Area of Inference

Implementation of MSIM across NFS lands requires a system for organizing,

synthesizing and evaluating population and habitat data at multiple scales (i.e., the Forest scale and larger). Above the Forest scale, data points may be variously combined to make inferences about NFS lands within particular geographic areas (e.g., administrative zones, ecoregions, Regions) or ecosystem types (e.g., bottom-land hardwood forests, subalpine ecosystems). Primary areas of interest and inference should be identified in the sampling design phase so that core sampling may be augmented as needed to meet

primary information needs.

Multiple National Forests or larger contextual scales serve an important function in large- scale monitoring programs by providing an ecologically meaningful basis for Forest-scale evaluations. At the scale of an individual Forest, fewer species will be sampled

adequately to determine change with the desired statistical confidence and power. However, status and change will be more precisely described at larger scales for many species because of the larger number of sample points, so they can lend context to less precise estimates of status and change generated at the Forest scale. In particular, the ecoregional scale can serve as a valuable context for interpreting Forest scale patterns of status and change in populations. In addition, species habitat associations are commonly consistent at the scale of ecoregions, so species detections can be used to quantify

geographically specific habitat associations and evaluate indicators of environmental conditions. These are not MSIM objectives per se, but rather they are valuable applications of monitoring data.

Many ecoregion classification schemes currently exist, including schemes that pertain to terrestrial or aquatic ecosystems (e.g., Udvardy 1975, Bailey and Hogg 1986, Omernik 1987, ECOMAP 1993, Maxwell et al. 1995, Ricketts et al. 1999). Few classification schemes are based on wildlife species distributions, such that the biogeography of wildlife species shaped the boundaries of the ecoregions resulting in greater consistency

Multiple Species Inventory and Monitoring Protocol Ch 2: National Framework

in species composition within than among ecoregions. For the purposes of monitoring populations, ecoregional boundaries should encompass entire distributional ranges of many species and ecologically meaningful subsets (e.g., ecosystem types) of more widely distributed species ranges (e.g., black bear, mule deer, American robin).

Several classification schemes currently exist that delineate ecoregional boundaries at various scales, based on a variety of ecological variables: climate, physiography, soils, land use, vegetation and flora/faunal species assemblages (Herbertson 1905, Fenneman 1928, Holdridge 1947, Udvardy 1975, Omernik 1987, Bailey 1995, Olsen et al. 2001). Indeed, monitoring points and sites can be grouped based on any variety of strata, including multiple ecoregional schemes (terrestrial or aquatic) depending on which confers the greatest advantage to the questions being answered.

Three recently developed ecoregional classification systems -- Bailey (1995), Maxwell et al. 1995, and Ricketts et al. (1999) -- stand out as most useful for evaluating population and habitat status and change at the ecoregional scale. Bailey’s hierarchical ecoregions (Bailey 1995) have been adopted for many applications within the U.S. Forest Service (e.g., FIA, Terrestrial Ecological Unit Inventory) (Figure 2.3). Bailey (1995) used primarily single variables to delineate boundaries at each of several ecoregional scales. The classification is based primarily on climatic variables to derive large-scale

boundaries, and vegetation and soil patterns to derive finer-scale boundaries. Bailey’s classification scheme does not take into consideration the biogeography of wildlife species, and the more dissected patterns of the smaller-scale ecoregions may limit their utility for clustering Forests to strengthen Forest-scale inferences. In Bailey’s

classification, there are 19 divisions (Figure 2.3) and 35 provinces in the United States. Divisions are likely to be the most feasible scale in Bailey’s classification for evaluating population and habitat status and change across NFS lands within an ecologically defined region.

