Anna Ringvall

National Inventory of Landscapes in Sweden (NILS) - scope, design, and experiences from establishing a multiscale biodiversity monitoring system

The landscape-level and multiscale biodiversity monitoring program National Inventory of Landscapes in Sweden (NILS) was launched in 2003. NILS is conducted as a sample-based stratified inventory that acquires data across several spatial scales, which is accomplished by combining aerial photo interpretation with field inventory. A total of 631 sample units are distributed across the land base of Sweden, of which 20% are surveyed each year. By 2007 NILS completed the first 5-year inventory phase. As the reinventory in the second 5-year phase (2008–2012) proceeds, experiences and insights accumulate and reflections are made on the setup and accomplishment of the monitoring scheme. In this article, the emphasis is placed on background, scope, objectives, design, and experiences of the NILS program. The main objective to collect data for and perform analyses of natural landscape changes, degree of anthropogenic impact, prerequisites for natural biological diversity and ecological processes at landscape scale. Different environmental conditions that can have direct or indirect effects on biological diversity are monitored. The program provides data for national and international policy and offers an infrastructure for other monitoring program and research projects. NILS has attracted significant national and international interest during its relatively short time of existence; the number of stakeholders and cooperation partners steadily increases. This is constructive and strengthens the incentive for the multiscale monitoring approach.

On the Field Performance of Transect Relascope Sampling for Assessing Downed Coarse Woody Debris

Abstract Transect relascope sampling is a recently proposed method for making efficient inventories of coarse woody debris on the ground. However, before the method can be recommended, its performance in the field requires investigation. This paper presents the results of a field study in which 11 surveyors tested the method in two coniferous stands in northern Sweden. The average bias introduced by the surveyors was moderate under normal sighting conditions, but underestimation was substantial when combining a small relascope angle with poor sighting conditions. In all cases, the differences between the systematic errors of individual surveyors were substantial. The conclusion is that transect relascope sampling is not an appropriate method when the demand for unbiased estimates is high, e.g. in connection with long-term monitoring, but that the method can be useful as a first phase in double sampling or in ocular-based inventories aimed at identifying biologically valuable areas.

A field test of surveyors' influence on estimates in line transect sampling.

Abstract Line transect sampling is a well-known method for estimating densities of wildlife populations and can be a useful alternative to the traditional methods for timber cruising when assessing objects and species of interest in relation to biodiversity. This paper presents results from a field test of line transect sampling for inanimate objects in boreal forest, focusing on the surveyors’ influence on estimates under different surveying conditions. The method was tested by 11 surveyors in northern Sweden in two forest types and for two different object types. An underestimation of between −0.3 and −22.2% was found, depending on the surveying conditions and the model used for the detection function. The underestimation is partly due to violation of the assumption that all objects on or very close to the survey line are detected. It was found that systematic differences among surveyors were generally moderate. At most, the coefficient of variation for surveyor effect was 6.8%. Still, this size of surveyor effect can be problematic for long-term monitoring, where systematic differences among surveyors’ estimates may indicate illusory changes or disguise real trends. When comparing line transect sampling with other sampling methods, the increased variation in estimates caused by surveyors’ systematic and random errors as well as the variation caused by model selection must be considered.

Using statistical power analysis as a tool when designing a monitoring program: experience from a large-scale Swedish landscape monitoring program.

The National Inventory of Landscapes in Sweden (NILS) is a large-scale, sample-based monitoring program that combines aerial photointerpretation with field inventory to follow landscape-scale biophysical conditions and changes. A statistical power analysis was conducted before the NILS program began in 2003 with the aim to determine an appropriate sampling effort and compare some design alternatives. The chosen sampling effort was then evaluated in a second power analysis conducted just before the first 5-year re-inventory rotation started. The latter power analysis revealed which magnitude of actual change might be detected within the future for different central monitoring variables. This article reports results from these power analyses and discusses our experiences in using power analysis as a tool for designing large-scale monitoring programs. The results showed that even quite small changes in the more common variables, such as land cover types and more common plant species, can be detected on the national scale. However, on the regional scale, or for less common variables, changes will be more difficult to detect. The power analyses have revealed the size level of changes that will be possible to detect. The results have also generated incentives for further improvements of NILS, e.g., input to the modification and revision of the variable content, flow and hierarchy, and incentives for launching other complementary monitoring programs connected to NILS. They have also created a basis for a better and more user-oriented communication of results from NILS to different stakeholders.

Conditioning post-stratified inference following two-stage, equal-probability sampling

This paper considers conditioning on the size of the samples observed in post-strata following a two-stage sampling design. We argue that it is reasonable to do so despite the complexity of the design. We derive an expression for the covariances among post-strata estimates resulting from secondary sampling units on the same primary sampling unit which reside in different post-strata. To motivate both issues we describe a two-stage LiDAR-assisted sample for aboveground biomass that was reported in Gregoire et al. (Can J For Res 41:83–95, 2011).