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Some oscine passerines incorporate heterospecific sounds into their repertoires, including vocalizations of other bird species, sounds of other fauna, and even anthropogenic sounds, through vocal mimicry. However, few studies have investigated whether mimics learn heterospecific sounds from model species or from conspecific tutors. Here, we investigate mimicry acquisition using innovation in Cuculidae calls imitated by the Chinese blackbird ( Turdus mandarinus ). If the mimicry innovation arises and spreads among several neighbors and is not produced by model species, the mimicry must be acquired partially from conspecifics. We found that: (1) Cuculidae calls imitated by blackbirds were reasonably accurate, but with some differences between mimetic and real calls in acoustic structures. (2) We identified four unique mimetic units (mimicry innovation or copy errors), and these units only occurred at certain sites and were shared by several neighbors. In aggregate, frequency parameters (the first principal component) of unique mimetic units were higher than usual mimetic units ( p  < 0.001). Our findings provide further evidence that mimetic units can be partially learnt from conspecifics based on four cases of unique mimetic units. Our study and approach provide a reference and theoretical basis for the future understanding of social learning and development of vocal mimicry.

In cleptoparasitic bees, host aggression and detection avoidance might be the main selective pressures shaping host-parasite interactions. However, the behavioral responses toward parasitism are unknown for most host species. In this study, we investigated the host-parasite interactions and behaviors of the cleptoparasitic bee Triepeolus remigatus when parasitizing the nests of its host, the squash bee Xenoglossa ( Peponapis ) pruinosa . Using circle-tube behavioral assays and direct observations at a nest aggregation of X. pruinosa , we assessed whether interactions between host and parasite were aggressive, tolerant, or avoidant and characterized the general parasitic behavior of T. remigatus . Our results reveal a lack of aggression between host and cuckoo bees, with interactions primarily characterized by tolerant and avoidant behaviors. Squash bees displayed minimal aggression toward both conspecifics and parasites. Interestingly, despite the absence of aggressive responses, T. remigatus preferred entering nests while the host was foraging, potentially indicating a strategy to avoid the discovery of parasitic visits. Furthermore, field observations provided insights into the parasitic behavior of T. remigatus , revealing primarily rapid visits to host nests without extensive inspection. The limited aggression and short time for nest visits observed in T. remigatus suggest adaptations to optimize parasitic success while minimizing host detection. Overall, our findings contribute to a better understanding of the behavior of open-cell parasites and provide a first accounting of the squash bee behavior when encountering parasitic bees. Further research is needed to elucidate the mechanisms underlying host-parasite coevolution and response to parasitism in ground-nesting bees.

Inherent hazards such as landslides pose a threat to human life and may inflict significant harm on the surrounding ecosystem. For planning, controlling, and avoiding landslide situations to minimize damages, a landslide susceptibility map is necessary. As a consequence of this, the current research makes use of a methodical approach and upgraded algorithms to identify and forecast locations that are susceptible to landslides. When it comes to problems associated with landslides, standard optimization techniques have been used quite a bit. This study presents a novel approach to the development of an artificial neural network (ANN) in the Iranian region of Kurdistan by using the cuckoo optimization algorithm (COA) and the SailFish optimizer (SFO) as metaheuristic approaches. In order to maximize the computational properties of these algorithms and depict a new kind of swarm intelligence, a multi-layer perceptron (MLP) neural network is used in the synthesis process. The findings of the landslide hazard maps were checked and compared using actual landslide sites. There were 1072 landslides shown on the inventory map. There was a 70:30 split between training and testing locations at random. Model input was narrowed down to 16 different landslide qualifying variables, namely elevation, slope aspect, slope angle, NDVI, distance to fault, plan curvature, profile curvature, rainfall, distance from river, distance to road, SPI, STI, TRI, TWI, land use, and geology. All of these parameters were considered to be important in determining the likelihood of a landslide occurring. The area under the curve (AUC) criterion was used to evaluate the accuracy of the probabilistic models that were put into use. Incidentally, the calculated comparable AUCs were as follows: 0.797, 0.789, 0.784, 0.779, 0.763, 0.758, 0.749, 0.740, 0.725, and 0.716 for COA-MLP, and 0.719, 0.695, 0.682, 0.675, 0.671, 0.670, 0.662, and 0.650 for SFO-MLP. The greatest hybrid model for forecasting landslide detection corresponds to the COA-MLP model, and it has a swarm size of four hundred people. As a consequence, the findings demonstrated that these two models had an effective performance for ANN-MLP optimization. Taking into consideration this evaluation, the hybrid models that were provided are trustworthy for the modeling of landslide susceptibility. As a result, the map of vulnerability that was developed can be utilized for hazardous design and increased planners' knowledge of dangerous locations.