An additional classification scheme that may be useful that reflects vertebrate biogeography is the ecoregion classification developed by the World Wildlife Fund (WWF) and National Geographic (Fig. 2.4). WWF ecoregions developed by Ricketts et al. (1999) are intended to represent distinct biotic communities across the globe and to aid in identifying areas of high priority for conservation (Olsen et al. 2001). Ricketts et al. (1999) combined boundaries of multiple variables per scale: they considered faunal and floral species assemblage patterns, as well as geologic history, to establish large-scale boundaries; and they used a combination of land form, land use, vegetation and soil types for delineating finer-scale boundaries. A total of 69 ecoregions are identified in the United States, including Hawaii and Puerto Rico, which reside within 10 biomes: tropical moist broadleaf forests, tropical dry broadleaf forests, temperate broadleaf and mixed forests, temperate coniferous forests, temperate grasslands/savanna/shrub, flooded grasslands, Mediterranean shrub and savanna, xeric shrublands/deserts, boreal forest/taiga, and tundra. The biome level is likely to be the scale at a number of

Multiple Species Inventory and Monitoring Protocol Ch 2: National Framework

Figure 2.3 Bailey’s ecoregions (Bailey 1995).

Multiple Species Inventory and Monitoring Protocol Ch 2: National Framework

National Forests that would be sufficient to interpret population and habitat change on NFS lands with a given ecoregion.

The evaluation of monitoring data for aquatic species is most appropriately assessed using Maxwell et al.’s (1995) aquatic ecosystem hierarchy. The aquatic hierarchy consists of three levels (subzones, regions, and subregions) within the continental United States, which are based primarily on the distribution of fish species. For some groups of aquatic amphibian and reptile species, the ecoregions identified in the aquatic hierarchy may be useful for summarizing population and habitat status and change.

2.6.2 Analysis Techniques

Data analysis is accomplished by a combination of ACCESS, EXCEL, and SAS software programs. Computer code to perform routine data manipulations and conduct basic data summaries for data associated with core and primary protocols will be developed and supported at the national level.

Population Data

Basic data analysis procedures will be developed for each protocol and associated

species. The target population parameter for each species is the proportion of monitoring points occupied. Detection probabilities directly affect the values used to represent population parameters for inventory and monitoring. The National Framework for the MSIM protocol provides guidance on how to derive estimates of the primary population parameters that account for spatial and temporal variation in probability of detection that is likely to result from a number of sources (e.g., climatic influences, observer variability, variation in sampling effort). Parameter estimation also allows for the consideration of environmental covariates that can mask and confound temporal change.

Proportion of points occupied (P) and probability of detection (1-q) estimates will be generated using maximum likelihood estimators for all species with adequate detections (MacKenzie et al. 2002, MacKenzie et al. 2003, MacKenzie et al. 2004). Not all

monitoring points within a Region need to be included in estimates of the proportion of points occupied. The statistical power to detect change declines as the proportion of sites unoccupied increases. Therefore, it is advantageous to eliminate sites from the analysis that have no probability of occupancy. However, it is not recommended to go so far as to eliminate points based on highly specific habitat requirements. The population of points included in each sample period should remain constant, and habitat conditions are subject to change. Geographic ranges can change also, but they are more likely to change

slowly, which can be more easily accommodated in data analysis over time.

For all but aquatic sample sites, detections obtained by conducting the survey methods for MSIM are considered associated with the monitoring point for the purposes of change analysis, and therefore are used to determine presence associated with the point

regardless of whether they were conducted in close proximity to the monitoring point. Aquatic sites selected in association with a given monitoring point (see Chapter 9,

Multiple Species Inventory and Monitoring Protocol Ch 2: National Framework

Vertebrate Monitoring at Aquatic Habitats) can occur anywhere within a large sample unit (e.g., 1500 to 6500 ha subwatersheds as per Maxwell et al. 1995) that will

encompass multiple monitoring points. For the purposes of describing the status and change of individual species, sites may be treated as independent sample sites. However, multiple sites may also be used to describe the status and change of species composition at the subwatershed scale as well. Program PRESENCE developed by USGS Patuxent Wildlife Research Center and available on their website (www.mbr-

pwrc.usgs.gov/software.html) can be used to generate estimates of P and 1-q where data

are collected from one sample site over several visits or are collected across several sample sites during one visit or both.

Population change estimates will be determined using paired comparison techniques, such as McNemar’s test (Zar 1984). Once several sample periods have been completed, trends may be evaluated using linear and non-linear regression techniques. For trends, the slope, intercept, and confidence intervals of trend lines for the duration of the monitoring program can be calculated and used to describe change over time. In addition, sample size analysis may be conducted to evaluate the confidence and power that the existing monitoring program offers for estimating status, change, and trend of various measures (occupancy, abundance, richness), particularly for species of interest or concern.

