Ecology

1.
Parolin, P. et al. Secondary plants used in biological control: a review. Int. J. Pest Manag. 58, 91–100 (2012).
Article  Google Scholar 
2.
Parker, J. E., Snyder, W. E., Hamilton, G. C. & Rodriguez‐Saona, C. Companion planting and insect pest control. Weed and Pest Control – Conventional and New Challenges (2013). https://doi.org/10.5772/55044

3.
Held, D. W., Gonsiska, P. & Potter, D. A. Evaluating companion planting and non-host masking odors for protecting roses from the Japanese beetle (Coleoptera: Scarabaeidae). J. Econ. Entomol. 96, 81–87 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Togni, P. H. B., Laumann, R. A., Medeiros, M. A. & Suji, E. R. Odour masking of tomato volatiles by coriander volatiles in host plant selection of Bemisia tabaci biotype B. Entomol. Exp. Appl. 136, 164–173 (2010).
Article  Google Scholar 

5.
Deletre, E. et al. Prospects for repellent in pest control: current developments and future challenges. Chemoecology 26, 127–142 (2016).
CAS  Article  Google Scholar 

6.
Ben-Issa, R., Gomez, L. & Gautier, H. Companion plants for aphid pest management. Insects 8, 112 (2017).
PubMed Central  Article  Google Scholar 

7.
Niemeyer, H. Secondary plant chemicals in aphid-host interactions. RK. RK Campbell RD Eikenbary Aphid-plant genotype Interact, 101–111 (1990).

8.
Powell, G., Tosh, C. R. & Hardie, J. HOST PLANT SELECTION BY APHIDS: behavioral, evolutionary, and applied perspectives. Annu. Rev. Entomol. 51, 309–330 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Mansion-Vaquié, A., Ferrer, A., Ramon-Portugal, F., Wezel, A. & Magro, A. Intercropping impacts the host location behaviour and population growth of aphids. Entomol. Exp. Appl. 168, 41–52 (2020).
Article  Google Scholar 

10.
Nottingham, S. F. et al. Behavioral and electrophysiological responses of Aphids to host and nonhost plant volatiles. J. Chem. Ecol. 17, 1231–1242 (1991).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Ben Issa, R., Gautier, H. & Gomez, L. Influence of neighbouring companion plants on the performance of aphid populations on sweet pepper plants under greenhouse conditions. Agric. For. Entomol. 19, 181–191 (2017).
Article  Google Scholar 

12.
Hatt, S., Xu, Q., Francis, F. & Osawa, N. Aromatic plants of East Asia to enhance natural enemies towards biological control of insect pests. A review. Entomol. Gener. 38, 275–315 (2019).
Article  Google Scholar 

13.
Basedow, T., Hua, L. & Aggarwal, N. The infestation of Vicia faba L. (Fabaceae) by Aphis fabae (Scop.) (Homoptera: Aphididae) under the influence of Lamiaceae (Ocimum basilicum L. and Satureja hortensis L.). J. Pest Sci. 79, 149 (2006).
Article  Google Scholar 

14.
Beizhou, S. et al. Temporal dynamics of the arthropod community in pear orchards intercropped with aromatic plants. Pest Manag. Sci. 67, 1107–1114 (2011).
PubMed  Article  CAS  PubMed Central  Google Scholar 

15.
Glinwood, R., Ninkovic, V., Pettersson, J. & Ahmed, E. Barley exposed to aerial allelopathy from thistles (Cirsium spp.) becomes less acceptable to aphids. Ecol. Entomol. 29, 188–195 (2004).
Article  Google Scholar 

16.
Jankowska, B., Poniedziałek, M. & Jędrszczyk, E. Effect of intercropping white cabbage with French Marigold (Tagetes patula nana L.) and Pot Marigold (Calendula officinalis L.) on the colonization of plants by pest insects. Folia Hortic. 21, 95–103 (2009).
Article  Google Scholar 

17.
Tang, G. B. et al. Repellent and attractive effects of herbs on insects in pear orchards intercropped with aromatic plants. Agroforest. Syst. 87, 273–285 (2013).
Article  Google Scholar 

18.
de Lima, J. S. S. et al. Agroeconomic evaluation of intercropping rocket and carrot by uni- and multivariate analyses in a semi-arid region of Brazil. Ecol. Ind. 41, 109–114 (2014).
Article  Google Scholar 

