Effective conservation decisions require detailed knowledge of where organisms occur and how their distribution and abundance may be affected by environmental change, for example changing land use practices or climate change. However, comprehensive surveys covering many species and large areas are prohibitively expensive and such data are rarely available. A much less expensive and commonly used alternative is to estimate the distribution or abundance of species from observations at a limited number of locations with mathematical models known as species distribution models (or a variety of other names, such as niche models, habitat models). Citizen science data, which are contributed by volunteers for many species, can be a valuable data source for species distribution models, with which to address some of today’s conservation challenges.
At ArtDatabanken, I am working with Tord Snäll to map the current distribution of forest birds in Sweden in order to inform land use decisions. We use data collected and contributed by birdwatchers from all over the country.
Before coming to ArtDatabanken, I worked at the University of Leeds, UK on methods for the mapping of species and habitats over larger areas. Specifically, I worked on how we can select appropriate spatial scales in species distribution models and on creating high resolution habitat maps from remotely sensed data. Prior to my PhD I worked several years in ornithological research and conservation projects in a number of countries, including with what became my favorite bird species, the white-naped crane.
O'Connell, J., Bradter, U. & Benton, T.G., 2015. Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 109: 165-177.
O'Connell, J., Benton, T.G. & Bradter, U., 2013. Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK. SPIE Proceedings 8887: 1-15.
Bradter, U., Kunin, W.E., Altringham, J.D., Thom, T.J. & Benton, T.G., 2013. Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm. Methods in Ecology and Evolution 4: 167-174.
Setchfield, R., Mucklow, C., Davey, A., Bradter, U., Anderson, G.Q.A., 2012. An agri-environment option boosts productivity of Corn Buntings Emberiza calandra in the UK. Ibis 154: 235-247.
Bradter, U., Thom, T.J., Altringham, J.D., Kunin, W.E. & Benton, T.G., 2011. Prediction of National Vegetation Classification communities in the British uplands using environmental data at multiple spatial scales, aerial images and the classifier random forest. Journal of Applied Ecology 48: 1057-1065.
Bradter, U., S. Gombobaatar, Ch. Uuganbayar, Grazia, T.G. & Exo, K.-M., 2007. Time budget and habitat use of white-naped cranes (Grus vipio) in the Ulz river valley, northeastern Mongolia during the breeding season. Bird Conservation International 17: 259-271.
Scheiffarth, G., Frank, D., Bradter, U. & Thoden, B., 2006. Crushing shells in a stomach: more than simple mechanics. Journal of Ornithology 147 suppl: 246.
Bradter, U., S. Gombobaatar, Ch. Uuganbayar, Grazia, T.G. & Exo, K.-M., 2005. Reproductive performance and nest site selection of white-naped cranes (Grus vipio) in the Ulz river valley, northeastern Mongolia. Bird Conservation International 15: 313-326.
Bradbury, R.B. & Bradter, U., 2004. Habitat selection by yellow wagtails on lowland wet grassland. Ibis 146: 241-246.
Exo, K.-M., Ketzenberg, C. & Bradter, U., 2000. Numbers, phenology and distribution of shorebirds in the Frisian Wadden Sea 1992 – 1995. Oldenburger Jahrbuch 100: 337-380 (in German).
Exo, K.-M., Ketzenberg, C. & Bradter, U., 1999. Space-time pattern of staging birds. In: Nationalparkverwaltung Niedersächsisches Wattenmeer & Umweltbundesamt, eds.: Umweltatlas Wattenmeer, Bd. 2. Stuttgart: Ulmer, pp. 84-85 (book chapter: popular science, in German).