Marbled Murrelet as Target Species For Land Management in Coastal British Columbia

Coastal temperate rain forests are complex ecosystems with high economic value. Conservation management of these forests must be based on clear, defendable strategies with quantifiable goals if they are to withstand economic pressure for exploitation. We introduce the target species strategy as an efficient management tool which allows quantification of conservation goals and continuity in planning time frames. A target species is a species used in defining and monitoring conservation goals. The marbled murrelet (Brachyramphus marmoratus) is an excellent example of a target species. This seabird is highly dependent on coastal old-growth forests as breeding habitat. It is a threatened species in Canada and is considered for legal designation as threatened or endangered in British Columbia. Therefore, it has become a focal species in coastal temperate rain forest conservation. Based on data collected during 4 years of marbled murrelet inventory by the British Columbia Ministry of Environment, Lands and Parks in Clayoquot Sound, British Columbia, we designed a habitat suitability index for the marbled murrelet for efficient habitat evaluation. This index allows a prioritization of habitats based on information from digital Vegetation Resources Inventory maps recently completed in Clayoquot Sound, as well as fine scale habitat prioritization based on vegetation plots. The habitat suitability index was used with a geographic information system (GIS) to rank and map habitats of importance to marbled murrelets in the Clayoquot Sound. The target species strategy, in combination with the presented habitat evaluation tools, bridges the gap between research and conservation management of the marbled murrelet and its habitat

Can Marbled Murrelet Use of Nesting Habitat be Predicted from Mapped Forest Characteristics?

We tested whether the use of nesting habitat by Marbled Murrelets (Brachyramphus marmoratus) could be predicted from mapped information. Our goal was to evaluate the feasibility of modelling habitat suitability on a large scale in preparation for building a sophisticated model, and to determine whether such a habitat suitability model could make basic predictions on murrelet nesting activity. In this study we did not build an elaborate habitat suitability model, but rather tested the predictions from a simple, preliminary model, based on a single mapped forest characteristic. Of the forest and terrain characteristics available on resource maps, tree height was the most useful variable to predict suitability of murrelet habitat in an analysis of data from 118 vegetation plots collected previously in the study area. We compared audio-visual detections of murrelets at 11 pairs of stands, selected using Vegetation Resource Inventory maps, with each pair having one stand with trees ON AVERAGE \u3e35 m tall (TALL) and one with trees(SHORT). Our prediction was that the TALL stands would show more activity associated with breeding by murrelets than the SHORT stands. Each pair of stands had a similar elevation, distance to ocean, slope position and aspect. We performed standardized audiovisual surveys at paired stands on the same morning to avoid biases caused by weather and season. We observed significantly higher numbers of occupied detections and subcanopy detections (both thought to be related to nearby breeding) in the TALL stands than in SHORT stands. Thus, we were able to show that Marbled Murrelet breeding activity can be predicted based on a mapped forest characteristic, a result that set the stage for the more sophisticated habitat model

Can Marbled Murrelet Use of Nesting Habitat be Predicted from Mapped Forest Characteristics?

We tested whether the use of nesting habitat by Marbled Murrelets (Brachyramphus marmoratus) could be predicted from mapped information. Our goal was to evaluate the feasibility of modelling habitat suitability on a large scale in preparation for building a sophisticated model, and to determine whether such a habitat suitability model could make basic predictions on murrelet nesting activity. In this study we did not build an elaborate habitat suitability model, but rather tested the predictions from a simple, preliminary model, based on a single mapped forest characteristic. Of the forest and terrain characteristics available on resource maps, tree height was the most useful variable to predict suitability of murrelet habitat in an analysis of data from 118 vegetation plots collected previously in the study area. We compared audio-visual detections of murrelets at 11 pairs of stands, selected using Vegetation Resource Inventory maps, with each pair having one stand with trees ON AVERAGE \u3e35 m tall (TALL) and one with trees(SHORT). Our prediction was that the TALL stands would show more activity associated with breeding by murrelets than the SHORT stands. Each pair of stands had a similar elevation, distance to ocean, slope position and aspect. We performed standardized audiovisual surveys at paired stands on the same morning to avoid biases caused by weather and season. We observed significantly higher numbers of occupied detections and subcanopy detections (both thought to be related to nearby breeding) in the TALL stands than in SHORT stands. Thus, we were able to show that Marbled Murrelet breeding activity can be predicted based on a mapped forest characteristic, a result that set the stage for the more sophisticated habitat model

