Utvikling av ikke-invasiv overvåking av rovdyr ved hjelp av hierarkiske modeller

The development of non-invasive approaches for monitoring wildlife populations made it feasible to obtain ecological parameters across landscapes and populations, rather than a few locations or individuals. The two most popular and widespread non-invasive monitoring methods are camera trapping and genetic sampling. The technical development associated with data collection has been impressive, whilst analytical capabilities have lagged behind. Only recently are we getting close to exploiting the potentials of non-invasively obtained data. The objective of my thesis is to apply modern hierarchical analytical models to several sets of carnivore monitoring data to address a series of conceptually and methodologically connected problems, faced by applied ecologists. The thesis consists of four articles. Two of these include simulations, and all four articles involve model fitting and case studies. The latter target a range of species including wolverine and mesocarnivores in Scandinavia and the Himalayan brown bear. Article I quantifies detectability of mesocarnivores by camera traps and sheds light on the behavioural responses of focal species to detection devices and to olfactory lures as an important aspect of detectability. Article II incorporates multiple data sources with varying levels of information in a data-sparse situation and introduces a multiple observation process model in the spatial capture-recapture framework to estimate population parameters. This model is applied to multi-method monitoring data of a Himalayan brown bear population in Pakistan. The focus in Article III is heterogeneity in the environment and it uncovers sex-specific patterns in wolverine home range size across the species’ range in Norway using solely non-invasively collected DNA data and spatial capture-recapture models. Article IV presents and evaluates an extension of the open-population spatial capture-recapture model to improve inferences on population parameters and showcases its application on wolverine data in central Norway. Hierarchical modelling offers ecologists an intuitive multi-level approach to disentangle observation and ecological processes. All chapters of this thesis include hierarchical models that account for imperfect detection. Depending on the research question, I use these models to estimate time-to-detection of species, population abundance and density, survival, variation in home range size and inter-annual movement. The monitoring methods used during this thesis are often applied to studies of rare or elusive species and data sparsity is another important challenge addressed in this thesis. Bayesian inference Using Gibbs Sampling (BUGS) language facilitates the construction of flexible models that make the incorporation of multiple types of data into one comprehensive analysis comparatively straightforward. The articles included in this thesis showcase how hierarchical models help us use non-invasively collected data to yield answers to a range of questions in applied ecology. Tackling the associated challenges increases our ability to draw inferences that more closely describe the complexity of real-world ecological systems.Utviklingen av ikke-invasive metoder for å overvåke dyrepopulasjoner har gjort det mulig å estimere økologiske parametere på tvers av landskap og populasjoner, snarere enn noen få steder eller individer. De to mest populære og utbredte ikkeinvasive overvåkingsmetodene er viltkameraer og genetisk prøvetaking. Den tekniske utviklingen knyttet til datainnsamling har vært imponerende, mens analytiske evner har hengt etter. Først nylig har vi kommet i nærheten av å utnytte potensialet til ikke-invasivt innsamlede data. Målet med avhandlingen min er å bruke moderne hierarkiske analytiske modeller på flere sett med overvåkningsdata av rovdyr for å adressere en serie konseptuelt og metodisk koblede problemer, som anvendte økologer møter. Oppgaven består av fire artikler. To av disse inkluderer simuleringer, og alle de fire artiklene involverer modelltilpassing og case-studier på en rekke arter, inkludert jerv og mesokarnivorer i Skandinavia og Himalaya brunbjørn.publishedVersio

Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring

[EN] The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the world’s largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimesSIThis study was funded by the Norwegian Environment Agency (Miljødirektoratet), the Swedish Environmental Protection Agency (Naturvårdsverket), the Research Council of Norway (NFR 286886; project WildMap), and the Peder Sather Gran

Environmental variability across space and time drives the recolonization pattern of a historically persecuted large carnivore

Wildlife populations are not static. Intrinsic and extrinsic factors affect individuals, which lead to spatiotemporal variation in population density and range. Yet, dynamics in density and their drivers are rarely documented, due in part to the inherent difficulty of studying long-term population-level phenomena at ecologically meaningful scales. We studied the spatiotemporal density dynamics in a recolonizing large carnivore population, the wolverine Gulo gulo, across the Scandinavian Peninsula over nine years. We fitted open-population spatial capture-recapture models to noninvasive genetic sampling data collected across Norway and Sweden to estimate annual density surfaces and their drivers. This approach allowed us to model sex-specific changes in wolverine density and the effect of landscape-level environmental determinants over time. Our results revealed that, as wolverines successfully recolonized many parts of their historical range in Scandinavia, the relationship with spatial determinants of density has changed over time. We also found support for sex-specific responses of the Scandinavian wolverine to the environmental determinants of density and differences in the temporal dynamics of their relationships, indicating disproportionate recolonization ability and anthropogenic pressures. We observed significant changes in the relationship of female wolverine density with several determinants during the study period, suggesting still ongoing expansion of female wolverines whereas males might have already reached the range limits. These findings show that the Scandinavian wolverine population is still recovering from centuries of persecution and severe range contraction. Our study sheds light on the dynamics and challenges of recolonizing large carnivores in human-dominated landscapes across time and space

Environmental variability across space and time drives the recolonization pattern of a historically persecuted large carnivore

