Direct Fitness Research Articles

Line mixing study of carbon monoxide near [formula omitted] broadened by nitrogen, helium, and hydrogen

We present quantitative, broadband absorbance measurements of the fundamental rovibrational band of carbon monoxide (CO) between 1965 and 2230 cm−1 in bath gases of nitrogen (N2), helium (He), and hydrogen (H2). These measurements are then compared to two constructed models capable of reproducing the effects of line mixing present in our measured results. The static cell measurements were taken using a narrow-linewidth, broad-scan external-cavity quantum-cascade laser at pressures of 15–35 atm and temperatures of 293, 453 (CO/H2) and 802 K (CO/N2, CO/He). Our first line mixing approach, based on the modified exponential gap (MEG) law with a fitted inter-branch factor, shows improved agreement with the measured spectra across different pressures and broadening partners relative to purely Lorentzian models. At the elevated temperatures, similar agreement is observed; however, a mismatch is present between extrapolated HITRAN broadening parameters and those observed in the measured spectra. This is likely due to the known deficiency of the single power law over large temperature ranges, and hence a minor scaling of the line-by-line temperature-dependence exponents is incorporated that is supported by previous studies in the literature. Our second, more empirical line mixing approach involves extracting MEG line mixing parameters through a direct fit to the measured spectra. The Direct Fit method bypasses the need for known line shape parameters and produces even stronger agreement with measured data, with a CO/H2 root-mean-square error of 0.4% at the highest-number-density condition of 293 K and 35 atm. The line mixing models presented and discussed will be useful for interpreting future satellite and telescope observations of CO from deeper, higher-number-density layers of exoplanetary gas-giant atmospheres, consisting of mainly H2 and He.

Identification of centrifugal pump flow based on feature recognition

The flow rate of water pumps is a crucial parameter for diagnosis, optimisation and renovation of transmission and distribution systems. To solve the problems of limited measurement conditions, inadequate consideration of operational wear and tear, and large deviations in the fitted flow curves when obtaining flow rates by direct measurement or characteristic curve fitting, this paper proposes a method for identification of centrifugal pump flow rates based on feature recognition. The method combines the unknown structural parameters of a centrifugal pump with three simple and easy-to-measure operating data: pump head, frequency and power, to obtain the pump characteristics and construct a model for identification of centrifugal pump flow. At the same time, the influence of measurement errors and wear and tear on the model’s accuracy is investigated. The results show that the maximum relative error of the pump operating flow obtained by the method is 2.27%, a slight deviation from the actual measured flow. The maximum relative error of flow rate is 2.7% when a maximum measurement error of ± 0.1%, 0.5% and ± (3%+5) for the required operational data, respectively, indicating that the method has a high resistance to interference. The method considers the different degrees of operating wear and tear of the pumps, saves on equipment input and enables fast and accurate identification of the flow rate of the pumps under different operating conditions.

Fitness consequences of chronic exposure to different light pollution wavelengths in nocturnal and diurnal rodents

Use of artificial at night (ALAN) exposes the world to continuously increasing levels and distribution of light pollution. Our understanding of the adverse effects of ALAN is based mostly on observational or laboratory studies, and its effects are probably underestimated. Demonstration of direct experimental fitness consequences of ALAN on mammals is missing. We studied the effects of chronic light pollution at different wavelengths on fitness and glucocorticoid hormone levels under semi-natural conditions in two closely related species: the nocturnal common spiny mouse (Acomys cahirinus) and the diurnal golden spiny mouse (Acomys russatus). Our results clearly demonstrate the adverse effects of ALAN exposure on the fitness of both nocturnal and diurnal species, manifested by changes in cortisol levels and reproductive timing, reduced reproductive output and reduced survival, which differed between species and wavelengths. In A. russatus exposure to blue ALAN had the strongest effect on fitness, followed by white and yellow ALAN exposure. In A. cahirinus the results are more complex and suggest it suffered from the combined effects of ALAN and competition. Our research shows that light pollution presents a real threat to both nocturnal and diurnal species, affecting the species fitness directly and through interspecific interactions. Worryingly, these effects are probably not limited to spiny mice. The clear adverse effects we documented, as well as the differences between wave lengths, contribute to our ability to present science-based recommendations to decision makers regarding the use of artificial light at night. Such information and guidelines are highly important nowadays when lighting systems are being replaced to promote energy efficiency.

