Estimating lichen volume and reindeer winter pasture quality from Landsat imagery

Reindeer and caribou are keystone species in the circumpolar region, and rely on lichens as their main winter forage to survive in some of the most extreme environments on Earth. Lichen mats, however, can be heavily overgrazed at high deer densities, triggering area abandonment or population declines. Although the species' management and conservation require precise information on the quality of winter grazing areas, no reliable and cost-efficient methods are available to date to measure lichen volume across wide and remote areas. We developed a new Lichen Volume Estimator, LVE, using remote sensing and field measurements. We used a Landsat TM land cover mask to separate lichen heath communities from other vegetation types and, therein, we predicted lichen volume from a two dimensional Gaussian regression model using two indexes: the Normalized Difference Lichen Index, NDLI (Band 5−Band 4 / Band 5+Band 4), and the Normalized Difference Moisture Index, NDMI (Band 4−Band 5 / Band 4+Band 5). The model was parameterized using 202 ground measurements equally distributed across a gradient ranging from 0 to 80 lichen dm3/m2 (R2=0.74 between predicted and observed ground measurements), and was validated with a ten-fold cross validation procedure (R2=0.67), which also showed a high parameter stability. The LVE can be a valuable tool to predict the quality of winter pastures for reindeer and caribou and, thus, help to improve the species' management and conservation. Remote sensing Habitat mapping Reindeer Lichen volume and biomass Winter pasturespublishedVersio

Maximum likelihood estimation for randomized shortest paths with trajectory data

Randomized shortest paths (RSPs) are tool developed in recent years for different graph and network analysis applications, such as modelling movement or flow in networks. In essence, the RSP framework considers the temperature-dependent Gibbs–Boltzmann distribution over paths in the network. At low temperatures, the distribution focuses solely on the shortest or least-cost paths, while with increasing temperature, the distribution spreads over random walks on the network. Many relevant quantities can be computed conveniently from this distribution, and these often generalize traditional network measures in a sensible way. However, when modelling real phenomena with RSPs, one needs a principled way of estimating the parameters from data. In this work, we develop methods for computing the maximum likelihood estimate of the model parameters, with focus on the temperature parameter, when modelling phenomena based on movement, flow or spreading processes. We test the validity of the derived methods with trajectories generated on artificial networks as well as with real data on the movement of wild reindeer in a geographic landscape, used for estimating the degree of randomness in the movement of the animals. These examples demonstrate the attractiveness of the RSP framework as a generic model to be used in diverse applications. randomized shortest paths; random walk; shortest path; parameter estimation; maximum likelihood; animal movement modellingpublishedVersio

Maximum likelihood estimation for randomized shortest paths with trajectory data

Randomized shortest paths (RSPs) are tool developed in recent years for different graph and network analysis applications, such as modelling movement or flow in networks. In essence, the RSP framework considers the temperature-dependent Gibbs–Boltzmann distribution over paths in the network. At low temperatures, the distribution focuses solely on the shortest or least-cost paths, while with increasing temperature, the distribution spreads over random walks on the network. Many relevant quantities can be computed conveniently from this distribution, and these often generalize traditional network measures in a sensible way. However, when modelling real phenomena with RSPs, one needs a principled way of estimating the parameters from data. In this work, we develop methods for computing the maximum likelihood estimate of the model parameters, with focus on the temperature parameter, when modelling phenomena based on movement, flow or spreading processes. We test the validity of the derived methods with trajectories generated on artificial networks as well as with real data on the movement of wild reindeer in a geographic landscape, used for estimating the degree of randomness in the movement of the animals. These examples demonstrate the attractiveness of the RSP framework as a generic model to be used in diverse applications. randomized shortest paths; random walk; shortest path; parameter estimation; maximum likelihood; animal movement modellingpublishedVersio

Caribou and reindeer migrations in the changing Arctic

Caribou and reindeer, Rangifer tarandus, are the most numerous and socio-ecologically important terrestrial species in the Arctic. Their migrations are directly and indirectly affected by the seasonal nature of the northernmost regions, human development and population size; all of which are impacted by climate change. We review the most critical drivers of Rangifer migration and how a rapidly changing Arctic may affect them. In order to conserve large Rangifer populations, they must be allowed free passage along their migratory routes to reach seasonal ranges. We also provide some pragmatic ideas to help conserve Rangifer migrations into the future. Barrier effect, Climate change, Connectivity, Conservation, Development, Mitigation, RangiferpublishedVersio

