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Maps Explained

Maps Explained

Quick-start guide to interpret the different maps

The Maps illustrate how reindeer perceive the landscape with respect to resources, functional areas, corridors, barriers, cumulative impacts and human footprint. Maps are based on statistical models combining reindeer GPS data with many environmental variables (food, topography, climate, vegetation dynamics etc.), infrastructure (roads, cabins, trails, hydropower, powerlines etc.), and human disturbance (tourist volume). Both the degree of avoidance of infrastructure and their Zone of Influence are estimated [16]; the estimated degree of avoidance or selection for all these variables is illustrated at the bottom of this page (for Norwegian description see here [0]). Maps are comparable across areas, and highlight the most imporant functional areas and corridors in Norway. Most maps are avaialble for the entire Norway - although not shown here [0]. Maps can be grouped into two groups:

All maps are needed to undersand reindeer habitat. However, only bird-eye view maps (2) allow to understand which areas are the most important in the entire landscape, and should therefore be prioritized for conservation or restoration

All images: Panzacchi et al 2022 (CC BY-SA) 

Images
Images



Pixel-focused maps

Assess each pixel in isolation, irrespective from its surroundings

Fig. refer to summer, Hardangervidda [8]

HABITAT QUALITY / SUITABILITY MAP: 

Greener pixels provide more suitable resources, preferred by reindeer (e.g. preferred vegetation, topographic and & climatic conditions) while lighter pixels tend to be avoided (e.g. due to roads, cottages, tourist volume & more [1,8,16, 17, 19]). Shows all suitalbe areas, including those reindeer cannot access (or can no longer access); this map can thus be useful to identfy areas that are no longer used due to loss of migration corridors


HABITAT PERMEABILITY MAP/BARRIERS: 

Darkest pixels represent impermeable barriers for the species' movements (e.g. fences, steep slopes, infrastructures); lighter pixels can be easily traversed by the species

 

Landscape-focused maps

Consider connectivity, highlight most functonal areas and corridors & help prioritizing [7,17]

Maps produced with ConScape [7] (ConnectedLandscape)

HABITAT FUNCTIONALITY MAP: 

Lighter areas are at the same time good (provide good quality resources an little disturbance) and well connected to other good quality areas (reindeer can easily reach them). These are therefore the most functional areas (or, core areas) in the entire landscape. Darker areas are unsuitable and/or isolated/ poorly accessible [17, 7,4,5,6,8,9,14]. These maps higlight the most important areas used and needed by reindeer in the entire landscape, and thus can be used for prioritizing [14]


MOVEMENT CORRIDORS MAP: 

Lighter colors indicate areas traversed by a higher number of reindeer moving among functional areas, i.e. the most crucial corridors and bottle necks to maintain connectivity within an area [17, 4,5,6,7,8,9,14]. Note that corridors between different areas (e.g. between Hardangervidda & Setesdal south or Nordfjella north), or between sub-areas (e.g. Snøhetta east & west) can be modeled by focussing on a larger area. NB: Results refer to the area shown (new corridors may appear in larger areas)



Human footprint maps

Visualize human impact on reindeer habitat [0,17]. Reindeer prefer areas with suitable natural resources and little disturbance. These maps show all these components together, and separately

SEE DASHBOARD HERE
These maps do not (yet) consider connectivity [feature under developent]

HUMAN FOOTPRINT MAP:

Darker colors indicate areas with the highest human impact on good reindeer habitat, i.e. areas that could potentially provide very suitable natural resources, BUT where there are also infrastructures and human activities avoided by reindeer [0, 3, 17, 19]. These area (potentially good, but "ruined" by human activities) can therefore be relevant for restoration


NATURAL POTENTIAL - "Nature Only" Scenario:

Darker colors indicate areas with potentially the best natural resources for reindeer, if there were no infrastructures or human activities [0, 3, 17, 19]

 

POTENTIAL IMPACT - "Humans Only" Scenario:

Darker colors indicate areas with the highest amount of infrastructures & human activities avoided by reindeer - irrespective from natural resources [0, 3, 17, 19]

ESTIMATES OF AVOIDANCE OR SELECTION OF INFRASTRUCTURE, HUMAN ACTIVITIES & LANDSCAPE FEATURES

Below we illustrate the degree to which GPS-monitored reindeer prefer or avoid each variable (left), and the degree to which these represents barriers for their movements (right). These estimates take inot account both the degree of avoidance of infrastructure (e.g. how strongly a tourist resort is avoided) and their Zone of Influence (how far the effect is detectable) [16; 0]. All maps build upon the results of two analyses (Resource Selection Function [1; 16], and Step Selection Function, right [2]) based on GPS data and on a range of environmental variables (incl. food, topography, climate), infrastructure (incl. roads, cabins, trails, hydropower, powerlines), and human activities (tourist volume). 

References

[0] Panzacchi, M, van Moorter, B, Tveraa, T, Rolandsen, C M, Gundersen, V, Lelotte, L, Dos Santos, B B N, Bøthun, S W, Andersen, R, Strand, O. (2022). Statistisk modellering av samlet belastning av menneskelig aktivitet på villreinområder. Identifisering av viktige leveområder og scenarioanalyser for konsekvensutredning og arealplanlegging. NINA Report 2189. Norwegian Institute for Nature Research (in Norwegian with english summary)

[00] Panzacchi, M., van Moorter, B., Sydenham, M.A.K., Horntvedt Thorsen, N., Niebuhr, B.B., Stange, E., Jansson, U., Nordén, B, Hofgaard, A., Rusch, G., Rolandsen, C. & Solberg E. 2024. Nasjonal kartlegging av grønn infrastruktur. De første nasjonale kartene for solitære bier, elg, edellauvskog og andre treslag. NINA Rapport 2371. 