19.
Sujayanand, G. K., Sharma, R. K., Shankarganesh, K., Saha, S. & Tomar, R. S. Crop diversification for sustainable insect pest management in eggplant (Solanales: Solanaceae). Fla. Entomol. 98, 305–314 (2015).
Article  Google Scholar 

20.
McCall, P. J. & Eaton, G. Olfactory memory in the mosquito Culex quinquefasciatus. Med. Vet. Entomol. 15, 197–203 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Little, C. M., Chapman, T. W. & Hillier, N. K. Considerations for insect learning in integrated pest management. J. Insect Sci. 19, 6 (2019).
PubMed  PubMed Central  Article  Google Scholar 

22.
Bandara, K. A. N. P. et al. Can leek interfere with bean plant-bean fly interaction? Test of ecological pest management in mixed cropping. J. Econ. Entomol. 102, 999–1008 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Mutiga, S. K., Gohole, L. S. & Auma, E. O. Effects of integrating companion cropping and nitrogen application on the performance and infestation of collards by Brevicoryne brassicae. Entomol. Exp. Appl. 134, 234–244 (2010).
CAS  Article  Google Scholar 

24.
Dugravot, S., Thibout, E., Abo-Ghalia, A. & Huignard, J. How a specialist and a non-specialist insect cope with dimethyl disulfide produced by Allium porrum. Neth. Entomol. Soc. Entomol. Exp. Appl. 113, 173–179 (2004).
Article  Google Scholar 

25.
Thibout, E. & Auger, J. Composés soufrés des Allium et lutte contre les insectes. Acta Bot. Gallica 144, 419–426 (1997).
Article  Google Scholar 

26.
Auger, J., Dugravot, S., Naudin, A. & Abo-Ghalia, A. Utilisation des composes allelochimiques des Allium en tant qu’insecticides. Use of pheromones and other semiochemicals in integrated production IOBC wprs Bulletin Vol. 25, 13 (2002).

27.
Amarawardana, L. et al. Olfactory response of Myzus persicae (Homoptera: Aphididae) to volatiles from leek and chive: potential for intercropping with sweet pepper. Acta Agric. Scand. Sect. B – Soil Plant Sci. 57, 87–91 (2007).
Google Scholar 

28.
Zhou, H. et al. Influence of garlic intercropping or active emitted volatiles in releasers on aphid and related beneficial in wheat fields in China. J. Integr. Agric. 12, 467–473 (2013).
Article  Google Scholar 

29.
Mauck, K. E., De Moraes, C. M. & Mescher, M. C. Deceptive chemical signals induced by a plant virus attract insect vectors to inferior hosts. Proc. Natl. Acad. Sci. USA 107, 3600–3605 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Tjallingii, W. F. Electrical recording of stylet penetration activities. In Aphids, Their Biology, Natural Enemies and Control (eds Minks, A. K. & Harrewijn, P.) 95–108 (Elsevier, Amsterdam, 1988).
Google Scholar 

31.
Tjallingii, W. F. Electronic recording of penetration behaviour by aphids. Entomol. Exp. Appl. 24, 721–730 (1978).
Article  Google Scholar 

32.
Giordanengo, P. EPG-Calc: a PHP-based script to calculate electrical penetration graph (EPG) parameters. Arthropod-Plant Interact. 8, 163–169 (2014).
Article  Google Scholar 

33.
MacGillivray, M. E. & Anderson, G. B. Three useful insect cages. Can. Entomol. 89, 43–46 (1957).
Article  Google Scholar 

34.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2014).
Google Scholar 

35.
Järvenpää, E. P., Zhang, Z., Huopalahti, R. & King, J. W. Determination of fresh onion (Allium cepa L.) volatiles by solid phase microextraction combined with gas chromatography-mass spectrometry. Z. Lebensm Unters Forsch 207, 39–43 (1998).
Article  Google Scholar 

36.
Løkke, M. M., Edelenbos, M., Larsen, E. & Feilberg, A. Investigation of volatiles emitted from freshly cut onions (Allium cepa L.) by real time proton-transfer reaction-mass spectrometry (PTR-MS) . Sensors 12, 16060–16076 (2012).
PubMed  Article  CAS  PubMed Central  Google Scholar 

37.
Camacho-Coronel, X., Molina-Torres, J. & Heil, M. Sequestration of exogenous volatiles by plant cuticular waxes as a mechanism of passive associational resistance: a proof of concept. Front. Plant Sci. 11, 121 (2020).
PubMed  PubMed Central  Article  Google Scholar 