Group augmentation on trial: helpers in small groups enhance antipredator defence of eggs.

Mechanisms selecting for the evolution of cooperative breeding are hotly debated. While kin selection theory has been the central paradigm to explain the seemingly altruistic behaviour of non-reproducing helpers, it is increasingly recognized that direct fitness benefits may be highly relevant. The group augmentation hypothesis proposes that alloparental care may evolve to enhance group size when larger groups yield increased survival and/or reproductive success. However, there is a lack of empirical tests. Here we use the cooperatively breeding cichlid fish Neolamprologus pulcher, in which group size predicts survival and group stability, to test this hypothesis experimentally by prompting two cooperative tasks: defence against an egg predator and digging out sand from the breeding shelter. We controlled for alternative mechanisms such as kin selection, load lightening and coercion. As predicted by the group augmentation hypothesis, helpers increased defence against an egg predator in small compared with large groups. This difference was only evident in large helpers owing to size-specific task specialization. Furthermore, helpers showed more digging effort in the breeding chamber compared with alternative personal shelters, indicating that digging is an altruistic service to the dominant breeders.

Automated dispersion curve picking using multi-attribute convolutional-neural-network based machine learning

SUMMARY Surface wave dispersion curves are useful to characterize shallow subsurface structures while accurately picking them is typically laborious. To make these approaches more efficient and practical, it is important to automate the picking process. We propose a convolutional neural network (CNN) based ML method to automatically pick multimode surface wave dispersion curves. We modify the typical U-net architecture to convert the conventional 2-D image segmentation problem into direct multimode curve fitting and subsequent picking. A variety of attributes of the data amplitude (A) in the (f, k) domain, such as frequency (F), wavenumber (K), maximum coherency (Coh) and Power weighted amplitude (Pwa), are combined to constrain the picking more accurately than a single attribute does. The effects of two different loss functions on the final picking results are compared; the one that combines conventional wavenumber residuals and curve slope residuals produces more continuous curves. Pre-training the network with synthetic data, and thus using transfer learning, improves the efficiency of the algorithm when the data set is large. To determine the frequency band of each dispersive mode (effective frequency band) in the picked curves, the CNN outputs are post-processed by using measurements such as long/short moving average ratios of squared picked wavenumbers, posterior uncertainty of picked wavenumbers and wavenumbers in the picked curves or the power attribute. We demonstrate the effectiveness of this automatic picking by applying it to a 2-D line and a 3-D subset from a field ocean bottom node data set, where the fundamental and first higher modes of Scholte waves are accurately picked.

Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 provides a highly efficient and flexible genome editing technology with numerous potential applications ranging from gene therapy to population control. Some proposed applications involve the integration of CRISPR/Cas9 endonucleases into an organism's genome, which raises questions about potentially harmful effects to the transgenic individuals. One example for which this is particularly relevant are CRISPR-based gene drives conceived for the genetic alteration of entire populations. The performance of such drives can strongly depend on fitness costs experienced by drive carriers, yet relatively little is known about the magnitude and causes of these costs. Here, we assess the fitness effects of genomic CRISPR/Cas9 expression in Drosophila melanogaster cage populations by tracking allele frequencies of four different transgenic constructs that allow us to disentangle 'direct' fitness costs due to the integration, expression, and target-site activity of Cas9, from fitness costs due to potential off-target cleavage. Using a maximum likelihood framework, we find that a model with no direct fitness costs but moderate costs due to off-target effects fits our cage data best. Consistent with this, we do not observe fitness costs for a construct with Cas9HF1, a high-fidelity version of Cas9. We further demonstrate that using Cas9HF1 instead of standard Cas9 in a homing drive achieves similar drive conversion efficiency. These results suggest that gene drives should be designed with high-fidelity endonucleases and may have implications for other applications that involve genomic integration of CRISPR endonucleases.

