birds occupying three ecozones in Arctic Canada, BBC data provided estimates of total landbird density. Total landbird density was split among three classes of landbirds on the basis of their likely detection distance: near, intermediate, and far. Relative species-specific abundance was calculated from Checklist data. The ratio of BBC total landbird density to Checklist abundance was calculated, and this density conversion factor was applied to the Checklist abundance data to provide species-specific density estimates. These bird densities were averaged within each ecoregion, then multiplied by the area of the region to derive a population estimate. Population estimates were summed across ecoregions to provide a total population estimate for each Arctic landbird species. The two estimates were summed for those species that occurred in both Arctic and non-Arctic regions to derive continental estimates of population size. Global population sizes were simply the United States-Canada population size multiplied by the ratio of the total breeding range to that in the United States and Canada. Rosenberg and Blancher (2005) identified four assumptions in their analysis: (1) each habitat type was sampled in approximate proportion to its occurrence in each region, (2) birds present but not counted during the BBS were accounted for by one or more of the adjustment factors, (3) the Checklist-BBC data for birds in Arctic Canada were comparable to BBS data, and (4) breeding densities in the United States and Canada were comparable to densities outside the United States and Canada (this latter assumption was relevant only to the extrapolation of North American population size to global estimates of population size). The panel focused primarily on evaluating the first two assumptions, because they apparently have the greatest effect on most population size estimates. Two issues, placement of routes and roadside effects, are critical in determining the correctness of the assumption that habitat was sampled by the BBS in approximate proportion to its occurrence in the regional landscape. Intensity and placement of BBS routes dictate whether the habitat was properly sampled. Unfortunately, BBS coverage is limited both by routes that are infrequently surveyed and by large roadless areas that are not sampled within the United States and Canada (Peterjohn 1994, O'Connor et al. 2000). These gaps in coverage may lead to over- or undersampling of particular habitats poorly represented along roadsides. For instance, mountaintops, western riparian areas, and large wetlands are often poorly represented in the BBS (Robbins et al. 1986). Unpublished studies by the late R. J. O'Connor (University of Maine, Orono) and colleagues, C. Flather (U.S. Department of Agriculture Forest Service), and P. J. Blancher (Canadian Wildlife Service) suggest that the effect of atypical route placement is minor, but no of this effect has been et al. 2000). the BBS is a roadside is the influence of on of bird abundance through their effect on the habitat or on bird that of the United States is by along may not be representative of roadless habitat et al. and may support a proportion of a population than in roadless birds may be to with whereas may be and 2000). is these in BBS data et al. and and The assumption of the population estimation was that birds present but not counted during the BBS were accounted for by one or more of the adjustment As by Link and Sauer a or a area of Any of the BBS index to a population size must for these the most important issue by the panel was the credibility of the assumptions in the proposed by Rosenberg and Blancher in these factors, and a of that could influence the and of these also identify the importance of estimating of both the and population sizes. The first adjustment factor was a adjustment the adjustment multiplied the index number of birds per route in the 1990s by geopolitical by on the assumption that only one of a was on a BBS route. This adjustment factor not for and largely and for instance, that as many as of the at their were and species, and species, and are poorly counted by the BBS (Robbins et al. O'Connor et al. may not be accounted for by by it is a species not covered by the estimates for the for instance, suggested that as many as of the birds in a population were Unfortunately, the relationship of birds to birds in BBS is generally the adjustment may be result in an for species, but also may species and those species in which individuals of both The adjustment factor was intended to species-specific detection by each species to one of and in the index of relative abundance into a density estimate. The PIF ratio a species-specific of detection based in part on between a bird and the The effect of a adjustment factor is to or the effective area to which the index is the BBS relative rather than abundance data, a assumption in this is that this index is to population size. The panel not detection for species but suggested that this adjustment may be the most important the most of the adjustment For instance, a detection distance, in the population with a detection of For species detection was m and of the and their estimates were to by this adjustment For of the species their population size estimate was by over the their estimate and of the species their estimate Estimates for of the species no from the species-specific can potentially influence the of the The on factors detection is (e.g., Buckland et al. 2001). detection not only by species but also by habitat, of of and et al. 2000, For the PIF estimates, a estimate of detection for each species was no attempt was made to with these The BBS to which counts are this by detection are not likely to be et al. 2000, et al. et al. in detection may lead to over- or population sizes et al. 2001). Unfortunately, few data for estimation of detection from BBS data. be useful to incorporate the uncertainty with these into the of the population estimates. The adjustment a was used to in the route is This adjustment factor was estimated for each species by polynomial to of stop where the first stop the count and the stop the a polynomial to these counts the in however, in using is that they may be at their because of a of data to the of the for species in abundance (e.g., or late may not be properly from these potentially poorly may be 1). in influence the maximum count species and are especially by this adjustment their estimate of abundance by between and (Table As with other sources of in it be useful to incorporate uncertainty in estimation of the rates in the population estimate. for the counts in the 1990s may be based on from to 10 derived from only a few years are than based on more may not be representative of the within the because they may be for instance, by or An additional is that may be over and Sauer a factor that can be in more analyses and Sauer is also the methodology used to counts from decades for sampled areas in Canada. may be important in the that most BBS routes are in the of this large because it is the estimates are to the many assumptions in the suggest that a be used to of in the A analysis identify those of the index and adjustment that are most with to or in the assumed The adjustment factor for detection has the for the greatest effect on population size estimates. estimates should be by estimates of analysis could identify for the population estimates, which to are only The Rosenberg and Blancher is based on adjustments of more sources of could be used to incorporate uncertainty in the estimates. For instance, for direct estimation of that are for and (e.g., Link and Sauer et al. 2004), and the adjustments used in the Rosenberg and Blancher (2005) population estimates. For instance, for geopolitical