Principle - Ecological

Criterion- Landscape patterns support native populations

Indicator –– Level of Fragmentation and Connectedness of Forest Ecosystem Components.

Box B: Definition:

CCFM Definition: - "When ecosystem components become separated in time and space, the integrity of the ecosystem is challenged. This fragmentation can affect critical connections within an ecosystem."

One of the concerns about intensive forest management is that harvest practices fragment the landscape into patches of habitat that are unusable for individual organisms or sub-populations. Connectivity refers to the arrangement of patches on the landscape and the ability of organisms to use those patches (see reviews by Lindenmayer, 1994 and Simberloff et al., 1992). If a given species of wildlife cannot travel between forest patches, then those patches are considered disconnected. Since many organisms use a variety of patches on the landscape, maintaining connectivity between them is considered essential.

Box D: Applicability to Different Landowners. Explain any differences:

This would apply to all landowners with aims to conserve ecological connectivity of the landscape. Connectivity is vital to conservation of populations.

Box E: Overlap:

CIFOR: 2.3.2 Corridors of Unlogged Forest Are Retained

GFE: 15. Road densities should be minimized

Box F: Geo-Political Scale:

Notes: The issue of connectivity is multi-scaled, applying to different scales depending on the mobility of individual organisms. For example, some birds and bats migrate through North America and beyond to South America and Asia. Wolves and Grizzly bears have been shown to disperse throughout the intermountain west. Pine marten move between parches of mature confer habitat at the forest management unit. Thus all spatial scales apply. Connectivity has been best studied at the level of study area and smaller. Certainly connectivity can be accounted for in the management of these scales. It is more difficult at regional and continental scales, but some groups are attempting to do so (i.e. Project Wildlands – wildlandsproject.org/htm/ summary .htm).

However it appears that connectivity does not apply equally to all scales. The work of Keitt et al. (1977) demonstrates that landscape connectivity for species is highly scale-dependent. They concluded that connectivity does not increase gradually with increasing scale, but rather undergoes a distinct transition. For the forest habitat distribution in the US southwest, the transition occurred at a threshold distance of approximately 30 miles or, in the case of dispersal probabilities, an average dispersal distance of ~ 10 miles. Thus, for an organism to perceive the habitat distribution as a single, large, interconnected cluster, it must, in general, be capable of dispersing a distance of ~ 30 miles over inhospitable habitat and must have an average dispersal distance of at least 10 miles. Thus it appear that this indicator applies best at the forest management unit. In general patterns, patterns of land ownership in the North America would require some level of cooperative planning to ensure connectivity.

Notes: There is little information for forestry that gives predictive value to a particular measure of connectivity or fragmentation. The relationship between fragmentation and population variables has been demonstrated for many species. However such relationships are highly specific to both species and area. There is not a generalized model that is applicable across a range of species.

Using percolation theory, Keitt et al. (1977) demonstrated that habitat loss has a highly scale-dependent effect on landscape connectivity. For organisms that perceive the landscape at fine scales, landscape configuration and stepping stone patches are of little consequence, because populations are restricted to local habitat patches. Similarly, movements of species capable of long-range dispersal will not be strongly influenced by the configuration of individual patches. However, for species near a theoretical threshold (percolation transition), landscape configuration may play a significant role in determining landscape connectivity. Near the percolation transition, individual patches can act as corridors or stepping stones, bridging gaps in the habitat distribution. Thus, we expect that landscape configuration will be important to species whose dispersal behavior places them near the landscape percolation threshold.

There are also population viability models that are spatially explicit (Akçakaya et al. 1995a) and can have predictive value. These model incorporate age-specific mortality, natality, immigration and emigration values with geographic information systems. The model provides a probability of survivorship for a given species in a given landscape.

Notes: This is a structural measure of the landscape. The key function is connectivity. The debate over this indicator concerns the relation of structure to function.

Box I: Underlying Concepts:

Fragmentation has been called the greatest worldwide threat to forest wildlife (Rosenburg and Raphael, 1986) and the primary cause of species extinction (Wilcox and Murphy, 1985). Connectivity of landscapes depends on the spatial distribution of habitats across a landscape as well as the scale at which organisms interact with landscape pattern (Merriam 1984, Noss 1991). The extent to which corridors are actually used by animals is influenced by a number of inter-acting factors (Lindenmayer, 1994a) including:

