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  The use of acoustic deterrent devices to mitigate fishery-seal conflict at the scale of an entire coastal bay

  Lehtonen, Topi K.; Lehtonen, Esa; Lehmonen, Roope
  Fisheries research (Elsevier, 2026)

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  Recovering seal populations are increasingly in conflict with aquaculture and fisheries. One potential approach to mitigate these conflicts is the use of seal deterrents, such as acoustic deterrent devices (ADDs). These devices are typically deployed near aquaculture pens or fishing gear where seal foraging is considered problematic. In the current multiyear study, we assessed the feasibility of creating a larger seal-restricted area by installing ADDs in narrow straits leading to a large (~40 km2) coastal bay. We found that fishers’ gillnets were subject to lower levels of seal-induced damage during years when ADDs were in use. However, seal-induced damage and seal sightings remained common within the bay even when the devices were operated. Catches of secondary target species seemed to respond positively to ADD deployment, whereas those of the main target species, the pikeperch (Sander lucioperca), did not significantly increase, suggesting a limited effect on the catches. Fishers’ perceptions of the use of ADDs varied but were generally positive. Overall, the results indicate that the tested approach provided only modest measurable benefits to the fishery within a large bay. One potential explanation to this finding is that the number of ADDs deployed may have been insufficient to more substantially limit seal movements into the area. The study also highlights the influence of local environmental conditions and other context-dependent factors on ADD effectiveness and the need for further research into the performance of different ADD configurations.

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  Using deregressed proofs to estimate multivariate variance components for traits originating from heterogeneous data sources

  Salaudeen, Ayo; Pitkänen, Timo; Kempe, Riitta; Koivula, Minna; Pösö, Jukka; Byskov, Kevin; Uimari, Pekka; Lidauer, Martin
  World Congress on Genetics Applied to Livestock Production Digital Archive : 1 (2026)

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  The aim of this study was to investigate the use of deregressed proofs (DRP) for estimating variance components (VC) for traits originating from heterogeneous data sources. Including a Saved Feed index into total merit index (TMI) improves feed efficiency, but TMI weights may need adjustment to avoid undesirable correlated responses. Updating TMI weights requires multivariate VC estimates for the correlated traits. Estimating all VCs in a single analysis may not be feasible when traits differ in data structure, whereas using DRP could offer a practical alternative. The traits considered were grouped into two field datasets (DAT1 and DAT2) and a research-farm dataset (DAT3). DAT1 included milk yield (MY), protein yield (PY), fat yield (FY), cow carcass weight (CCW), cow carcass conformation (CCC), cow carcass fatness (CCF), stature (ST), and metabolic body weight (MBW); DAT2 included bull daily carcass gain (DCG), bull carcass conformation (BCC) and bull carcass fatness (BCF); and DAT3 included MY, PY, FY, MBW, dry matter intake (DMI), and body weight gain (BWG). Observations were from 18,757 cows, 45,514 bull calves and 898 cows for DAT1, DAT2, and DAT3, respectively. After pruning, pedigree sizes were 172,480 for DAT1 and DAT2, and 3,823 for DAT3. For each trait group, we ran a multivariate REML analysis using a repeatability animal model with trait-specific fixed and random effects. In the second part, DRPs were developed for the models used in part one. Reliabilities of breeding values (BVs) for animals with observations were used to derive multi-trait effective record contributions (ERC), which were then used to deregress the BVs with the Broyden method. To validate the use of DRPs in VC estimation, VCs were re-estimated by replacing phenotypic observations with DRPs and fitting a simple animal model with a general mean, a random animal effect, and ERC as weights. The originally estimated heritabilities from the field datasets (DAT1 and DAT2 ) MY, PY, FY, CCW, CCC, CCF, ST, MBW, DCG, BCC and BCF were 0.29±0.02, 0.20±0.02, 0.21±0.02, 0.46±0.03, 0.24±0.03, 0.27±0.02, 0.55±0.03, 0.65±0.03, 0.34±0.03, 0.19±0.02 and 0.20±0.02, and those from DAT3 for MY, PY, FY, MBW, DMI and BWG were 0.46±0.12, 0.28±0.11, 0.25±0.09, 0.84±0.11, 0.27±0.10, and 0.22±0.10, respectively. The heritability estimates and correlations obtained using DRPs were in good agreement with the original estimates when considering their standard errors. Differences between genetic correlations ranged from -0.09 to 0.09. Current results indicate that using DRPs can serve as an alternative for multivariate VC analysis when data sources are diverse, although some deviation from the original estimates should be expected.