NO2 retrievals from NOAA-20 OMPS: Algorithm, evaluation, and observations of drastic changes during COVID-19

We present the first NO2 measurements from the Nadir Mapper of Ozone Mapping and Profiler Suite (OMPS) instrument aboard the NOAA-20 satellite. NOAA-20 OMPS was launched in November 2017, with a nadir resolution of 17 × 13 km2 similar to the Ozone Monitoring Instrument (OMI). The retrieval of NOAA-20 NO2 vertical columns were achieved through the Direct Vertical Column Fitting (DVCF) algorithm, which was uniquely designed and successfully used to retrieve NO2 from OMPS aboard Suomi National Polar-orbiting Partnership (SNPP) spacecraft, predecessor to NOAA-20. Observations from NOAA-20 reveal a 20–40% decline in regional tropospheric NO2 in January–April 2020 due to COVID-19 lockdown, consistent with the findings from other satellite observations. The NO2 retrievals are preliminarily validated against ground-based Pandora spectrometer measurements over the New York City area as well as other U.S. Pandora locations. It shows OMPS total columns tend to be lower in polluted urban regions and higher in clean areas/episodes associated with relatively small NO2 total columns, but generally the agreement is within ±2.5 × 1015 molecules/cm2. Comparisons of stratospheric NO2 columns exhibit the excellent agreement between OMPS and OMI, validating OMPS capability in capturing the stratospheric background accurately. These results demonstrate the high sensitivity of OMPS to tropospheric NO2 and highlight its potential use for extending the long-term global NO2 record.

A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis.

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10−4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.

A comparison between different ways to assess demands-abilities fit in higher education: Empirical results and recommendations for research practice.

Researchers studying person-environment fit can choose between various measurement approaches. Even though these measures are distinctly different, they often get used interchangeably, which makes interpreting the results of person-environment fit studies difficult. In the present article, we contrast the most commonly used measurement approaches for person-environment fit in higher education and compare them in terms of explained variance. We obtained data on the fit as well as subjective and objective study-related outcomes of N = 595 university students. We analyzed the fit between the demands of the study program and the abilities of the student, using the algebraic, squared and absolute difference score, response surface analysis (RSA), and direct fit as measurement approaches. Our results indicate that RSA explains the most variance for objective outcomes, and that direct fit explains the most variance for subjective outcomes. We hope that this contribution will help researchers distinguish the different measurement approaches of demands-abilities fit (and ultimately person-environment fit) and use them accordingly.

Temperature-dependent dielectric function of intrinsic silicon: Analytic models and atom-surface potentials

The optical properties of monocrystalline, intrinsic silicon are of interest for technological applications as well as fundamental studies of atom-surface interactions. For an enhanced understanding, it is of great interest to explore analytic models which are able to fit the experimentally determined dielectric function $\epsilon(T_\Delta, \omega)$, over a wide range of frequencies and a wide range of the temperature parameter $T_\Delta = (T-T_0)/T_0$, where $T_0 = 293\,{\rm K}$ represents room temperature. Here, we find that a convenient functional form for the fitting of the dielectric function of silicon involves a Lorentz-Dirac curve with a complex, frequency-dependent amplitude parameter, which describes radiation reaction. We apply this functional form to the expression $[\epsilon(T_\Delta, \omega) -1]/[ \epsilon(T_\Delta, \omega)+2]$, inspired by the Clausius-Mossotti relation. With a very limited set of fitting parameters, we are able to represent, to excellent accuracy, experimental data in the (angular) frequency range $0 < \omega < 0.16 \, {\rm a.u.}$ and $0< T_\Delta < 2.83$, corresponding to the temperature range $ 293\,{\rm K} < T < 1123\, {\rm K}$. Using our approach, we evaluate the short-range $C_3$ and the long-range $C_4$ coefficients for the interaction of helium atoms with the silicon surface. In order to validate our results, we compare to a separate temperature-dependent direct fit of $\epsilon(T_\Delta, \omega)$ to the Lorentz-Dirac model.

Exploring the interplay between natural and intersexual selection on the evolution of a cognitive trait.

