Biology subjects

Lindgren, F.

Publications and source records attributed to Lindgren, F..

8 recordsLinked to original sources

bioRxiv2026

Integrating dedicated and opportunistic surveys to estimate seasonal common dolphin density off mainland Portugal

1. Common dolphins (Delphinus delphis) are the most abundant cetaceans off mainland Portugal and a species of conservation and bycatch concern, so reliable abundance estimates are a management priority. Yet no single survey provides adequate spatiotemporal coverage: dedicated programmes offer sparse snapshots, while opportunistic programmes are spatially constrained by their routes. Integrating them is methodologically demanding, and the existing methods that do so in a single model estimate the density of animal groups, not of the individual animals that management often requires. 2. We develop a spatiotemporal marked log-Gaussian Cox process that integrates four structurally distinct surveys through a shared intensity surface, comprising environmental covariates, a latent spatial field, and survey-specific detection functions. Group size enters as a zero-truncated negative binomial mark with its own spatiotemporal field. We apply it to a dedicated aerial survey and three opportunistic ship-based programmes off mainland Portugal, comprising 162,514 km of on-effort transects and 1,756 sightings of 17,000 animals. We fit the model in a Bayesian framework with integrated nested Laplace approximations, yielding seasonally resolved group and animal density surfaces across the Portuguese Exclusive Economic Zone, and abundance estimates with fully propagated uncertainty. 3. Predicted animal density concentrated over the shelf and upper slope in every season, peaking northwest, and increased in shallower, cooler, more productive waters and over steeper seabed. Abundance nearly doubled from summer (77,000 [95% credible interval: 60,000, 98,000]) to winter (146,000 [99,000, 208,000]). Seasons shared 56% of the spatial field's variance, so effort from any programme in any season informed all others, and the seasonal contrast remained estimable despite uneven coverage. 4. Synthesis and applications. Our framework allows managers to combine dedicated surveys with existing platforms of opportunity to estimate animal density by season. Because group size is modelled explicitly, densities are estimated in animals rather than groups, as required for bycatch risk assessment and marine spatial planning. Because each survey enters through its own observation model, the approach accommodates programmes that differ in platform and protocol and transfers to any species or region where several surveys each cover part of a population.

ecology↗

bioRxiv2025

Spatiotemporal data integration for marine megafauna SDMs in dynamic environments: A point process approach

Species distribution models (SDMs) are widely used to predict where and when species occur, but the data available to fit them often resolve space and time unevenly. Designed surveys, such as line-transect distance-sampling surveys, provide structured spatial coverage and the detection information needed to estimate detectability, but logistical and funding constraints often limit them to short, infrequent periods. Opportunistic or citizen-science records are collected more frequently across the year, but from areas shaped by observer access and interest rather than survey design, and usually without comparable detection information. As a result, models built from either source alone tend to capture spatial structure or temporal dynamics well, but not both, limiting predictions for mobile species in dynamic environments. Here, we present a general framework, based on a log-Gaussian Cox process (LGCP) fitted using integrated nested Laplace approximations (INLA), that links multiple data sources to a shared spatiotemporal measure of species density through source-specific observation models: a detection function for survey transects, and spatial constraints reflecting the footprint of opportunistic effort. We apply the framework to cetacean line-transect surveys and whale-watching records. In a simulation with one month of survey data and twelve months of opportunistic data, the integrated model recovered spatiotemporal distribution patterns well (median correlation with the true pattern = 0.91) and estimated monthly abundance with a mean error of +10.7%, outperforming single-source models. In a case study of common dolphins (Delphinus delphis) off mainland Portugal, integration improved the spatial accuracy of predictions, although weak covariate effects limited the temporal variation the model could resolve. These results show that integration adds the most value when the two data sources resolve complementary aspects of distribution, in this case broad spatial structure from the survey and temporal replication from whale-watching, while temporal predictions remain limited when covariates carry weak seasonal signal. The framework is general and can be extended to other taxa, systems, and combinations of structured and opportunistic data by substituting the relevant observation models.

ecology↗

bioRxiv2025

Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle

BackgroundSmallholder dairy production systems in low-and middle-income countries are characterised by large phenotypic variance due to diverse environmental effects, farming practices, and crossbreeding. Furthermore, small herds, low genetic connectedness, and limited data recording challenge accurate separation of environmental and genetic effect in such settings, limiting genetic improvement. Here, we evaluated the impact of modelling spatial variation between herds to address these challenges and improve the accuracy of genomic evaluation for Tanzanian smallholder dairy cattle. ResultsWe analysed 19,375 test-day milk yield records of 1894 dairy cows from 1386 herds across four distinct geographical regions in Tanzania. The cows had 664,822 SNP marker genotypes after quality control and were highly admixed. We fitted a series of GBLUP models to evaluate the impact of modelling the herd effect and the spatial effect on. The herd effect was fitted as an independent random effect, while the spatial effect was fitted as a random effect with Euclidean distance-based Matern covariance function. The models were compared based on: model fit; estimates of variance components and breeding values; correlations between the estimated contribution of breeding values, herd effect, and spatial effect to phenotype values; and the accuracy of phenotype prediction in cross-validation and forward validation. The results showed large differences in milk yield between and within regions, as well as significant variation due to the spatial effect, which were not fully captured by modelling the herd effect. The results also strongly indicate that a model with just the herd effect underestimated breeding values of animals in less favourable environments and overestimated breeding values of animals in more favourable environments. ConclusionsThis study demonstrated the challenge of achieving accurate genomic evaluation in smallholder settings. By leveraging spatial modelling we maximised the use of available data and improved the separation of genetic and environmental effects. Further work is required to improve smallholder genetic evaluations by understanding environmental and genetic processes that drive the large phenotypic variance in African smallholder setting.