regions not sampled by BBS the in counts may be a useful of estimating expected counts for areas in which they are The present for population estimation from BBS data is limited by uncertainty in the magnitude and of the adjustments for data on detection estimates be to the panel studies to for estimating in the BBS. estimates the panel that efforts to the estimates be including a survey of and of current to identify available information about species-specific detection and the on of habitat, of of and other particular is the of gaps in our and The number of species for which detection are available is but from the if they not for because of may be than the current detection Collecting data on detection is the to improve our of and in rates. As of these data in the BBS and other surveys is a for the of the population size estimates. The of the adjustment used in the population estimation among species, differences in and The panel and of the adjustment the of current data, studies based on surveys of populations may be the only to estimates of of polynomial for maximum counts for the adjustment should be as an average of the several and or more as estimation of the that could influence counts should be in the several have suggested that between rates and population density can indices to abundance (e.g., and review of for studies be These studies may be relevant to polynomial in or the adjustment may not in the or the adjustment from adjustment factors that may be for those species that are or or late or have an adjustment be to BBS counts with to they as to the estimates for or of of and studies of changes as a function of to these Estimates of population sizes for each of the three ecozones in Arctic Canada should be with the methodology used by PIF for estimating population size of landbirds in the Arctic use of most of the data that are available for the region, suggest that the estimates based on those data are to because BBC and Checklist sites are not selected and to be in areas of bird The assumption that Checklist-BBC estimates are comparable to BBS estimates likely not suggest that the total amount of useable by birds should be used as the area for which estimates of populations are that large areas in the Arctic are covered by or are of of the uncertainty with the by Rosenberg and Blancher (2005) is a result of using the data for a for which they were not The BBS was not to provide continental estimates of population sizes but rather to monitor in To these it be to the population estimates as new information detection rates and other factors available from concerns about the of roadside habitats and of data from outside the BBS survey area can be addressed only with additional survey information and procedures as et al. methods et al. and methods and Nichols direct estimation of detection rates from point of these to the BBS and other surveys provide data on effects, habitat and effective detection and direct estimation of many of the that are presently or poorly estimated in the Rosenberg and Blancher The panel that be to collect this studies of along BBS routes be studies on of species of particular interest that have large factors, as and are Population estimates are most they are based on limited or on data as the of data for birds in Arctic Canada and that global may be most at et al. development of surveys that provide information on bird populations be useful in both present and changes in this important region. of models is an of development of population estimates. Population estimates be most useful for conservation planning if they are to habitat and other goals. studies are using BBS data to models bird populations with habitat and other environmental (e.g., et al. 2004), and more use of these models in bird conservation planning may several of the with population For example, of roadside habitats can be addressed by of data in habitats or by bird habitat using data and using the models to predict in can then be used to and the additional data example, counts made from can be used to improve These efforts geographic information on habitats, other environmental that influence bird and BBS data. can for controlling of population in the estimation of of species-specific population estimates as they are made is that populations are assumed to be Sauer et al. however, that of the species surveyed in the BBS during and bird populations are not in many are in the panel a population estimation that in are for their to estimate population and Sauer also a for population sizes across and for species. The most is estimates of density for each species. the models estimating population size for each state-province-territory Bird Conservation Region be These models may be to identify factors and could be used to numerical population objectives. models with estimates of may of population size in to proposed of The assumption of the population estimation is that densities of birds are in North and This assumption because differences in density for species primarily outside the United States and Canada may in the estimated global population size for a species. into densities of North American species and is The panel that extrapolation to areas outside North is and not relevant to PIF goals. Rich et al. current bird populations are estimated using the procedures in this but goals are often in of estimation of population the of bird conservation are in of and of habitats, and in populations are based on models numbers of birds as a function of these numbers are not comparable to the estimated or the habitat goals by bird conservation this of a of population estimates, and habitat goals as a in the As to models that estimates, and population estimates, and suggest that these be for in population estimates between species, especially for the of this uncertainty in population size to with determining whether species have a particular conservation as in the North American Conservation the of with the population estimates enhance the of the estimates, and most of our our that of the present is complicated by the present of estimates. of the estimates must also be to in bird Bird populations are and estimates must the of population the panel P. J. Blancher and Rosenberg for their progress with the of estimating the population sizes of North American As they with the estimates, and of the estimates should involve a of their The panel a of of the present with and to population estimates with bird conservation and The PIF Population Estimation for selecting the review panel was of P. J. and P. (U.S. Geological Survey and and for for J. for of the review for at the and P. J. Blancher and Rich for in J. C. and R. on an of this of is after the first the assumption that stop number is a for of sixth-order were to counts across all routes to identify a the for the maximum count was whereas the maximum from the polynomial stop was The maximum for was were from P. J. Blancher and Rosenberg Estimates of population size for 10 species of birds in the United States and Canada and as estimated by Rich et al. to the Estimates of population size for 10 species of birds in the United States and Canada and as estimated by Rich et al. to the sources of mortality with in North limited to in of estimated magnitude sources of mortality with in North limited to in of estimated magnitude of detection used in population estimates from North American Breeding Bird Survey data of detection used in population estimates from North American Breeding Bird Survey data of adjustment factors used in population estimates from North American Breeding Bird Survey data of adjustment factors used in population estimates from North American Breeding Bird Survey data

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