1. The particular species targeted for conservation;

2. The attributes of the corridors themselves, such as width and length and vegetation cover;

3. The suitability of habitat in the area surrounding corridors;

4. The spatial location of corridors in the landscape (e.g. on gullies vs. ridges);

5. The type of logging operations and their intensity and pattern in areas surrounding corridors;

6. The impacts of edge effects such as windthrow;

1. The value for fauna of patches connected by corridors.

A basic question is whether or not animals use corridors (or other specific landscape patterns) when moving across the landscape. The answer to this question is again not clear and seems to depend very much on the species. Certainly animals tend to find some patches inhospitable, in most cases large homogeneous areas. In many cases it is clear that animals need a variety of landscape elements to provide for a range of needs that vary in space and time. In a study of dispersing foxes, the animals' movements did not show any correlation to landscape pattern (Storm et al., 1976). The only common pattern observed was that the animals tended to maximize their distance from human habitation. Some animals make extensive use of corridors for much of their movement ( e.g., skunks use hedgerows and mink use stream valleys). Others use corridors on a seasonal basis (e.g.,. wolves use frozen streams for travel). Also, landscape elements often are used to define boundaries between territories.

The exact specifications for connectivity are not well known. Most connectivity-related research has been done in predominately agricultural rather than forested landscapes. Furthermore, it is difficult to extrapolate from individual species connectivity requirements to general rules. However, it is known with certainty that connectivity is important for the survival of populations.

The maintenance of connectivity is important in managed forests where harvesting has the potential to eliminate species from logged areas and fragment and isolate those populations which remain in uncut areas. The idea of connectivity covers features such as exchanges of individuals between sub-populations in a meta-population and the role of sub-optimal habitat (which may or may not be logged) in maintaining links with optimal habitat for particular species.

Connectivity and corridors are often confused. While connectivity is known to be important, the role of corridors in assuring connectivity is not well understood. Corridors assist the movement of animals through otherwise sub-optimal habitat as well as between valued habitat patches. They also provide habitat for resident populations, which may re-colonize adjacent logged and regenerating areas. Corridors may also facilitate continuity between sub-populations in a meta-population, and allow previously unexploited habitat to become available.

Corridors may also have some disadvantages. Corridors may help spread deleterious genes, weeds, pest animals, diseases and fires and act as population 'sinks' (Simberloff et al., 1992). It should be pointed out that such potential disadvantages have rarely been demonstrated in terrestrial ecosystems.

Box J: Relevance to Sustainable/Unsustainable Management :

The maintenance of viable populations of native species is generally considered to be fundamental component of sustainable forest management (at a forest management unit scale). Fragmentation has been called the greatest worldwide threat to biodiversity. Certainly we know that habitat loss is not the only issue, rather the key measure is unfragmented habitat. Unfragmented habitat can and should be considered in land management planning.

Box K: Measurement Methods:

There are a number of measurement methods available to assess landscape fragmentation. The measurement method suggested in Canadian Council of Forest Ministers Technical Report (1977) is to measure road density as a surrogate for fragmentation.

"As a proxy indicator, we can look at human intrusion into landscapes by reporting on the densities of roads in New Brunswick and British Columbia. Although road density is also a function of terrain, it is one type of distribution with significant consequences for landscape fragmentation. In most parts of Canada, roads are a precursor to human activity. The density of roads clearly illustrates the intensity of human activities, ranging from urban areas with very high densities, to remote areas with sparse or nonexistent road networks. Density is expressed as the length of all existing roads divided by the surface area of the ecoregion in question. Some wildlife species are highly sensitive to roads. Wolves, for example, are almost never found where there is more than 0.45 km of roads per km ². While the Atlantic Maritime ecozone has a moderate road density throughout (>0.25 km/km2), in the Taiga Plains and Boreal Cordillera ecozones of British Columbia, there are vast stretches with sparse road densities (<0.25 km/km 2 ). Comparative figures are available for Alaska (0.08 km/km 2), Maine (0.53 km/km2), the United Kingdom (2.29 km/km2), and Connecticut (8.76 km/km2). The only areas that can claim status as undisturbed (non-fragmented) are those located at a certain distance from any road. Because most human influences occur close to roads and decline rapidly with distance, 1 km can be assumed to be the critical distance. In British Columbia, roughly 22% of the landscape is within this distance; the remaining 78% has less human disturbance. More work is needed to establish the relationship between road densities and the fragmentation of forest ecosystems."

If more work is needed to establish the relationship between an indicator and ecosystem condition, then its utility is doubtful. Although road densities are easy to obtain and track, they lack precision as an indicator of fragmentation. As an indicator, road densities chief utility seems to be ease of collection. However, roads have far reaching ecological impacts that may not be directly related to fragmentation and thus may be useful indicators in their own right. For a review on the ecological effects of roads, see Forman et al. 1997.

