Methods for Analyzing Status of Pollinators
The status of pollinator populations and assemblages can be assessed in many ways, both direct and indirect. Because a decline is a decrease in abundance over time, long-term surveys are the most valuable indicators for assessing pollinator status. The inherent year-to-year variability of pollinator populations, however, makes trend analysis difficult. Roubik (2001) surveyed various studies of the population dynamics of 59 bee species—short-term projects (2–4 years) and longer studies (17–21 years, all tropical)—and reported that the species’ mean abundances had varied by factors of 2.06 for temperate bees and 2.16 for tropical bees (see Appendix F). Because the natural variability of populations can lead them to halve or double the average in 1-year intervals, reliable population trends cannot be determined from short-term studies, and even trends from long-term studies might not be definitive.
Although direct monitoring of natural populations is invaluable for identification of pollinator population trends, the ease of use and the accuracy of monitoring techniques differ among species. Some of the best examples of large-scale, long-term monitoring involve birds. Banding records from research stations and the annual Audubon Christmas Bird Counts have combined to provide more than a century’s worth of census data on the birds of North America (http://www.audubon.org/bird/cbc/). Focused research on a handful of species of particular interest—such as monarch butterflies or specific threatened or model species, for example—also can provide an accurate picture of population trends (for example, Ehrlich and Hanski, 2004).
Visual counts can be used effectively for direct assessment of abundance for vertebrate pollinators, such as bats and hummingbirds. Mist nets can also be used to capture these flying vertebrates, and their density can be accurately estimated because the individuals are large enough to be banded or marked by some other method. Direct assessment of insect pollinator abundance is typically conducted by observation at the flowers they visit. Pollinating insects are much more conspicuous when visiting flowers than they are in transit among foraging areas or when engaged in other activities; for example, ground-nesting bees’ nests can be difficult to find. One method is to count all visitors to a set number of flowers, generally of a single species, during a specified period (typically 10 minutes), when meteorological observations are simultaneously recorded (Kearns and Inouye, 1993). In addition to providing an estimate of pollinator visitation rate, this method allows a snapshot assessment of the dependence of insect activity on environmental factors, such as temperature, humidity, wind, and light (for example, McCall and Primack, 1992). However, one problem with assessing pollinator populations based on flower visits is that floral abundance and diversity often vary greatly as well.
An alternative method for estimating insect pollinator abundance involves counting or collecting individuals along a transect (for example, 1m 25 m, or 1 m for a fixed period), as in a Pollard walk census for butterflies (Caldas and Robbins, 2003), or in a recent survey of bumble bee populations (Knop et al., 2006). Bees that are not collected can be captured, marked, and released to distinguish individuals and prevent redundant counting (Hines and Hendrix, 2005). Netting at flowers along transects in permanent, one-hectare plots also has been used for native bees (Cane et al., 2000). Insects that readily adopt artificial nest sites—such as nest boxes for bumble bees or trap nests for solitary bees—can be monitored by placing the nests in appropriate habitats. A disadvantage of this method is that adoption rates can be low (as is often the case for bumble bees; Inouye, unpublished). Pollinators that can be manipulated by reward—euglossine bees are attracted to terpene-soaked blotter paper (Dodson et al., 1969; Roubik, 1989; Roubik and Hanson, 2004), hummingbirds will consume artificial nectar from feeders, moths fly to traps baited with fermenting fruit—are more easily monitored than are those that cannot be reliably attracted to a particular location.
Passive traps that collect insects indiscriminately are not always suitable. Entomologists have long used Malaise traps (screen tents that catch insects and funnel them up into a collecting head) because they work well for many kinds of flies. However, the traps rarely capture butterflies, moths, or bumble bees. The selectivity of pan traps for bees depends on the use of appropriate combinations of trap size, color, and number (S. Droege, Patuxent Wildlife Research Center, presentation to the committee, October 18, 2005),
and pan traps can be used to provide reliable population estimates (Russell et al., 2005). Light traps, which attract insects with mercury vapor lamps (visible and ultraviolet light) or various short- and long-wave ultraviolet lights, are also used to sample insect pollinators (some nocturnal bees, beetles, flies, wasps, moths) in diverse temperate and tropical habitats.
