DOI: 10.5281/zenodo.4779107
title: Habitat suitability predictions for six boreal forest indicator bird species in Finland

description: This repository contains files of values of habitat suitability predictions for six studied biodiversity indicator bird species in Finland based on nesting habitat suitability modelling using the MaxEnt model. Habitat suitability values are based on airborne laser scanning and other remote sensing data and spatial information on the distribution of important forest stands over the whole country. The predictions of habitat suitability for each species were included in the following publication: Virkkala, Raimo<sup>a</sup>; Leikola, Niko<sup>a</sup>; Kujala, Heini<sup>b</sup>; Kivinen, Sonja<sup>a,c</sup>; Hurskainen, Pekka<sup>a</sup>; Kuusela, Saija<sup>a</sup>; Valkama, Jari<sup>b</sup>; Heikkinen, Risto K.<sup>a </sup>2022. Developing fine-grained nationwide predictions of valuable forests using biodiversity indicator bird species, Ecological Applications 32(2): e2505. https://doi.org/10.1002/eap.2505. <sup>a</sup>Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland <sup>b</sup>Finnish Museum of Natural History, P.O. Box 17, FI-00014 University of Helsinki, Finland <sup>c</sup>Department of Geographical and Historical Studies, University of Eastern Finland. P.O. Box 111, FI-80101 Joensuu, Finland. The files are zipped into one compressed file which includes ArcGIS compatible tiff files indicating the spatial location of the 96 m × 96 m grid cells in which the predicted habitat suitability for each species is presented. The habitat suitability models and values of species-specific habitat suitability were developed across the whole country using Maxent software (Phillips et al. 2006). The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summarization of the key settings and elements of the study are provided below. A detailed treatment will be provided in the article published in Ecological Applications (Virkkala et al. 2022) for which an official link is as follows :https://doi.org/10.1002/eap.2505 . <strong>Summary of the study:</strong> The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modelling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, i.e., 96 x 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting habitat suitability modelling, i.e., the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas was considerable, but our analysis also revealed many potentially high-quality forest stands outside protected areas. However, many of these sites are increasingly threatened by logging due to increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and corresponding approach would also be feasible and recommendable elsewhere where similar data are available. <strong>The study species:</strong> Our indicator species included three hawk species, the European honey buzzard <em>Pernis apivorus</em>, the northern goshawk <em>Accipiter gentilis</em> and the common buzzard <em>Buteo buteo</em>, and three woodpecker species, the white-backed woodpecker <em>Dendrocopos leucotos</em>, the lesser spotted woodpecker <em>Dryobates minor</em> and the Eurasian three-toed woodpecker <em>Picoides tridactylus.</em>The locations of the nest sites of the focal species were extracted from the bird ringing data gathered and maintained by the Finnish Natural History Museum (LUOMUS) and used as an input data in the modelling. Nest sites were located in a 96 m x 96 m grid cell. <strong>Habitat suitability modelling:</strong> For each grid cell in the 96 x 96 m lattice system, we measured environmental predictor variables representing four variable groups: (i) forest stand characteristics, (ii) land cover/land use within the 96-m grid cells, (iii) land cover/land use at the broader landscape scale, and (iv) two variables showing the geographic variation in summer and winter thermal conditions. In the model building and in the variable-selection process, we applied the framework outlined in Williams et al. (2012), where ecologically similar predictor variables are first grouped into two or more subgroups and tested for their importance, using backward elimination to exclude variables that do not explain significant levels of variation in the response variable. Significant variables from each subgroup are then combined and again tested for their importance via backward elimination and associated tests in MaxEnt. Here, we used this approach to reduce the number of predictor variables within three of the subgroups described above: (A) eight forest stand structure and quality variables, (B) eight land cover/land use variables recorded at the 96-m focal (nesting) site, and (C) eleven landscape-level measures of forest stands and land cover/land use, recorded with a 500 m and 1 km buffer around the nest site for woodpecker and hawk species, respectively. In addition, (D) the two local topoclimatic variables, January mean temperature and growing degree days (GDD5) (see Heikkinen et al. 2020) were included in the final predictive model and tested there. In all the models, default values were used except that we used a bias grid in MaxEnt to correct for survey bias in the distribution of ringing sites. The ringing data of the six bird species were heavily concentrated in southern Finland, although all species (except the white-backed woodpecker) also occur, albeit more sparsely, in northern Finland. As MaxEnt compares the environmental conditions of nesting sites to a randomly sampled set (called background points) across the study area (the whole of Finland), not accounting for survey bias can lead to areas in northern Finland being erroneously designated as unsuitable, when they are in fact poorly surveyed for these species (Phillips et al. 2009). To account for this, we built a kernel density layer (in R v. 3.6.1, with package <em>MASS </em>v. 7.3-51.4) using the nest locations of the six species, supplemented with nest locations of three, predominantly northern bird species (the rough-legged buzzard <em>Buteo lagopus</em>, the merlin <em>Falco columbarius</em> and the hawk owl <em>Surnia ulula</em>), and a 50 km kernel distance (see Elith et al. 2011, Kramer-Schadt et al. 2013, Kujala et al. 2015). As MaxEnt models are based on presence-only data, absolute distribution sizes cannot be derived from the predicted maps (Guillera-Arroita et al. 2015). Therefore, species distribution patterns from MaxEnt predictions can only be examined in relative terms. High values in the 96 x 96 m grid cells indicate high suitability for the species based on Maxent prediction. <strong>References</strong>: Elith, J., S. J. Phillips, T. Hastie, M. Dudik, Y. E. Chee, and C. J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17:43-57. Guillera-Arroita, G., J. J. Lahoz-Monfort, J. Elith, A. Gordon, H. Kujala, P. E. Lentini, M. A. McCarthy, R. Tingley, and B. A. Wintle. 2015. Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography 24:276-292. Heikkinen, R. K., N. Leikola, J. Aalto, K. Aapala, S. Kuusela, M. Luoto, and R. Virkkala. 2020. Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. Kramer-Schadt, S., J. Niedballa, J. D. Pilgrim, B. Schroder, J. Lindenborn, V. Reinfelder, M. Stillfried, I. Heckmann, A. K. Scharf, D. M. Augeri, S. M. Cheyne, A. J. Hearn, J. Ross, D. W. Macdonald, J. Mathai, J. Eaton, A. J. Marshall, G. Semiadi, R. Rustam, H. Bernard, R. Alfred, H. Samejima, J. W. Duckworth, C. Breitenmoser-Wuersten, J. L. Belant, H. Hofer, and A. Wilting. 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions 19:1366-1379. Kujala, H., A. L. Whitehead, W. K. Morris, and B. A. Wintle. 2015. Towards strategic offsetting of biodiversity loss using spatial prioritization concepts and tools: A case study on mining impacts in Australia. Biological Conservation 192:513-521. Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259. Phillips, S. J., M. Dudik, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick, and S. Ferrier. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19:181-197. Williams, K. J., L. Belbin, M. P. Austin, J. L. Stein, and S. Ferrier. 2012. Which environmental variables should I use in my biodiversity model? International Journal of Geographical Information Science 26:2009-2047.