There has been an increased focus on the role of natural and sexual selection in shaping cognitive abilities, but the importance of the interaction between both forces remains largely unknown. Intersexual selection through female mate choice might be an important driver of the evolution of cognitive traits, especially in monogamous species, where females may obtain direct fitness benefits by choosing mates with better cognitive abilities. However, the importance given by female to male cognitive traits might vary among species and/or populations according to their life‐history traits and ecology. To disentangle the effects of natural and sexual selection, here we use an agent‐based simulation model and compare the model's predictions when females mate with the first randomly encountered male (i.e., under natural selection) versus when they choose among males based on their cognitive trait values (i.e., under natural and intersexual selection). Males and females are characterized, respectively, by their problem‐solving ability and assessment strategy. At each generation, agents go through (1) a choosing phase during which females assess the cognitive abilities of potential mates until eventually finding an acceptable one and (2) a reproductive phase during which all males compete for limited resources that are exploited at a rate, which depends on their cognitive abilities. Because males provide paternal care, the foraging success of mated males determines the breeding success of the pair through its effect on nestling provisioning efficiency. The model predicts that intersexual selection plays a major role in most ecological conditions, by either reinforcing or acting against the effect of natural selection. The latter case occurs under harsh environmental conditions, where intersexual selection contributes to maintaining cognitive diversity. Our findings thus demonstrate the importance of considering the interaction between both selective forces and highlight the need to build a conceptual framework to target relevant cognitive traits.

Temperature‐induced compensatory growth in the nematode Caenorhabditis elegans is regulated by a thermosensitive TRP channel and influences reproductive rate

Abstract Animals are often not growing at the maximum rate, but can compensate for a bad start of life by subsequently increasing growth rate. While this compensatory growth is widespread, its direct fitness consequences are seldom investigated and its genetic basis is unknown. We investigated the genetic regulation, as well as fitness and lifespan consequences of compensatory growth in response to temperature, using Caenorhabditis elegans knockout of the thermo‐sensitive TRP ion channel TRPA‐1, involved in temperature recognition. We exposed juvenile worms to cold, favourable (intermediate) or warm temperatures in order to delay or speed up development. Wild‐type worms initially exposed to cold temperature experienced slower growth but after being switched to a more favourable temperature, they expressed compensatory growth and caught up in size. Those initially reared at warmer temperatures than favourable experienced slower growth and attained smaller adult size after being switched to the most favourable temperature. Compensatory growth also altered the reproductive schedule. While rate‐sensitive individual fitness decreased by cold juvenile temperatures, as a direct effect of the substantial developmental delay, once worms returned to more favourable temperature, they shifted their reproductive schedule towards early reproduction. Therefore, when focusing on the post‐treatment period, the reproductive rate increased even though lifetime reproductive success was unaffected. Surprisingly, compensatory growth did not reduce adult lifespan. In contrast to the findings for wild‐type worms, juvenile temperature did not induce compensatory or slowed‐down growth in the trpa‐1 knockout mutants. We thus show that the trpa‐1 is involved in the network regulating temperature‐induced compensatory growth in C. elegans and that this compensatory growth can influence the reproductive rate. Read the free Plain Language Summary for this article on the Journal blog.

Prediction of vibrational energy levels for the CO molecule and 7Li2 dimer

Through solving the Schrödinger equation with the improved Pöschl-Teller potential energy model, we obtain a new vibration–rotation energy spectrum representation for diatomic molecules. Using the developed energy spectrum expression and experimental data of four molecular constants for the X1Σ+ state of the CO molecule and the a3Σu+ state of 7Li2 dimer, we predict the vibrational energy levels. The predicted values from the improved Pöschl-Teller potential are in better agreement with the vibrational energy values of the direct potential fit and experimentally observed data reported in the literature than those obtained from the improved Tietz potential.