Accelerating advances in landscape connectivity modelling with the ConScape library

Increasingly precise spatial data (e.g. high-resolution imagery from remote sensing) allow for improved representations of the landscape network for assessing the combined effects of habitat loss and connectivity declines on biodiversity. However, evaluating large landscape networks presents a major computational challenge both in terms of working memory and computation time. We present the ConScape (i.e. “connected landscapes”) software library implemented in the high-performance open-source Julia language to compute metrics for connected habitat and movement flow on high-resolution landscapes. The combination of Julia's ‘just-in-time’ compiler, efficient algorithms and ‘landmarks’ to reduce the computational load allows ConScape to compute landscape ecological metrics—originally developed in metapopulation ecology (such as ‘metapopulation capacity’ and ‘probability of connectivity’)—for large landscapes. An additional major innovation in ConScape is the adoption of the randomized shortest paths framework to represent connectivity along the continuum from optimal to random movements, instead of only those extremes. We demonstrate ConScape's potential for using large datasets in sustainable land planning by modelling landscape connectivity based on remote-sensing data paired with GPS tracking of wild reindeer in Norway. To guide users, we discuss other applications, and provide a series of worked examples to showcase all ConScape's functionalities in Supplementary Material. Built by a team of ecologists, network scientists and software developers, ConScape is able to efficiently compute landscape metrics for high-resolution landscape representations to leverage the availability of large data for sustainable land use and biodiversity conservation. As a Julia implementation, ConScape combines computational efficiency with a transparent code base, which facilitates continued innovation through contributions from the rapidly growing community of landscape and connectivity modellers using Julia. circuitscape, conefor, ecological networks, least-cost path, metapopulation, random walk, randomized shortest pathspublishedVersio

‘You shall not pass!’: quantifying barrier permeability and proximity avoidance by animals

1. Impediments to animal movement are ubiquitous and vary widely in both scale and permeability. It is essential to understand how impediments alter ecological dynamics via their influence on animal behavioural strategies governing space use and, for anthropogenic features such as roads and fences, how to mitigate these effects to effectively manage species and landscapes.2. Here, we focused primarily on barriers to movement, which we define as features that cannot be circumnavigated but may be crossed. Responses to barriers will be influenced by the movement capabilities of the animal, its proximity to the barriers, and habitat preference. We developed a mechanistic modelling framework for simultaneously quantifying the permeability and proximity effects of barriers on habitat preference and movement.3. We used simulations based on our model to demonstrate how parameters on movement, habitat preference and barrier permeability can be estimated statistically. We then applied the model to a case study of road effects on wild mountain reindeer summer movements.4. This framework provided unbiased and precise parameter estimates across a range of strengths of preferences and barrier permeabilities. The quality of permeability estimates, however, was correlated with the number of times the barrier is crossed and the number of locations in proximity to barriers. In the case study we found that reindeer avoided areas near roads and that roads are semi-permeable barriers to movement. There was strong avoidance of roads extending up to c. 1 km for four of five animals, and having to cross roads reduced the probability of movement by 68·6% (range 3·5–99·5%).5. Human infrastructure has embedded within it the idea of networks: nodes connected by linear features such as roads, rail tracks, pipelines, fences and cables, many of which divide the landscape and limit animal movement. The unintended but potentially profound consequences of infrastructure on animals remain poorly understood. The rigorous framework for simultaneously quantifying movement, habitat preference and barrier permeability developed here begins to address this knowledge gap

Estimating and managing broad risk of chronic wasting disease spillover among cervid species