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  Deriving covariance functions (CF) for Nordic Red dairy cattle yield evaluation model

  Pitkänen, Timo; Taskinen, Matti; Koivula, Minna; Pösö, Jukka; Kudinov, Andrei; Nielsen, Ulrik; Byskov, Kevin; Lidauer, Martin
  World Congress on Genetics Applied to Livestock Production Digital Archive : 1 (Iowa State University Digital Press, 2026)

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  This study compares different covariance function (CF) structures regarding their fit to observed variance components and their effects on genomic estimated breeding values (GEBV) and validation outcomes. Parameter reduced CF have been utilized in the Nordic Red dairy cattle test-day model for production traits (9 traits, milk, protein, and fat for first three lactations) to reduce unknowns, smooth genetic variance curves, and decrease computational demand. For that model, applied CF were fitted separately to estimated variance-covariance matrices for genetic and non-hereditary effect, followed by a parameter reduction. Although the effect of applying parameter reduced CF on estimated breeding values has been studied, little is known about the effect on genomic prediction reliability. The base CF was derived similarly as that one used in the current Nordic test-day model, using a second-order Legendre polynomial combined with an exponential term (exp(-0.04d) on estimated variance components, and hereafter is named Legendre-Wilmink (LW) function (rank 36). The applied function was the same as used for estimating the original variance components. Two alternative CF were developed: a Wilmink function (rank 27) and a repeatability function (rank 9). CF dimensions were reduced by considering the largest eigenvalues explaining ≥99.5% of the variation. For LW, additional reductions considered 99.0, 98.0 or 97.0% of variance. Depending on function and variance retained, CF parameters for the genetic effect ranged from 5 to 20, and for non-hereditary effect from 6 to 25. The CF fit was assessed using Log Likelihood (LogL) values and daily heritability estimates, while the impact on GEBV was evaluated through linear regression (LR) method and correlations between yield deviations (YD) and GEBVs. Results indicated that considering even less variance (99.0 to 97.0%) for LW CF had only a minor effect on model fit (LogL), whereas applying the Wilmink or repeatability model caused more pronounced differences. Daily heritability estimates followed similar patterns: Wilmink produced slightly varying heritabilities across days compared to original values, while the repeatability model yielded a single heritability estimate for the entire lactation period. LR validation results, based on regression coefficients (b1 and coefficients of determination (R²), and correlations, showed that using the simpler functions when fitting CF is not the optimal choice for GEBV prediction accuracy. Validation results for milk yield showed that the repeatability model performed the weakest (b1 = 0.95, R² = 0.73), while the LW model CF that considered 99.5% of the variance achieved the best validation results (b1 = 0.98, R² = 0.78). Similar patterns were observed for protein and fat yields. Overall, parameter reduction within LW had negligible impact on reliability, but replacing LW with simpler CF structures compromised GEBV accuracy.

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  Precise determination of electron-capture Q value of Sn113 decay related to electron neutrino mass measurements

  Ge, Zhuang; Eronen, Tommi; Sevestrean, Vasile Alin; Niţescu, Ovidiu; Stoica, Sabin; Ramalho, Marlom; Suhonen, Jouni; Kankainen, Anu; Hukkanen, Marjut; Jaries, Arthur; Jokinen, Ari; Kostensalo, Joel; Kotila, Jenni; Mougeot, Maxime; Moore, Iain D.; Rattanasakuldilok, Wirunchana; Ruotsalainen, Jouni; Stryjczyk, Marek
  Physical review C : 3 (American Physical Society, 2026)

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  Guidelines for selecting variability measure in limited-size ANOVA experiments

  Gabbrielli, Mara; Valkama, Elena; Calone, Roberta; Bregaglio, Simone; Manici, Luisa; Ragaglini, Giorgio; Perego, Alessia; Acutis, Marco
  Scientific reports : 1 (Springer Nature, 2026)

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