In this research, to predict landslide susceptibility mapping (LSM), we have studied and optimized an artificial neural network (ANN) by utilizing the backtracking search algorithm (BSA) as well as the Cuckoo optimization algorithm (COA). Multiple research studies have shown that ANN-based techniques can be used to figure out the LSM. Still, ANN computing models have big problems, like slow system learning and getting stuck in their local minimums. Optimization strategies may improve ANN performance results. Existing uses of the BSA and COA models in ANN training have not been used to map landslides, nor have the best ways to set up networks or other factors that affect this problem been examined. Consequently, the present research focuses on predicting landslide susceptibility for hazardous mapping using hybrid BSA and COA-based ANN algorithms (BSA-MLP and COA). A large data set was provided from an area in the province of Kurdistan, west of Iran, to provide training and testing datasets for the algorithms. All of the BSA and COA algorithms’ parameters and weights, for instance, were fine-tuned to make the utmost accurate maps of landslide risk. The input dataset consists of elevation, slope angle, slope orientation, NDVI, fault tolerance, profile curvature, plan curvature, distance to the river, rainfall, far from the road, SPI, STI, TRI, TWI, land use, and geology; the output is landslide susceptibility value. In the testing phase, the AUC rose significantly from 0.701 to 0.864 for BSA-MLP and 0.738 to 0.822 for COA-MLP after using the abovementioned techniques. We have used the area under the curve (AUC) to evaluate how well the probabilistic models worked. In addition, the computed AUCs for the BSA-MLP available databases and the actual AUCs were 0.864, 0.857, 0.833, 0.778, 0.777, 0.769, 0.763, 0.758, 0.727, and 0.701 and 0.822, 0.808, 0.807, 0.805, 0.804, 0.777, and 0.769 for the COA-MLP combination. The integrated models can produce beneficial results for this area of research. The results suggest that the BSA-ANN model is better than the COA-ANN in optimizing an artificial neural network model’s structure and computational parameters. The collected landslide susceptibility maps are significant for figuring out how dangerous landslides are in the studied area.

The chestnut-winged cuckoo (Clamator coromandus) is a bird species known for its brood parasitism, laying eggs in the nests of other bird species. However, there is a paucity of genetic information available for this species and their genus Clamator. In this study, we present the first complete mitochondrial genome sequence of C. coromandus and compare it with other species within the Cuculidae family. The mitogenome is a closed circular molecule consisting of 17,082 bp with an organization typical of the mitochondrial genomes of Cuculidae. Alignment of the control regions across Cuculidae species revealed substantial genetic variation and a significant abundance of AT content. A significant difference was detected in AT-skews between brood-parasitic and parental-care species. A distinctive long poly-C sequence was located at the 5′ end of domain I. Phylogenetically, C. coromandus is more closely related to Piaya cayana than Ceuthmochares aereus. The phylogenetic analysis indicated a general divergence between species with brood parasitism and those with parental care, with transitions between these behaviors within brood parasitism branches, suggesting multiple evolutionary occurrences of these traits. The complete mitogenome of C. coromandus serves as a valuable resource for further investigation into the taxonomic status and phylogenetic history of Clamator species.

Offspring sex ratios may deviate from parity when the fitness benefits of producing male or female offspring vary. We tested for sex ratio bias in smooth‐billed anis Crotophaga ani, a communal laying cuckoo with low within‐group relatedness and high offspring dispersal. One male group member performs nocturnal incubation and sires more offspring than other males in the group, suggesting males may have greater reproductive variance than females. We hypothesized that pre‐breeding rainfall influences food availability and offspring sex ratio, predicting that breeding females skew production towards the sex with higher reproductive variance (males) in high food years. Females may also adjust sex ratio across the hatching order to increase survival of the more competitive sex, especially when clutches are larger and within‐brood competition is higher. As adults, male smooth‐billed anis are larger than females, so we assumed male nestlings are more competitive than females and predicted a male‐bias in first hatched chicks in larger broods. Contrary to our first prediction, offspring sex ratio was male biased when pre‐breeding rainfall was lower. In partial support of our second prediction, marginally more first hatched chicks were male in larger broods. To our knowledge, this is the first evidence of offspring sex ratio bias in a communal laying bird species. Future work in this system will attempt to uncover the mechanisms by which co‐breeding females adjust offspring sex ratio and test alternative hypotheses to explain male‐biased offspring sex ratios under different conditions.