genomics↗

bioRxiv2023

Inference of population genetics parameters using discriminator neural networks: an adversarial Monte Carlo approach

Accurately estimating biological variables of interest, such as parameters of demographic models, is a key problem in evolutionary genetics. Likelihood-based and likelihood-free methods both typically use only limited genetic information, such as carefully chosen summary statistics. Deep convolutional neural networks (CNNs) trained on genotype matrices can incorporate a great deal more information, and have been shown to have high accuracy for inferring parameters such as recombination rates and population sizes, when evaluated using simulations. However these methods are typically framed as regression or classification problems, and it is not straightforward to ensure that the training data adequately model the empirical data on which they are subsequently applied. It has recently been shown that generative adversarial networks (GANs) can be used to iteratively tune parameter values until simulations match a given target dataset. Here, we investigate an adversarial architecture for discriminator-based inference, which iteratively improves the sampling distribution for training the discriminator CNN via Monte Carlo density estimation. We show that this method produces parameter estimates with excellent agreement to simulated data. We developed dinf, a modular Python package for discriminator-based inference that incorporates this method, and is available from https://github.com/RacimoLab/dinf/.

evolutionary biology↗

bioRxiv2023

Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.

Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, knowledge of the true number and locations of vampire bat colonies precludes informed allocation of measures intended to limit lethal rabies spillover to humans and livestock. We inferred the complete spatial distribution of vampire bat roosts while accounting for observation effort and environmental covariates by fitting a Log Gaussian Cox Process model to the locations of 563 roosts in three regions of Peru. Our model explained 47% of the variance in the observed roost distribution and identified landscape correlates of roost establishment. Our model estimated that 1,795 roosts (76%) remain undiscovered and identified hotspots of undetected roosts in currently rabies-free areas, implying high risk for viral incursion. Incorporating the locations of undetected roosts improved spatial predictions of rabies spillover to livestock, revealed areas with disproportionate underreporting to surveillance systems, and indicated a higher rabies burden than previously estimated. We provide a robust approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis.

ecology↗

bioRxiv2023

Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects

O_LIStep selection analysis (SSA) is a common framework for understanding animal movement and resource selection using telemetry data. Such data are, however, inherently autocorrelated in space, a complication that could impact SSA-based inference if left unaddressed. Accounting for spatial correlation is standard statistical practice when analyzing spatial data, and its importance is increasingly recognized in ecological models (e.g., species distribution models). Nonetheless, no framework yet exists to account for such correlation when analyzing animal movement using SSA. C_LIO_LIHere, we extend the popular method Integrated Step Selection Analysis (iSSA) by including a Gaussian Field (GF) in the linear predictor to account for spatial correlation. For this, we use the Bayesian framework R-INLA and the Stochastic Partial Differential Equations (SPDE) technique. C_LIO_LIWe show through a simulation study that our method provides unbiased fixed effects estimates, quantifies their uncertainty well and improves the predictions. In addition, we demonstrate the practical utility of our method by applying it to three wolverine (Gulo gulo) tracks. C_LIO_LIOur method solves the problems of assuming spatially independent locations in the SSA framework. In addition, it offers new possibilities for making long-term predictions of habitat usage. C_LI

animal behavior and cognition↗

bioRxiv2022

Optimisation of the core subset for the APY approximation of genomic relationships

BackgroundBy entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have have been proposed to address this challenge, like the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of genomic relationship matrix. The partitioning into subsets is often done at random. While APY is a good approximation of the full model, the random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. MethodsWe derived a novel algorithm for optimising the core subset based on the conditional genomic relationship matrix or the conditional SNP genotype matrix. We compared accuracy of genomic predictions with different core subsets on simulated and real pig data. The core subsets were constructed (1) at random, (2) based on the diagonal of genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we have visualised population structure of genotyped animals with the linear Principal Component Analysis and the non-linear Uniform Manifold Approximation and Projection. ResultsAll core subset constructions performed equally well when the number of core animals captured most of variation in genomic relationships, both in simulated and real data. When the number of core animals was not optimal, there was substantial variability in results with the random construction and no variability with the conditional construction. Visualisation of population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. ConclusionsOur results confirm that the size of the core subset in APY is critical. The results further show that the core subset can be optimised with the conditional algorithm that achieves a good and repeatable spread of core animals across the domain of genotyped animals.

genetics↗

bioRxiv2020

Hierarchical modeling of haplotype effects based on a phylogeny

This paper introduces a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are usually many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially - few haplotypes have high frequency and many haplotypes have low frequency. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations and leveraging these similarities can improve the estimation of haplotype effects. There is extensive literature on this topic. Here we build on these observations and develop an autoregressive model of order one that hierarchically models haplotype effects by leveraging phylogenetic relationships between the haplotypes described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network and we therefore refer to the model as the haplotype network model. The haplotype network model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. The key contribution of this work is that by leveraging the haplotype network structure we obtain a sparse model and by using hierarchical autoregression the flow of information between similar haplotypes is estimated from the data. We show with a simulation study that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially when there are few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a case study of modeling the effect of mitochondrial haplotypes on milk yield in cattle.

genomics↗