Habitat Suitability Mapping for Marbled Murrelets in Clayoquot Sound

Digitally mapped information on the habitats of threatened wildlife species, in particular the Marbled Murrelet (Brachyramphus marmoratus), is important to the management of forest resources in this region. We created habitat suitability maps for Marbled Murrelets based on a Habitat Suitability Index model, which evaluates forest polygons from Vegetation Resource Inventory (VRI) maps. The VRI maps, which contain detailed land cover information with a focus on forest cover, were determined to be better suited as a basis for the model than the Terrestrial Ecosystem (TEM) maps, which contain biogeoclimatic information on vegetation associations. We reached this conclusion by comparing mapped vegetation data with field data and by considering the relevance of the mapped information to murrelet nesting. Information on habitat requirements of murrelets, which was the basis for the model, came from past murrelet inventories and from the literature. This information guided our selection of vegetation characteristics used to represent habitat suitability. We sampled these characteristics in vegetation plots in stratified, randomly-selected polygons from VRI maps. The sampled variables describing habitat suitability were summarized in two factors by a Principal Component Analysis (PCA) and related to mapped variables available for these polygons. The significant relationships between mapped and PCA factor variables were modelled with 90th quantile least absolute deviation regressions. Based on these regressions and information from literature we selected seven mapped variables to be included in a habitat suitability model. We constructed non-linear, individual suitability indices (SI), which assigned evaluation scores to the values of the seven selected mapped variables. The seven individual SIs were combined in a single equation whose output is a habitat suitability index (HSI) between 0 and 1 for each mapped polygon. We divided the HSI scores into four categories: “Excellent” (HSI \u3e0.875); “Good” (HSI between 0.78 and 0.875); “Sub-optimal” (HSI between 0.65 and 0.78); and “Unsuitable” (HSI \u3c0.65). The application of this Habitat Suitability Model to 335,127 ha of land area (everything except for the ocean and fresh water bodies) on 36 1:20 000 map sheets in Clayoquot Sound resulted in: 34,833 ha (10.4% of the land area) of Excellent habitat; 40,466 ha (12.1%) of Good habitat; 59,388 ha (17.7%) of Sub-optimal habitat; and 200,440 ha (59.8%) of Unsuitable habitat. The model identified 75,299 ha (22.5% of land area) of Excellent and Good habitat, and 259,828 ha (77.5%) of Sub-optimal and Unsuitable habitat

Habitat Suitability Mapping for Marbled Murrelets in Clayoquot Sound

Digitally mapped information on the habitats of threatened wildlife species, in particular the Marbled Murrelet (Brachyramphus marmoratus), is important to the management of forest resources in this region. We created habitat suitability maps for Marbled Murrelets based on a Habitat Suitability Index model, which evaluates forest polygons from Vegetation Resource Inventory (VRI) maps. The VRI maps, which contain detailed land cover information with a focus on forest cover, were determined to be better suited as a basis for the model than the Terrestrial Ecosystem (TEM) maps, which contain biogeoclimatic information on vegetation associations. We reached this conclusion by comparing mapped vegetation data with field data and by considering the relevance of the mapped information to murrelet nesting. Information on habitat requirements of murrelets, which was the basis for the model, came from past murrelet inventories and from the literature. This information guided our selection of vegetation characteristics used to represent habitat suitability. We sampled these characteristics in vegetation plots in stratified, randomly-selected polygons from VRI maps. The sampled variables describing habitat suitability were summarized in two factors by a Principal Component Analysis (PCA) and related to mapped variables available for these polygons. The significant relationships between mapped and PCA factor variables were modelled with 90th quantile least absolute deviation regressions. Based on these regressions and information from literature we selected seven mapped variables to be included in a habitat suitability model. We constructed non-linear, individual suitability indices (SI), which assigned evaluation scores to the values of the seven selected mapped variables. The seven individual SIs were combined in a single equation whose output is a habitat suitability index (HSI) between 0 and 1 for each mapped polygon. We divided the HSI scores into four categories: “Excellent” (HSI \u3e0.875); “Good” (HSI between 0.78 and 0.875); “Sub-optimal” (HSI between 0.65 and 0.78); and “Unsuitable” (HS