Habitat Data

Ideally, status and change in habitat conditions are derived from the FIA program and data collected at FIA points. However, a number of factors pose short-term barriers to relying on FIA data for habitat change, namely the 10 to 15 year remeasurement cycle and the limitation of most programs to Phase 2 protocols, which target primarily woody vegetation. Phase 3 protocols target some additional, more detailed measurements of herbaceous vegetation and woody debris that are important habitat variables for many species. As the FIA program is fully implemented, it can serve an increasingly central role in providing habitat data. In the short term, habitat measurements are taken at the MSIM monitoring points that can be used as covariates to improve initial estimates of proportion of points occupied, and can then be used to evaluate the proportion of FIA points potentially occupied as they are sampled each year over time. The need to

remeasure habitat conditions in the subsequent sample periods can be evaluated based on the strength of habitat covariates and the ability of FIA data to adequately describe key habitat variables for priority species.

Data summaries of habitat conditions will consist of simple summary statistics that describe plant species composition and vegetation structure (e.g., tree density by size class, canopy closure, etc). Basic metrics to describe habitat conditions and their derivation will follow FIA procedures. Species-specific habitat parameters may be developed individually or jointly by Regions, and their measurement and description should be described in Regional plans.

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Ancillary Data

MSIM protocol yields ancillary data on population parameters, community ecology, and habitat relationships that has great utility to Forests and Regions. For example, detection data can be used to estimate species richness for each site within or among taxonomic groups (Burnham and Overton 1979, Boulinier et al. 1998). Shifts in species composition within and among sites can provide insights into potential causal factors for observed changes in individual species or groups of species. In addition, abundance estimates and indices may be generated with data from some survey methods. Guidelines for

generating and interpreting these additional population and community metrics from detection/non-detection and abundance data (where applicable) need to be developed. There are many approaches for exploring habitat relationships, and it is recommended that Regions work with Research Stations to develop analysis plans that will address key questions, conduct associated analyses, and interpret management implications. Habitat data collected at MSIM monitoring points rather than FIA point data should be used to build initial habitat relationship models because it is spatially and temporally coincident with plant and animal population data.

Finally, the effects of natural and anthropogenic disturbances can be explored through retrospective analysis of point data. Once a set of sites has been sampled two or more times, points that have experienced disturbances of a given type, such as prescribed burns or thinning, can be analyzed retrospectively to evaluate changes in plant and animal populations. Sites that did not experience any disturbances and that match disturbed sites for key environmental variables can be used as a baseline against which to evaluate changes associated with the disturbance. This represents an exploratory analysis that will generally only be possible at multi-Forest and larger scales. Many different techniques could be useful for such an analysis (e.g., Burnham and Anderson 2002).

2.7 Reporting

Periodic evaluation of monitoring data is a cornerstone of any effective monitoring program, and it is essential to adaptive management. For each year of sampling, a report should be produced that describes the monitoring activity, including the number of points sampled and their identity, survey methods conducted and any aberrancies in

implementation, and a list of species detected at each point. At the end of the sample period, the data should be analyzed and results reported and evaluated within 1 year of completing field data collection. During evaluation, the results of monitoring should be reviewed with respect to checkpoints to provide a context for evaluating institutional performance and management direction. In the second (and subsequent) sample periods, status and change are both reported.

Monitoring points within the geographic range of each species should be determined prior to data analysis. The precision of all estimates will depend on the number of detections and the proportion of monitoring points with detections. For each Forest and Region, the MSIM protocol will then produce an observed and estimated proportion of

Multiple Species Inventory and Monitoring Protocol Ch 2: National Framework

monitoring points occupied and estimated probability of detection for each vertebrate and plant species detected based on the monitoring points within their geographic range. Data can also be compiled across Forests within the same ecoregion (i.e., with the same vegetation series) and then used to generate estimates of proportion of points occupied on NFS lands for the ecoregion. The MSIM protocol will also provide change data on environmental variables (including natural and anthropogenic disturbance) that can be used to make inferences about habitat conditions for a range of species. Habitat relationships can be inferred by exploring patterns of co-occurrence of species and environmental conditions.