There are many other more direct ways to measure the structure of a landscape in terms of fragmentation and connectivity. Geographic information systems, readily available in North America can be used to asses key variables. Descriptions of landscape metrics as well as software packages that calculate these metrics can be found in McGarigal and Marks (1993), Baker and Cai (1992), Mladenoff et al. (1993), and Scheiner (1992). For simplicity, metrics can be categorized into four main groups - Patch Shape, Patch Size & Extent, Patch Connectivity, and Patch Dispersion. Useful measures of connectivity include: nearest neighbor probability and percolation index (Turner et al. 1989). Dynamical analyses of landscapes can be used to evaluate potential future effects of disturbance on landscape function and structure.

Box L: Data Required:

If road densities are used as a surrogate measure of fragmentation, spatially-referenced vector data on road densities are all that are required. A more complex road density analysis would include road type, traffic volumes and use relative to ecosystems components (i.e. hunting, fishing). However these types of use data are rarely available.

Analysis of landscape structure required sets of spatially references data on vegetation types, vegetation condition, successional stage, human land use, roads and rivers. Depending on the variability of the terrain, elevation data is also critical. Such complex data sets are increasingly available throughout North America. Coarse-scale vegetation and elevation maps can be downloaded from the internet. Most forest management companies use geographic information systems and complex data sets as a regular part of planning and assessment.

Box M: Data Used for the North American Test:

Date was used from 2 sources. The GIS lab at Boise State University has compiled a detailed spatial data from the Boise National Forest as well as limited coverages from the State of Idaho and Boise Cascade. Road data was taken from 1:24000 scale map sheets and is undoubtedly incomplete. Fragmentation analysis using existing spatial data sets were not conducted during this test because of lack of time and clear methods. However the capability to conduct such analysis as well as the spatial data sets clearly exists at the Boise test site.

Box N: Example Results:

In the Boise study area there was a full range of relevant spatial information to conduct an analysis of fragmentation and connectivity. We measured road density as miles of road per square mile of area and then assessed the value by different by tenure type. The density of roads in each of the land tenures are given below:

It should be noted that these are average values for the entire tenure. Indivudual management areas in the Boise National Forest, for example, can go as high is 14 mi./sq. mi.(Lynette Morelan, pers. Comm).

There are some norms and standards available in North America for road densities, based on the survival of individual species in areas of different densities. For example, Mech et al. (1988) described the primary wolf range in Minnesota as having a mean road density of 0.36 km/km2, and the peripheral and disjunct parts of the range having a road density of 0.54 km/km2. Other studies indicate road densities of greater than 1 mile/mile2 have been shown to reduce habitat security and increase mortality for a range of mammals, including elk, bears, wolverines, and lynx. The Interior Columbia Basin Ecosystem Management Study classed road densities at extremely high (4.7+ mi./sq. mi.), high (1.7-4.7 mi./sq. mi.), moderate (.7-1.7 mi./sq. mi.), low (.1-.7 mi./sq. mi.), and very low (.02-.1 mi./sq. mi.). In the Boise National Forest, there are road density standards for each forest management area, based on a habitat effectiveness model for elk. Typical values are 3.0 mi./sq. mi. Forman et al. (1997) suggested a threshold value of 0.6 km/km2 was a good indicator of the loss of large mammals. In general, standards for assessing road density are not well developed. They can vary by road use type and species. However, road density is an easily obtained value with an increasing body of biological impacts information.

Fragmentation analysis, using existing spatial data sets, were not conducted during this test because of lack of time and clear methods. However the capability to conduct such analysis, as well as the spatial data sets, clearly exists at the Boise test site.

Box O: Assessing the Practicality:

It is feasible to conduct an analysis of road density or habitat structure in any management area that has spatially referenced data. Although methods are not fully standardized, there is increasing agreement in the scientific community regarding the measurement of fragmentation.

Box P: Assessing the Information Value :

Many of the measures of fragmentation and connectivity are obtuse and difficult to understand, such as percolation index. Others may be too simple and thus suspect, such as road density. If this indicator is to have information value it will have to be interpreted into a context that is meaningful to managers and publics. One way to do this is to look at the probability of survival of indicator species under different measures of connectivity and fragmentation.

Box Q: Overall assessment

Accepted, but needs development -

Understanding fragmentation and connectivity is fundamental to assessing the state of ecological integrity of an ecosystem. However it is both difficult to measure and then to interpret the meaning of the measure. We reject road density as a proxy measure of fragmentation. Roads have a range of impacts, which may or may not be related to fragmentation. We have used roads as a separate measure of ecological stress and placed it under indicator R9 (GFE 15, CIFOR 3.3.2).

