Inference from gene to population : propagating uncertainty in estimates of population characteristics through ecological scales

A current trend in population biology is the increasing realisation of the effect of individual variability on some of the big patterns of population dynamics. Simultaneously, the field of population genetics continues to develop a sophisticated theoretical basis for the inference of large-scale population dynamics from information derived from the smallest ecological unit, that of the gene. This thesis aims to contribute to the synthesis of these two fields by outlining a series of novel methods that can be used in the scaling up of genetic information to individual dynamics, and, eventually, to inference of patterns of the population. A critical feature of the methods described here is the preservation and propagation of uncertainty in estimates at each stage of the analysis. The thesis begins by introducing an estimation procedure for the calculation of allele frequencies when observation error means that frequencies cannot be directly observed. Genotyping errors can also prove troublesome in the field of parentage analysis, the basis of many models of inference of population-level processes. Any assignment errors made at this stage can be disastrous for any inference build upon these assignments. I describe a novel method of conducting parentage analysis, extend these methods for a series of common marker types and arbitrary ploidy, and show how uncertainty in parentage allocations can be propagated robustly to further stages of analysis. I review a set of new methods that may prove useful for the fitting of individual-based models to real data. I describe how these methods can be applied in the context of individual-based modelling and describe an extension of the methods to efficiently handle common data used to parametrise individual-based models. I discuss that individual-based models may provide a key bridging discipline between the field of traditional population ecology and population genetics. Finally, I describe a method to use information on dispersal collected at the individual-level to inform population-level estimate of immigration and emigration rates of spatially-explicit models of population dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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

On the Inadequacy of Species Distribution Models for Modelling the Spread of SARS-CoV-2: Response to Araújo and Naimi

The ongoing pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing significant damage to public health and economic livelihoods, and is putting significant strains on healthcare services globally. This unfolding emergency has prompted the preparation and dissemination of the article “Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate” by Araújo and Naimi (2020). The authors present the results of an ensemble forecast made from a suite of species distribution models (SDMs), where they attempt to predict the suitability of the climate for the spread of SARS-CoV-2 over the coming months. They argue that climate is likely to be a primary regulator for the spread of the infection and that people in warm-temperate and cold climates are more vulnerable than those in tropical and arid climates. A central finding of their study is that the possibility of a synchronous global pandemic of SARS-CoV-2 is unlikely. Whilst we understand that the motivations behind producing such work are grounded in trying to be helpful, we demonstrate here that there are clear conceptual and methodological deficiencies with their study that render their results and conclusions invalid. What follows is a response to the Araújo and Naimi article centered around three main criticisms: 1) Given the fact that SARS-CoV-2 has a primary infection pathway of direct contact, it is in an active spreading phase, and remains largely underreported in the Global South, it represents an inappropriate system for analysis using the SDM framework. 2) Even if we were to accept that an SDM framework would be applicable here, the methodology presented in the article strays far from best-practice guidelines for the application of SDMs. 3) The dissemination strategy of the authors failed to respect the frameworks of risks adhered to in other academic disciplines pertaining to public health, resulting in erroneous but well-publicised claims with broad policy implications before any scientific oversight could be applied

An Updated Algorithm for the Generation of Neutral Landscapes by Spectral Synthesis

Background: Patterns that arise from an ecological process can be driven as much from the landscape over which the process is run as it is by some intrinsic properties of the process itself. The disentanglement of these effects is aided if it possible to run models of the process over artificial landscapes with controllable spatial properties. A number of different methods for the generation of so-called ‘neutral landscapes’ have been developed to provide just such a tool. Of these methods, a particular class that simulate fractional Brownian motion have shown particular promise. The existing methods of simulating fractional Brownian motion suffer from a number of problems however: they are often not easily generalisable to an arbitrary number of dimensions and produce outputs that can exhibit some undesirable artefacts. Methodology: We describe here an updated algorithm for the generation of neutral landscapes by fractional Brownian motion that do not display such undesirable properties. Using Monte Carlo simulation we assess the anisotropic properties of landscapes generated using the new algorithm described in this paper and compare it against a popular benchmark algorithm. Conclusion/Significance: The results show that the existing algorithm creates landscapes with values strongly correlated in the diagonal direction and that the new algorithm presented here corrects this artefact. A number of extensions of the algorithm described here are also highlighted: we describe how the algorithm can be employed to generate landscapes that display different properties in different dimensions and how they can be combined with an environmental gradient to produce landscapes that combine environmental variation at the local and macro scales