The management of infectious wildlife diseases often involves tackling pathogens that infect multiple host species. Chronic wasting disease (CWD) is a prion disease that can infect most cervid species. CWD was detected in reindeer (Rangifer tarandus) in Norway in 2016. Sympatric populations of red deer (Cervus elaphus) and moose (Alces alces) are at immediate risk. However, the estimation of spillover risk across species and implementation of multispecies management policies are rarely addressed for wildlife. Here, we estimated the broad risk of CWD spillover from reindeer to red deer and moose by quantifying the probability of co-occurrence based on both (1) population density and (2) habitat niche overlap from GPS data of all three species in Nordfjella, Norway. We describe the practical challenges faced when aiming to reduce the risk of spillover through a marked reduction in the population densities of moose and red deer using recreational hunters. This involves setting the population and harvest aims with uncertain information and how to achieve them. The niche overlap between reindeer and both moose and red deer was low overall but occurred seasonally. Migratory red deer had a moderate niche overlap with the CWD-infected reindeer population during the calving period, whereas moose had a moderate niche overlap during both calving and winter. Incorporating both habitat overlap and the population densities of the respective species into the quantification of co-occurrence allowed for more spatially targeted risk maps. An initial aim of a 50% reduction in abundance for the Nordfjella region was set, but only a moderate population decrease of less than 20% from 2016 to 2021 was achieved. Proactive management in the form of marked population reduction is invasive and unpopular when involving species of high societal value, and targeting efforts to zones with a high risk of spillover to limit adverse impacts and achieve wider societal acceptance is important. disease management, host range, moose, multihost pathogens, niche overlap, Norway, population estimation, red deer, reindeerpublishedVersio

Estimating the cumulative impact and zone of influence of anthropogenic features on biodiversity

1. The concept of cumulative impacts is widespread in policy documents, regulations and ecological studies, but quantification methods are still evolving. Infrastructure development usually takes place in landscapes with preexisting anthropogenic features. Typically, their impact is determined by computing the distance to the nearest feature only, thus ignoring the potential cumulative impacts of multiple features. We propose the cumulative ZOI approach to assess whether and to what extent anthropogenic features lead to cumulative impacts.2. The approach estimates both effect size and zone of influence (ZOI) of anthropogenic features and allows for estimation of cumulative effects of multiple features distributed in the landscape. First, we use simulations and an empirical study to understand under which circumstances cumulative impacts arise. Second, we demonstrate the approach by estimating the cumulative impacts of tourist infrastructure in Norway on the habitat of wild reindeer (Rangifer t. tarandus), a near-threatened species highly sensitive to anthropogenic disturbance.3. In the simulations, we showed that analyses based on the nearest feature and our cumulative approach are indistinguishable in two extreme cases: when features are few and scattered and their ZOI is small, and when features are clustered and their ZOI is large. The empirical analyses revealed cumulative impacts of private cabins and tourist resorts on reindeer, extending up to 10 and 20 km, with different decaying functions. Although the impact of an isolated private cabin was negligible, the cumulative impact of `cabin villages' could be much larger than that of a single large tourist resort. Focusing on the nearest feature only underestimates the impact of `cabin villages' on reindeer.4. The suggested approach allows us to quantify the magnitude and spatial extent of cumulative impacts of point, linear, and polygon features in a computationally efficient and flexible way and is implemented in the oneimpact R package. The formal framework offers the possibility to avoid widespread underestimations of anthropogenic impacts in ecological and impact assessment studies and can be applied to a wide range of spatial response variables, including habitat selection, population abundance, species richness and diversity, community dynamics and other ecological processes

Sustainable Development Goals and risks: The Yin and the Yang of the paths towards sustainability

The United Nations 2030 Agenda and Sustainable Development Goals (SDGs) define a path towards a sustainable future, but given that uncertainty characterises the outcomes of any SDG-related actions, risks in the implementation of the Agenda need to be addressed. At the same time, most risk assessments are narrowed to sectoral approaches and do not refer to SDGs. Here, on the basis of a literature review and workshops, it is analysed how SDGs and risks relate to each other’s in different communities. Then, it is formally demonstrated that, as soon as the mathematical definition of risks is broadened to embrace a more systemic perspective, acting to maintain socioenvironmental systems within their sustainability domain can be done by risk minimisation. This makes Sustainable Development Goals and risks ‘‘the Yin and the Yang of the paths towards sustainability’’. Eventually, the usefulness of the SDG-risk nexus for both sustainability and risk management is emphasized. 2030 Agenda Environmental risks Planet boundaries Risk quantification Sustainability science Systemic approachpublishedVersio