A systematic review of Industry 4.0 maturity models: applicability in the O&G upstream industry

PurposeThe study aims to review the currently available Industry 4.0 (I4.0) maturity models (MMs) for manufacturing industries and analyse their applicability in the oil and gas (O&G) upstream sector. Knowing that the growth in demand for energy through crude oil and natural gas is still viable over the next decade, there is the drive to ensure sustenance and improvement in production. The study sees an opportunity in harnessing the gains of Industry 4.0 technologies for better solution-driven strategies in production processes, equipment availability and reliability which would translate into higher production performance. So, a review on the Industry 4.0 MMs is considered important.Design/methodology/approachA systematic and in-depth literature review was performed to identify the specific requirements of this industry. This study examined the key characteristics of the O&G upstream sector and identified research gaps that need to be addressed to successfully support this industry for Industry 4.0 implementation. An Industry 4.0 MM that reflects the industrial realities for this industry more accurately from insights drawn from reviews of existing MMs is proposedFindingsThe review of 19 selected Industry 4.0 MMs revealed that the existing MMs are not a direct fit for the O&G upstream industry. Only a few of the models were clear on validation but with subjectivity, low number of persons and industries involved as limitations; none of the models confirmed validation with the O&G industry. There are varying views on the model dimensions and maturity levels by each author and not all required areas specific to the O&G industries were acknowledged by the models. An MM specific to this industry is therefore required.Originality/valueAlthough the journey of digitisation has commenced in the O&G industry, a reduction with the challenges of transition towards Industry 4.0 implementation and provision of support for improved efficiency is assured using a robust MM, as proposed in this paper.

The evolution of cooperative breeding by direct and indirect fitness effects.

The evolution of cooperative breeding has been traditionally attributed to the effect of kin selection. While there is increasing empirical evidence that direct fitness benefits are relevant, the relative importance of alternative selection mechanisms is largely obscure. Here, we model the coevolution of the cornerstones of cooperative breeding, delayed dispersal, and alloparental care, across different ecological scenarios while allowing individuals to adjust philopatry and helping levels. Our results suggest that (i) direct fitness benefits from grouping are the main driver for the evolution of philopatry; (ii) kin selection is mainly responsible for the emergence of alloparental care, but group augmentation can be a sufficient promoter in harsh environments; (iii) the coevolution of philopatry and alloparental care is subject to positive feedback; and (iv) age-dependent dispersal is triggered by both group benefits and relatedness. Model predictions are supported by empirical data and provide good opportunities for comparative analyses and experimental tests of causality.

Plasticity in social behaviour varies with reproductive status in an avian cooperative breeder.

Cooperatively breeding vertebrates are common in unpredictable environments where the costs and benefits of providing offspring care fluctuate temporally. To balance these fitness outcomes, individuals of cooperatively breeding species often exhibit behavioural plasticity according to environmental conditions. Although individual variation in cooperative behaviours is well-studied, less is known about variation in plasticity of social behaviour. Here, we examine the fitness benefits, plasticity and repeatability of nest guarding behaviour in cooperatively breeding superb starlings (Lamprotornis superbus). After demonstrating that the cumulative nest guarding performed at a nest by all breeders and helpers combined is a significant predictor of reproductive success, we model breeder and helper behavioural reaction norms to test the hypothesis that individuals invest more in guarding in favourable seasons with high rainfall. Variation in nest guarding behaviour across seasons differed for individuals of different reproductive status: breeders showed plastic nest guarding behaviour in response to rainfall, whereas helpers did not. Similarly, we found that individual breeders show repeatability and consistency in their nest guarding behaviour while individual helpers did not. Thus, individuals with the potential to gain direct fitness benefits exhibit greater plasticity and individual-level repeatability in cooperative behaviour.

Calibrating with a smile: A Mellin transform approach to volatility surface calibration

The implied volatility in the Black-Scholes framework is not a constant but a function of both the strike price (“smile/skew”) and the time to expiry. A popular approach to recovering the volatility surface is through the use of deterministic volatility function models via Dupire’s equation. A new method for volatility surface calibration based on the Mellin transform is proposed. An explicit formula for the volatility surface is obtained in terms of the Mellin transform of the call option price with respect to the strike price, and a numerical algorithm is provided. Results of numerical simulations are presented and the stability of the method is numerically verified. The proposed Mellin transform approach provides a simpler and more direct fitting of generalised forms of the volatility surface given previously in the literature.