Indeterminacy of Reverse Engineering of Gene Regulatory Networks: The Curse of Gene Elasticity

Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. A number of reverse engineering approaches have been developed to help uncover the regulatory networks giving rise to the observed gene expression profiles. However, this is an overspecified problem due to the fact that more than one genotype (network wiring) can give rise to the same phenotype. We refer to this phenomenon as “gene elasticity.” In this work, we study the effect of this particular problem on the pure, data-driven inference of gene regulatory networks.We simulated a four-gene network in order to produce “data” (protein levels) that we use in lieu of real experimental data. We then optimized the network connections between the four genes with a view to obtain the original network that gave rise to the data. We did this for two different cases: one in which only the network connections were optimized and the other in which both the network connections as well as the kinetic parameters (given as reaction probabilities in our case) were estimated. We observed that multiple genotypes gave rise to very similar protein levels. Statistical experimentation indicates that it is impossible to differentiate between the different networks on the basis of both equilibrium as well as dynamic data.We show explicitly that reverse engineering of GRNs from pure expression data is an indeterminate problem. Our results suggest the unsuitability of an inferential, purely data-driven approach for the reverse engineering transcriptional networks in the case of gene regulatory networks displaying a certain level of complexity

Inference From Gene to Population: Propagating Uncertainty In Estimates of Population Characteristics Through Ecological Scales

A current trend in population biology is the increasing realisation of the effect of individual variability on some of the big patterns of population dynamics. Simultaneously, the field of population genetics continues to develop a sophisticated theoretical basis for the inference of large-scale population dynamics from information derived from the smallest ecological unit, that of the gene. This thesis aims to contribute to the synthesis of these two fields by outlining a series of novel methods that can be used in the scaling up of genetic information to individual dynamics, and, eventually, to inference of patterns of the population. A critical feature of the methods described here is the preservation and propagation of uncertainty in estimates at each stage of the analysis. The thesis begins by introducing an estimation procedure for the calculation of allele frequencies when observation error means that frequencies cannot be directly observed. Genotyping errors can also prove troublesome in the field of parentage analysis, the basis of many models of inference of population-level processes. Any assignment errors made at this stage can be disastrous for any inference build upon these assignments. I describe a novel method of conducting parentage analysis, extend these methods for a series of common marker types and arbitrary ploidy, and show how uncertainty in parentage allocations can be propagated robustly to further stages of analysis. I review a set of new methods that may prove useful for the fitting of individual-based models to real data. I describe how these methods can be applied in the context of individual-based modelling and describe an extension of the methods to efficiently handle common data used to parametrise individual-based models. I discuss that individual-based models may provide a key bridging discipline between the field of traditional population ecology and population genetics. Finally, I describe a method to use information on dispersal collected at the individual-level to inform population-level estimate of immigration and emigration rates of spatially-explicit models of population dynamics

Inference from gene to population : propagating uncertainty in estimates of popuation

A current trend in population biology is the increasing realisation of the effect of individual variability on some of the big patterns of population dynamics. Simultaneously, the field of population genetics continues to develop a sophisticated theoretical basis for the inference of large-scale population dynamics from information derived from the smallest ecological unit, that of the gene. This thesis aims to contribute to the synthesis of these two fields by outlining a series of novel methods that can be used in the scaling up of genetic information to individual dynamics, and, eventually, to inference of patterns of the population. A critical feature of the methods described here is the preservation and propagation of uncertainty in estimates at each stage of the analysis. The thesis begins by introducing an estimation procedure for the calculation of allele frequencies when observation error means that frequencies cannot be directly observed. Genotyping errors can also prove troublesome in the field of parentage analysis, the basis of many models of inference of population-level processes. Any assignment errors made at this stage can be disastrous for any inference build upon these assignments. I describe a novel method of conducting parentage analysis, extend these methods for a series of common marker types and arbitrary ploidy, and show how uncertainty in parentage allocations can be propagated robustly to further stages of analysis. I review a set of new methods that may prove useful for the fitting of individual-based models to real data. I describe how these methods can be applied in the context of individual-based modelling and describe an extension of the methods to efficiently handle common data used to parametrise individual-based models. I discuss that individual-based models may provide a key bridging discipline between the field of traditional population ecology and population genetics. Finally, I describe a method to use information on dispersal collected at the individual-level to inform population-level estimate of immigration and emigration rates of spatially-explicit models of population dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Consequences of ignoring group association in spatial capture–recapture analysis

publishedVersio

RovQuant : estimating density, abundance and population dynamics of bears, wolverines and wolves in Scandinavia