Obligate brood parasites lay their eggs in nests of other species, with host parents bearing the cost of raising their offspring. These costs imposed on hosts select for the evolution of host defenses against parasitism at all stages of the reproductive cycle. The most effective defense is egg rejection at early stages of the breeding cycle, with later-stage defenses (nestling and fledgling discrimination) being less common. In this study, we tested whether the hoopoe (Upupa epops), a potential host of the great spotted cuckoo (Clamator glandarius) without egg rejection ability, presents defenses after the egg stage. We experimentally parasitized hoopoe nests with great spotted cuckoo nestlings creating mixed broods (with hoopoe and cuckoo nestlings) and broods with only cuckoo nestlings and measured parental feeding behavior and survival of nestlings and fledglings of both species. Cuckoo fledglings were fed fewer often than hoopoe fledglings in mixed broods, and adults approached more often to feed hoopoe fledglings than cuckoo fledglings. Consequently, the survival of cuckoo fledglings in both mixed and only-cuckoo-broods, was significantly lower than that of hoopoe fledglings. These results suggest that hoopoes would discriminate great spotted cuckoo fledglings, with or without direct comparison with their own fledglings. However, the survival of some cuckoos suggests that hoopoes have not reached highly efficient defenses so, other life history traits hindering parasitism by cuckoos may explain low parasitism rates and low levels of defenses in this species.Significance statementBrood parasites lay their eggs in nests of other species, tricking hosts into raising their parasitic offspring. However, hosts may fight back impeding successful parasitism by developing defences at any of the stages of their breeding cycle. We investigated why the hoopoe is not parasitized by the great spotted cuckoo despite this potential host apparently does not show such anti-parasitic defenses. We found that hoopoes have evolved the less common host defense: discrimination of parasite fledglings, even in the absence of their own fledgling for comparison. Our study supports the idea that discrimination during the later stages of the nesting cycle (i.e. nestling and fledgling periods) may be more common that previously assumed.

Kin selection theory predicts that conflict over resource allocation will intensify as relatedness between dependent young and adult caregivers decreases. As inclusive fitness constraints on dishonest signalling relax, begging behaviour is less likely to be a reliable indicator of hunger or condition. Therefore, dishonest signalling is expected to be especially prevalent in communally breeding species, for which offspring survival often depends on care from both related and unrelated adults. We evaluated the scope for conflict and its consequences for dishonest signalling in the greater ani (Crotophaga major), a communally nesting cuckoo in which multiple unrelated pairs lay in the same nest. Using video recordings of nearly 2500 feeding events across 10 nests, we demonstrate that begging behaviour is a reliable signal of hunger, with hungrier nestlings begging more intensely. We also show that begging may communicate reliable information about condition in the long term, with smaller nestlings begging more intensely than their larger broodmates. Ultimately, larger nestlings and those who begged more intensely were more likely to receive food, indicating that both begging signals and scramble competition influence resource allocation. Together, our results indicate that honest begging signals can persist even when caregivers and young are unrelated.Significance statementOffspring solicit food from their adult caregivers through a variety of begging behaviours. These behaviours can convey important information about offspring hunger and/or long-term condition, but may be exaggerated, if offspring attempt to gain more than their proportionate share of resources. We examined whether offspring exaggerate their begging behaviour, such that it is not a reliable indicator of their hunger or condition, in the greater ani. Greater anis breed communally, with multiple pairs sharing a single nest simultaneously such that nestlings are fed by both their parents and unrelated adult caregivers. Theory predicts that begging should be less reliable if offspring and caregivers are unrelated, but we found that greater ani begging behaviour reliably communicated hunger, and potentially long-term condition, to adults. This study is the first to evaluate begging signal reliability in a communally breeding species.

Brood parasites are expected to lay only one egg per parasitized nest, as the existence of several parasitic nestlings in a brood increases competition and can lead the starvation of some of them. However, multiparasitism (laying of two or more eggs by one or more parasitic females in a single host nest) is surprisingly frequent. Here, we study multiparasitism by different females or by the same female (repeated parasitism) in the great spotted cuckoo Clamator glandarius, a non‐evictor brood parasite that mainly parasitizes the magpie Pica pica, and whose chicks may be raised together with host nestlings in the same nest. We used a total of 262 magpie nests found during four breeding seasons. Multiparasitism and repeated parasitism are very frequent because this brood parasite is less virulent than other cuckoo species and magpie hosts can successfully raise more than one parasitic nestling per nest. The total number of cuckoo chicks fledged was higher in multiparasitized nests than in single‐ or double‐parasitized magpie nests. Magpie breeding success (i.e. the proportion of eggs that produce young that leave the nest) did not differ between single‐, double‐, and multiparasitized magpie nests. These results suggest that multiparasitism is an adaptation in the great spotted cuckoo. The intensity of parasitism (number of cuckoo eggs per nest), after controlling for the potential effect of year, did not affect nest desertion or nest predation rate, neither during the incubation nor the nestling periods. This implies that nest concealment does not affect the susceptibility of one nest being parasitized and predated, as nest predation rate was similar regardless of the intensity of parasitism. Predation rate during the nestling phase did not vary according to intensity of parasitism, which does not support either the ‘mutualism' hypothesis or the ‘predation cost of begging' hypothesis.

Water pollution escalates with rising waste discharge in river systems, as the rivers’ limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA’s Pareto front is superior to NSGA-II, which highlights the MOCOA’s effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size.