We recommend that this indicator rely on more direct measures of fragmentation using geographic information systems. Standard methods are not available but there are many good examples and even software (i.e. Fragstats – ftp://ftp.fsl.orst.edu/pub/fragstats.2.0/). Another method to get a general measure of fragmentation is the percolation index.

There is not one standard method of measuring fragmentation. Even if a standard method existed, measures of landscape fragmentation are difficult to relate to ecosystem condition. Any fragmentation norms or standards targets are currently relevant only on a species-by-species basis. This is a major limitation to the indicator. However as more and more case studies are done on the relationship between landscape pattern and species survival, the utility will go up. A practical value of this indicator is that long term trends can be done simply by obtaining sequential data sets from satellite images or other achieved data sets.

We feel there is utility is having an ongoing measure of fragmentation, even if it is generalized. This would act as a overall indicator of the landscape. The difficulty with a generalized measure is that fragmentation is a fractal measure, and the degree of fragmentation depends on the scale at which the question is asked. One solution is to choose the scale level of "stand", as is generally used in forestry land classifications. This scale has been the one commonly employed in fragmentation analysis, as well as in habitat suitability indexes. This is a practical solution that would allow monitoring of a key variable. At the level of stand the key variables to track are the size of each stand type, the shape of each stand and its position on the landscape. This is an important indicator that needs further development to be operational.

Box R: Did you rewrite or revise to a new indicator. If so what?

New ways of measuring the indicator were provided, but the indicator was not rewritten.

Box S: References:

• Akçakaya, H.R., M.A. McCarthy and J. Pearce. 1995a. Linking landscape data with population viability analysis: management options for the helmeted honeyeater Biological Conservation 73:169-176.
• Baker, W. L. and Y. Cai. 1992. The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology. 7:291-302.
• Forman, Richard T., Debra S. Friedman, David Fitzhenery, Jay D. Marten, Allen S. Chen and Lauren E. Alexander. 1997. Ecological effects of roads: Toward three summary indices and an overview for North America. Habitat fragmentation and infrastructure – proceedings.
• Franklin, J. F. and R. T. Forman. 1987. Creating landscape patterns by forest cutting: ecological consequences and principles. Landscape Ecology 1:5-18.
• Keitt, T.H., D.L. Urban, and B.T. Milne. 1997. Detecting critical scales in fragmented landscapes. Conservation Ecology [online]1(1): 4. Available from the Internet. URL: http://www.consecol.org/vol1/iss1/art4
• Lindenmayer, D. B. 1994. Wildlife corridors and the mitigation of logging impacts on fauna in wood production forests in south-eastern Australia: a review. Wildlife Research 21:323-340.
• McGarigal, K. and B. Marks. 1993. FRAGSTATS User's Manual. Department of Forest Science, Oregon State University, Corvallis. UnPubl.
• Mladenoff, D. J., G. E. Host, and J. Boeder. 1993. LANDIS: A spatial model of forest landscape disturbance, succession, and management. In: Second International Conference on Integrating Modeling and GIS. NCGIA. September, 1993.
• Merriam, G. 1984 . Connectivity: a fundamental ecological characteristic of landscape pattern. Pages 5-16 in J. Brandt and P. Agger, editors. Proceedings of the First International Seminar on Methodology in Landscape Ecological Research and Planning. Theme I: Landscape Ecological Concepts ,volume 1. Roskilde University Centre Book Company, Roskilde, Denmark
• Noss, R. F. 1991. Landscape connectivity: different functions and different scales. Pages 27-39 in W. E. Hudson, editor. Landscape linkages and biodiversity . Island Press, Washington, D.C., USA.
• Rosenburg, K.V. and Raphael, M.G. (1986). Effects of forest fragmentation on vertebrates in Douglas-fir forests. In. J. Verner, M.L. Morrison, and C.J. Ralph (eds.) Wildlife 2000: Modeling Habitat Relationships of Terrestrial Vertebrates. Madison, Wisconsin: University of Wisconsin Press, pp. 327-9.
• Scheiner, S. M. 1992. Measuring pattern diversity. Ecology. 73:1860-1867.
• Simberloff, D.J. Farr, J. Cox, and D. Mehlman, 1992. Movement Corridors: Conservation Bargains or Poor Investment?. Conservation Biology 6(4):495.
• Turner, M. G., Gardner, R. H., Dale, V. H., and O'Neill, R. V. 1989. Predicting the spread of disturbance across heterogeneous landscapes. OIKOS. 55:121-129.
• Wilcox, B.A. and Murphy, D.D. (1985). Conservation strategy: The effects of fragmentation on extinction. American Naturalist 125:879-887.