Background Reliable estimates of population status are a pre-requisite for informed wildlife management. However, abundance estimates can be challenging to obtain, especially for species that are highly mobile, rare and elusive. For nearly two decades, management agencies in Norway and Sweden have been monitoring populations of three large carnivores – brown bear (Ursus ursus), wolf (Canis lupus), and wolverine (Gulo gulo) – using non-invasive genetic sampling (NGS). DNA extracted from fæces, urine, and hair can be used to identify the species, sex, and individual from which each sample originated. Samples thus become evidence of the presence of an individual carnivore in space and time. Project RovQuant was initiated in 2017 with the objective to develop statistical methods that allow a comprehensive assessment of population status and dynamics using NGS data and other sources of information collected by the national monitoring programs in Sweden and Norway. Approach We developed a Bayesian open-population spatial capture-recapture (OPSCR) model that, using a combination of NGS and recoveries of dead carnivores, jointly estimates 1) the spatial variability in the probability of genetic detection, 2) the spatial distribution and interannual movements of individuals and 3) population size and dynamics. We fitted this model to the extensive individual-based monitoring data for bears, wolverines, and wolves, which had been compiled in the Scandinavian large carnivore database Rovbase 3.0 between 2012 and 2019. Results The OPSCR model yielded annual density maps both total and jurisdiction-specific population sizes for each species. The estimated number of bears for April 1, 2018 was 2 757 (95% credible interval, CrI: 2 636 - 2 877), of which 2 615 (CrI: 2 499-2 732) were located in Sweden and 142 (CrI: 124-162) in Norway. The estimated number of wolverines for December 1, 2018 was 1 035 (CrI: 985 - 1 088), of which 660 (CrI: 619-703) were located in Sweden and 375 (CrI: 353-397) in Norway. The estimated number of wolves for October 1, 2018 was 375 wolves (CrI: 352 - 402), of which 297 (CrI: 274-322) were located in Sweden and 79 (CrI: 72-86) in Norway. In addition to density and abundance estimates, the OPSCR models also yielded estimates of survival, recruitment, and space use parameters for each species. Six additional tasks linked to the development of OPSCR model were implemented as either prerequisite technical developments or to address persistent challenges in monitoring and management of large carnivores in Scandinavia. Although this report focuses on the main results from the OPSCR model, findings related to these additional tasks are briefly described as well. Conclusions The unique Scandinavian data set combined with a novel OPSCR model allowed RovQuant to quantify the population status of three large carnivore species at an unprecedented spatial scale (up to 593 000 km2). The approach used here has several advantages over proxy-based approaches for obtaining estimates of population size. The OPSCR model directly estimates annual abundance from NGS and dead recovery data while accounting for spatial and temporal variation in detection probability of individuals. The resulting estimates are spatially explicit, allowing extraction of abundance estimates and associated measures of uncertainty for any spatial extent desired by the user (e.g. management unit). Annual cause-specific mortality and recruitment are also estimated, which are both useful metrics of the population’s status and trajectory. Importantly, this approach efficiently exploits the data (NGS and dead recoveries) currently collected annually by Swedish and Norwegian management authorities at the population level. Although the OPSCR model has been extensively tested, it constitutes a novel approach and is still under development. The ability of the model to produce trustworthy estimates relies on several statistical assumptions and on the suitability of the input data. For example, although the model was able to produce annual density maps and abundance estimates for bears throughout Scandinavia, the current patchy sampling for this species in Sweden means that confidence in the reliability of the results for bear is substantially lower than for the other two species. We discuss the strengths and limitations of our approach and suggest areas for further study and development in order to increase the reliability of the OPSCR model and the cost-efficiency of large carnivore monitoring in