Wildlife populations are not static. Intrinsic and extrinsic factors affect individuals, which lead to spatiotemporal variation in population density and range. Yet, dynamics in density and their drivers are rarely documented, due in part to the inherent difficulty of studying long-term population-level phenomena at ecologically meaningful scales. We studied the spatiotemporal density dynamics in a recolonizing large carnivore population, the wolverine Gulo gulo, across the Scandinavian Peninsula over nine years. We fitted open-population spatial capture-recapture models to noninvasive genetic sampling data collected across Norway and Sweden to estimate annual density surfaces and their drivers. This approach allowed us to model sex-specific changes in wolverine density and the effect of landscape-level environmental determinants over time. Our results revealed that, as wolverines successfully recolonized many parts of their historical range in Scandinavia, the relationship with spatial determinants of density has changed over time. We also found support for sex-specific responses of the Scandinavian wolverine to the environmental determinants of density and differences in the temporal dynamics of their relationships, indicating disproportionate recolonization ability and anthropogenic pressures. We observed significant changes in the relationship of female wolverine density with several determinants during the study period, suggesting still ongoing expansion of female wolverines whereas males might have already reached the range limits. These findings show that the Scandinavian wolverine population is still recovering from centuries of persecution and severe range contraction. Our study sheds light on the dynamics and challenges of recolonizing large carnivores in human-dominated landscapes across time and spacepublishedVersio

Comparison of methods for estimating density and population trends for low-density Asian bears

Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth (λ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species

A flexible and efficient Bayesian implementation of point process models for spatial capture‐recapture data

Spatial capture–recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway. area search, binomial point process, continuous sampling, NIMBLE, non-invasive genetic sampling, Poisson point process, spatial capture–recapture, wolverinepublishedVersio

Analysis of a communication satellite for lunar far-side exploration

Analysis of communication satellite for lunar far-side exploration relaying color television, voice, high-bit-rate telemetry data, and ranging code from command and service, and lunar module

Assessment of the residential Finnish wolf population combines DNA captures, citizen observations and mortality data using a Bayesian state-space model

Assessment of the Finnish wolf population relies on multiple sources of information. This paper describes how Bayesian inference is used to pool the information contained in different data sets (point observations, non-invasive genetics, known mortalities) for the estimation of the number of territories occupied by family packs and pairs. The output of the assessment model is a joint probability distribution, which describes current knowledge about the number of wolves within each territory. The joint distribution can be used to derive probability distributions for the total number of wolves in all territories and for the pack status within each territory. Most of the data set comprises of both voluntary-provided point observations and DNA samples provided by volunteers and research personnel. The new method reduces the role of expert judgement in the assessment process, providing increased transparency and repeatability

How do forest characteristics relate to brown bear (Ursus arctos) density?

Brown bear (Ursus arctos) is one of our most common large carnivores. The historic population in Sweden has been reduced due to conflicts with humans. The diet of the brown bear varies with the season and location. In the USA, brown bears feed more on salmon (Salmo salar) and trout (Salmo trutta), while in Sweden it is more reindeer (Rangifer tarandus) and moose (Alces alces) during the spring which interferes with reindeer husbandry. Bilberry (Vaccinium myrtilus), and lingonberry (Vaccinium vitis ideae) is the most important food resource during the late summer and fall, occasionally the bears also feed on ants. Bears occur in boreal forests and females select home ranges that provide good food resources. Before entering hibernation, bilberries are one of the most important food resources and are heavily affected by forest management, the thinning phase, and mature forests that are ready to be clear-cut. After a clear-cut, the bilberry is greatly decreased but starts to recover as the forest grows and the canopy closes. When the forest becomes too dense the bilberry stops growing but it starts to increase again after thinning. This study investigated how the forest characteristics are related to the bear density since the bilberry abundance changes during one management cycle of the forest and bilberry is one of the most important food recourses. Two linear mixed models (lme) were created with bear density as a response variable. Model one contained mean height, basal area, and age. The second model contained the seven groups of field layers (bilberry, lingonberry, poor, grass, herbs, crowberry, and no field layer) as explanatory variables. The bear density data was estimated from spatial capture-recapture surveys based on DNA from feces collected during fall, the forest data were obtained through the NFI (National Forest Inventory). The results show that age was positively correlated with the bear density. This could be since bilberry is heavily affected by clear-cutting and takes years to recover. The basal area and mean height, on the other hand, had a negative correlation with bear density. This could be since bilberry is favored by forests that are not too dense but have a basal area of around 30-40 m2 /ha and a canopy openness of 50%. Furthermore, both bilberry and lingonberry abundance peak at a lower forest height, bilberries around 15 meters and lingonberries around 0 meters. There was no difference between the field layers, except the No field layer which gave a lower bear density compared to bilberry

PD operads and explicit partition lie algebras

Infinitesimal deformations are governed by partition Lie algebras. In characteristic 0, these higher categorical structures are modelled by differential graded Lie algebras, but in characteristic p, they are more subtle. We give explicit models for partition Lie algebras over general coherent rings, both in the setting of spectral and derived algebraic geometry. For the spectral case, we refine operadic Koszul duality to a functor from operads to divided power operads, by taking ‘refined linear duals’ of Σn-representations. The derived case requires a further refinement of Koszul duality to a more genuine setting