[1] Panzacchi M, van Moorter B Strand O, Loe LE, Reimers E. (2015) Searching for the fundamental niche using individual-based habitat selection modelling across populations. Ecography 38: 659-669. (open access)

[2] Panzacchi, M, van Moorter B, Strand B, Saerens M, Kivimäki I, St. Clair CC, Herfindal I, Boitani L (2016) Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths. J Anim Ecol 85: 32-42. (open access)

[3] Lelotte Lucie (2021) MSc Thesis. Analysis of the human footprint on reindeer summer habitat. Using habitat selection modeling to assess anthropogenic drivers of habitat loss in Norwegian wild mountain reindeer. Univ. Liege, Belgium & NINA  (link)

[4] Kivimäki I, Shimbo M, Særens M (2014) Developments in the theory of randomized shortest paths with a comparison of graph node distances. Physica A: Statistical Mechanics and its Applications 393: 600-616. 

[5] Kivimäki I, van Moorter B, Panzacchi M, Jari Saramäki, Marco Saerens. (2020) Maximum likelihood estimation for randomized shortest paths with trajectory data. Journal of Complex Networks (8), 4.

[6] Van Moorter B, Kivimäki I, Panzacchi M, Særens M (2021). Review & Synthesis: defining and quantifying Effective Connectivity. Ecography44, 6: 870-884

[7] Van Moorter M, Kivimaki I, Noack A, Devooght R, Panzacchi M, Hall K, Leleux P, Saerens M. (2022) Accelerating advances in landscape connectivity modeling with the ConScape library. Methods in Ecology and Evolution, 00, 1-13

[8] Gundersen, V., van Moorter, B. Panzacchi, M., Rauset, G.R. & Strand, O. 2021. Villrein-ferdselsanalyser på Hardangervidda - Anbefalinger og tiltak. NINA Rapport 1903. Norsk institutt for naturforskning. [in Norwegian]

[9] Dorber, M., Panzacchi, M., Strand, O., B. van Moorter. New indicator of habitat functionality reveals high risk of underestimating trade-offs among sustainable development goals: The case of wild reindeer and hydropower. Ambio (2023). https://doi.org/10.1007/s13280-022-01824-x

[10] M.Panzacchi, Fornybar energi og reinsdyr: nye metoder for å simulere effekten av inngrep, forstyrrelser og kompenserende tiltak Energi Norge - Produksjonsteknisk konferanse 2021 (here)

[11] Stange E, Panzacchi M, van Moorter B (2019) Modelling green infrastructure for conservation and land planning – a pilot study. NINA Report 1625

[12] Panzacchi M, Bram van Moorter, Ilkka Kivimaki, Marco Særens, Andreas Noak, Kimberly Hall, Olav Strand, Audun Stien, Torkild Tveraa, Knut Langeland, Stefan Blumentrath, Vegard Gundersen et al. (2019). Samla belasting og bærekraftig arealplanlegging (Cumultive impacts) – Fagseminaret "Naturmangfold og klima" under NINA-dagan 2019 (link)

[13] Panzacchi, Manuela; Van Moorter, Bram; Strand, Olav; Kivimäki, Ilkka; Saerens, Marco; Stien, Audun; Tveraa, Torkild; Langeland, Knut; Gundersen, Vegard; Eftestøl, Sindre; Tsegaye, Diress; Coleman, Jonathan I.R.. (Keynote talk) 2021. How to Quantify the Cumulative Impact of Human Activities on reindeer and aid Sustainable Land Planning". 18th North American Caribou Workshop (link)

[14] Van Moorter B, Kivimäki I, Panzacchi M, Saura S, Niebuhr B B, Strand O, Saerens M. (2023) Habitat Functionality: integrating environmental and geographic space in niche modelling for conservation planning. Ecology 104(7): e4105. (open acess)

[15] Unikt verktøy beregner hvordan mennesker påvirker naturen. Panzacchi M, Landrø J. NINA nyhettsak 2020 (here)

[16] Niebuhr B B, Van Moorter B, Stien A, Tveraa T, Strand O, Langeland K, Sandström P, Alam, M, Skarin A, Panzacchi M  (2022). Estimating the cumulative impact and zone of influence of anthropogenic features on biodiversity. Method in Ecology and Evolution  14: 2362–2375. (open access)

[17] van Moorter, B., Panzacchi, M., Niebuhr, B.B., Lelotte, L., Rolandsen, C.M., & Tveraa, T. 2023. Menneskelig påvirkning på alle villreinområder i Norge. Et nytt Dashbord som leverer kart og statistiske estimater til støtte for forvaltningsprosesser. NINA Rapport 2342.  (here)

[18] Niebuhr, B.B., Panzacchi, M., van Moorter, B., Gundersen, V., & Tveraa, T. 2023. Scenarioanalyser – evaluering av effekten av avbøtende tiltak for villrein i Rondane Nord. NINA Rapport 2359.  (here)

[19] DASHBOARD: Dashbord”, https://www.nina.no/apps/villrein.habitattap 



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