Scandinavia.Bakgrunn Gode estimater på populasjonsstatus er en forutsetning for en kunnskapsbasert viltforvaltning. Til tross for det, kan estimater på antall dyr være utfordrende å skaffe til veie, spesielt for arter som beveger seg over store avstander, er fåtallige og vanskelig å påvise. I nesten to tiår har forvaltningsmyndighetene i Norge og Sverige overvåket bestandene av tre store rovdyr arter – brun bjørn (Ursus ursus), ulv (Canis lupus) og jerv (Gulo gulo) – ved bruk av ikke-invasiv genetisk prøveinnsamling (NGS). DNA fra skit, urin og hår kan brukes til å identifisere art, kjønn og individ fra hver enkelt prøve. Prøvene blir således et bevis på tilstedeværelsen av et rovdyrindivid i tid og rom. Prosjektet RovQvant ble igangsatt i 2017, med et formål om å utvikle statistiske metoder som gjør det mulig å foreta en omfattende vurdering av bestandsstatus og -dynamikk ved bruk av NGS-data og andre informasjonskilder innsamlet gjennom de nasjonale overvåkingsprogrammene på store rovdyr i Sverige og Norge. Tilnærming Vi utviklet en Bayesiansk åpen romlig fangst-gjenfangst populasjons modell (OPSCR) som benytter en kombinasjon av NGS-data og gjenfunn av døde rovdyr. Modellen estimerer 1) den romlige fordelingen av den genetiske oppdagbarhetssannsynligheten, 2) den romlige fordelingen og mellomårs bevegelsene til individene, og 3) bestandsstørrelsen og -dynamikken. Vi tilpasset modellen til de omfattende individbaserte overvåkingsdatasettene på bjørn, ulv og jerv, som har vært innsamlet og ivaretatt i den skandinaviske databasen for store rovdyr (Rovbase 3.0) mellom 2012 og 2019. Resultater OPSCR-modellen gav årlige kart med tetthet for den enkelte art hvor bestandsstørrelsen både totalt og innenfor ulike administrative enheter kunne avledes. Det estimerte antallet bjørner 1. april 2018 var 2 758 (CrI: 2 636 - 2 877), hvorav 2 615 (CrI: 2 499-2 732) var i Sverige og 142 (CrI: 124-162) i Norge. Det estimerte antallet jerver 1. desember 2018 var 1 035 (CrI: 985 - 1 088), hvorav 660 (CrI: 619-703) var i Sverige og 375 (CrI: 353-397) i Norge. Det estimerte antallet ulver 1. oktober 2018 var 375 (CrI: 352 - 402), hvorav 297 (CrI: 274-322) var i Sverige og 79 (CrI: 72-86) i Norge. I tillegg til estimater på tetthet og antall gav OPSCR-modellen også estimater på årlig overlevelse, rekrutering og arealbruk parametere. Seks tilleggsoppgaver, knyttet til utviklingen av OPSCR-modellen, ble iverksatt enten som nødvendig teknisk utvikling eller for å adressere eksisterende utfordringer i overvåkingen og forvaltningen av store rovdyr i Skandinavia. Selv om denne rapporten fokuserer på hovedresultatene fra OPSCR-modellen er også funnene knyttet til disse tilleggsoppgavene kort beskrevet. Konklusjoner Det unike skandinaviske datasettet, kombinert med en helt ny OPSCR-modell, har gjort RovQvant i stand til å kvantifisere populasjonsstatusen til tre arter av store rovdyr på en romlig skala som savner sidestykke (opp til 593 000 km2). Tilnærmingen som er brukt har flere fordeler når man skal fremskaffe estimater på bestandsstørrelse, fremfor indirekte tilnærminger. OPSCR-modellen estimerer antall dyr direkte fra NGS-data og gjenfunn av døde rovdyr, samtidig som den tar hensyn til individenes sannsynlighet for å påvises i tid og rom. Estimatene fra modellen er romlig relatert, muliggjør ekstraksjon av bestandsestimater og tilhørende usikkerhet for en hvilken som helst geografisk enhet som brukeren ønsker (f. eks. nasjonalt nivå eller en forvaltningsenhet). Årlig årsaksspesifikk dødelighet og rekrutering blir også estimert, begge nyttig informasjon om bestandsstatus og -utvikling. Denne tilnærmingen utnytter effektivt data (NGS og gjenfangst av døde rovdyr) som i dag samles inn årlig både i Sverige og Norge på populasjonsnivå. Selv om OPSCR-modellen har blitt omfattende testet så utgjør den en helt ny tilnærming som fortsatt er under utvikling. Evnen som modellen har til å produsere sikre estimater avhenger av flere statistiske antagelser og egnetheten til de data som puttes inn i den. For eksempel, selv om OPSCR-modellen var i stand til å produsere årlige tetthetskart og bestandsestimater for bjørn i hele Skandinavia, så gjør dagens flekkvise overvåkingsdesign for bjørn i Sverige at tillitten til sikkerheten i resultatene for bjørn er vesentlig lavere enn for de to andre artene. Vi diskuterer styrkene og svakhetene ved vår tilnærming, og foreslår områder for videre utforsking og utvikling for å øke sikkerheten til OPSCR-modellen og få en kostnadseffektiv overvåking av store rovdyr i Skandinavia