Date:
2018/06/15

Time:
13:30

Room:
K305 Alvar


Forest indicator species correlate only weakly with richness of red-listed species and perform poorly compared to simple stand variables

(Oral and Poster)

Jörgen Rudolphi
,
Joakim Hjältén
,
Therese Lövroth
,
Mats Dynesius
,
Jörgen Olsson
,
Jean-Michel Roberge

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In the face of worldwide biodiversity declines, a variety of conservation measures are being implemented to preserve species and their habitats. Due to imperfect ecological knowledge and limited economic resources, these conservation measures often involve the use of shortcuts. Among these, the use of indicator species – organisms whose characteristics are used as an index of other ecosystem attributes – has been proposed as a tool for conservation planning and monitoring in a range of environments. The overarching aim of this study was to evaluate the usefulness of signal species as indicators of high species richness among species of special conservation concern in a managed boreal landscape. To address these questions, we surveyed wood-decay fungi, bryophytes, beetles, and forest structure in a large number of stands characterized by varying management histories within a geographically restricted area, and related the occurrence of signal species and basic forest characteristics to the number of red-listed species.

Our study, involving a large number of forest stands covering a wide gradient of forest characteristics, showed that indicator species commonly used in forest conservation planning do indicate red-listed species, at least to some extent: When considering stands of all ages, the presence of signal species was associated with higher richness of red-listed species, and the number of signal species correlated positively with that of red-listed species. However, when restricting the analyses to older forests, we could not detect any correlation between signal species and the richness of red-listed species. When we included information on forest structure in the analyses we found that, for predicting the richness of red-listed species across all of our study stands, the amount of coarse woody debris and stand age were much better predictors than the presence-absence or number of signal species. Moreover, adding information about signal species to models based on these forest variables did not bring any substantial improvements to our capacity to predict the number of red-listed species. This suggests that – in this particular setting – information about signal species does not contribute with information about red-listed species beyond that already derivable from forest stand measurements.


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