Because direct long-term monitoring studies are so rare, population patterns over time must be ascertained in other ways. Biological databases—taxonomic and genomic databases and information collected from conservation-related enterprises—can often be mined for data on historic patterns of pollinator distribution and, in some cases, abundance.
Specimen databases contain the information associated with vouchers in museum collections. At a miminum, the records show when, where, how, and by whom particular specimens were collected as well as their presumptive identifications. Specimen databases also can hold information about field observations. Museums capture information associated with the specimens in their collections, usually processing information on the best known and most widely studied groups first and then moving on to groups that are less well characterized. It is so widely assumed that mammal and bird species have been described that discovery of an undescribed primate genus (Rungwecebus) makes worldwide headlines (Davenport et al., 2006). In contrast, some groups with greater relevance to pollination, such as flies, are so diverse that experts cannot even venture a guess as to what proportion of genera remain to be described.
Specimen databases may be accessible online, and software applications grant access to all available databases together. The Global Biodiversity Information Facility (GBIF) provides a single interface that queries all online specimen databases that conform to community standards and protocols. The GBIF portal provides access to 90 million records from more than 700 collections (http://www.europe.gbif.net/portal/index.jsp). Sample queries for the honey bee returned 6,362 records from 9 data providers; most records were from Costa Rica (INBio, 5,920 records). The ruby-throated hummingbird, in contrast, returned 15,912 records from 9 data providers. Although specimen databases are optimal sources for trend information, few museum collections have digital databases of their specimen holdings, particularly of insects.
Character databases document the characteristics that taxonomists use to distinguish groups of organisms, primarily for specialists. The information also can be used to construct interactive identification aids for parataxonomists and citizen-scientists. There are three principal character databases: MorphBank (http://www.morphbank.net/) and MorphoBank (http://www.morphobank.org/)
contain morphological characters, and GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) has DNA sequence characters.
Nomenclatural databases provide information and documentation on the scientific names of organisms. They provide the correct (valid) names for species so users have the appropriate search terms for queries in other databases. Like specimen databases, the number of nomenclature databases for particular groups and areas is increasing, and so is the number of software applications that consolidate or provide access to them. The Catalogue of Life, through its annual checklist, provides minimal information on more than a half-million species; all of that information is integrated with the services of GBIF as part of the Electronic Catalogue of Life Names of Known Organisms. The Universal Biological Indexer and Organizer contains approximately 5 million names; the Taxonomic Search Engine searches all major nomenclators; and the Integrated Taxonomic Information System provides the official taxonomy of living organisms for the United States, Canada, and Mexico. Some specialized databases that include pollinator data are the BioSystematic Database of World Diptera and the Hymenoptera Name Server.
Species databases provide information and documentation on organisms. Unfortunately, a comprehensive database does not yet exist, although ultimately, species databases will be transformed into the envisioned Electronic Encyclopedia of Life (Wilson, 2003). Species databases sort information by attributes, such as the pollinators of a given plant, and they provide summaries about species or links to species web pages.
Literature databases compile published information and they comprise the same sources used generally for the biological sciences. Literature databases range from general commercial compilations, such as Biological Abstracts and the Zoological Record, to specialized research databases, such as AnimalBase, which links digital versions of the early zoological literature to personally maintained, but publicly accessible databases. One example is the Pollination Biology Database, maintained by David Inouye at the University of Maryland.
Conservation-oriented databases exist to track and monitor putatively threatened populations of animals and plants. Among them are the Nature-Serve Explorer and the Heritage Program network; the federal database of species protected by the U.S. Endangered Species Act; and international lists, such as those from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (www.cites.org/) and the World Conservation Union (IUCN; www.redlist.org/). The Heritage Program and NatureServe tracking systems provide a first step in understanding patterns of decline.
Numerous sources of data—including museum collections, naturalists’ observations, and accounts published in peer-reviewed literature—contrib-
ute to formal rankings of rarity and abundance making them dynamic and subject to continuing input. In the United States, the Heritage Program has established a global rank system that denotes global, regional, and state-specific rarity. The rank of G1, for example, denotes fewer than five occurrences of a given species or community globally; G2 and G3 represents 6–20 occurrences and 21–100 occurrences, respectively; G4 and G5 denotes apparently abundant globally and demonstrably widely abundant species globally, repsectively. The state rankings within the United States are equivalent: S1–S5 parallels G1–G5. Those data are available to the public through NatureServe.org.