Adaptive fitting of potential energy surfaces of small to medium-sized molecules in sum-of-product form: Application to vibrational spectroscopy.

A semi-automatic sampling and fitting procedure for generating sum-of-product (Born-Oppenheimer) potential energy surfaces based on a high-dimensional model representation is presented. The adaptive sampling procedure and subsequent fitting rely on energies only and can be used for re-fitting existing analytic potential energy surfaces in the sum-of-product form or for direct fits from ab initio computations. The method is tested by fitting ground electronic state potential energy surfaces for small to medium sized semi-rigid molecules, i.e., HFCO, HONO, and HCOOH, based on ab initio computations at the coupled-cluster single double and perturbative triples-F12/cc-pVTZ-F12 or MP2/aug-cc-pVTZ levels of theory. Vibrational eigenstates are computed using block improved relaxation in the Heidelberg multi-configurational time dependent Hartree package and compared to available experimental and theoretical data. The new potential energy surfaces are compared to the best ones currently available for these molecules in terms of accuracy, including resulting vibrational states, required number of sampling points, and number of fitting parameters. The present procedure leads to compact expansions and scales well with the number of dimensions for simple potentials such as single or double wells.

The effect of crack length and maximum stress on the fatigue crack growth rates of engineering alloys

The fatigue crack growth rate (FCGR) curve of metallic alloys is usually divided into three regions. Region II is often referred to as the Paris regime and is usually modelled with a power law relationship with a single exponent. Regions I and III are located at the beginning and end of the FCGR curve, respectively, and are frequently modelled with asymptotic relationships. In this paper we hypothesize that fatigue crack growth is governed by power law behaviour at all crack lengths and all stress intensity factor ranges (ΔK). To accommodate for the changes in the FCGR slope at regions I - III mathematical pivot points are introduced in the Paris equation. Power law behaviour with the presence of pivot points enables direct fitting of the crack length vs. cycles (a-N) curve to obtain the FCGR as a function of ΔK. This novel approach is applicable to small and long crack growth curves and results in accurate multilinear FCGR curves that are suitable for reconstruction of the measured a-N curves. The method is subsequently applied to i) different alloys to show local changes in the FCGR curve for changes in alloy composition and heat treatments, ii) naturally increasing ΔK testing of microstructurally small cracks to obtain accurate small crack FCGR data. The comparison with accurate long crack data shows that small cracks are faster, but the transition from region I to region II occurs at a specific fatigue crack growth rate which results in an apparent shift in ΔK at the transition. iii) Long cracks, which show that the FCGR increases with maximum stress for a given ΔK and stress ratio when the maximum stress approaches the yield stress. The maximum stress phenomenon becomes important in the case of fatigue testing, where the initial crack lengths are usually small and maximum stresses are high. It is concluded that for long cracks the phenomenon explains why the Paris equation is applicable rather at low maximum stress, while the Frost-Dugdale equation is more applicable at high maximum stress.

Complementary interactions between indirect and direct fitness in a cooperatively breeding bird.

Altruism is difficult to explain evolutionarily and to understand it, there is a need to quantify the benefits and costs to altruists. Hamilton's theory of kin selection argues that altruism can persist if the costs to altruists are offset by indirect fitness payoffs from helping related recipients. Nevertheless, helping nonkin is also common and in such situations, the costs must be compensated for by direct benefits. While previous researchers tended to evaluate the indirect and direct fitness in isolation, we expect that they have a complementary interaction where altruists are associated with recipients of different relatedness within a population. The prediction is tested with 12 years of data on lifetime reproductive success for a cooperatively breeding bird, Tibetan ground tits Pseudopodoces humilis. Helpers who helped distantly related recipients gained significantly lower indirect benefits than those who helped closely related recipients, but the opposite was true for direct fitness, thereby making these helpers have an equal inclusive fitness. Helping efforts were independent of helpers' relatedness to recipients, but those helping distantly related recipients were more likely to inherit the resident territory, which could be responsible for their high direct reproductive success. Our findings provide an explanatory model for the widespread coexistence of altruists and recipients with varying relatedness within a single population.