38.
Himanen, S. J. et al. Birch (Betula spp.) leaves adsorb and re-release volatiles specific to neighbouring plants—a mechanism for associational herbivore resistance?. New Phytol. 186, 722–732 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Dahlin, I., Vucetic, A. & Ninkovic, V. Changed host plant volatile emissions induced by chemical interaction between unattacked plants reduce aphid plant acceptance with intermorph variation. J Pest Sci 88, 249–257 (2015).
Article  Google Scholar 

40.
Dardouri, T. et al. Non-host volatiles disturb the feeding behavior and reduce the fecundity of the green peach aphid, Myzus persicae. Pest Manag Sci. https://doi.org/10.1002/ps.6190 (2020).
Article  PubMed  PubMed Central  Google Scholar 

41.
Dancewicz, K., Gabryś, B. & Przybylska, M. Effect of garlic (Allium sativum L.) and tansy (Tanaceum vulgare L.) extracts and potassic horticultural soap on the probing and feeding behaviour of Myzus persicae (Sulzer, 1776). Aphids Other Homopterous Insects 17, 126–136 (2011).
Google Scholar 

42.
Chyb, S., Eichenseer, H., Hollister, B., Mullin, C. A. & Frazier, J. L. Identification of sensilla involved in taste mediation in adult western corn rootworm (Diabrotica virgifera virgifera LeConte). J. Chem. Ecol. 21, 313–329 (1995).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Prado, E. & Tjallingii, W. F. Effects of previous plant infestation on sieve element acceptance by two aphids. Entomol. Exp. Appl. 82, 189–200 (1997).
Article  Google Scholar 

44.
Sauge, M.-H., Lacroze, J.-P., Poëssel, J.-L., Pascal, T. & Kervella, J. Induced resistance by Myzus persicae in the peach cultivar ‘Rubira’. Entomol. Exp. Appl. 102, 29–37 (2002).
Article  Google Scholar 

45.
Tolosa, T. A. et al. Molasses grass induces direct and indirect defense responses in neighbouring maize plants. J. Chem. Ecol. 45, 982–992 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Ninkovic, V., Rensing, M., Dahlin, I. & Markovic, D. Who is my neighbor? Volatile cues in plant interactions. Plant Signal Behav. 14, 1634993 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Karban, R., Yang, L. H. & Edwards, K. F. Volatile communication between plants that affects herbivory: a meta-analysis. Ecol. Lett. 17, 44–52 (2014).
PubMed  Article  PubMed Central  Google Scholar 

48.
Kumar, P., Mishra, S., Malik, A. & Satya, S. Insecticidal properties of Mentha species: a review. Ind. Crops Prod. 34, 802–817 (2011).
CAS  Article  Google Scholar 

49.
Nuñez-Mejía, G., Valadez-Lira, J. A., Gomez-Flores, R., Rodríguez-Padilla, C. & Tamez-Guerra, P. Trichoplusia ni (Lepidoptera: Noctuidae) survival, immune response, and gut bacteria changes after exposure to Azadirachta indica (Sapindales: Meliaceae) volatiles. Fla. Entomol. 99, 12–20 (2016).
Article  Google Scholar 

50.
Regnault-Roger, C., Vincent, C. & Arnason, J. T. Essential oils in insect control: low-risk products in a high-stakes world. Annu. Rev. Entomol. 57, 405–424 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Sousa, R. M. O. F., Rosa, J. S., Oliveira, L., Cunha, A. & Fernandes-Ferreira, M. Activities of Apiaceae essential oils and volatile compounds on hatchability, development, reproduction and nutrition of Pseudaletia unipuncta (Lepidoptera: Noctuidae). Ind. Crops Prod. 63, 226–237 (2015).
CAS  Article  Google Scholar 

52.
Petrakis, E. A. et al. Responses of Myzus persicae (Sulzer) to three Lamiaceae essential oils obtained by microwave-assisted and conventional hydrodistillation. Ind. Crops Prod. 62, 272–279 (2014).
CAS  Article  Google Scholar 

53.
Poorjavad, N., Goldansaz, S. H., Dadpour, H. & Khajehali, J. Effect of Ferula assafoetida essential oil on some biological and behavioral traits of Trichogramma embryophagum and T. evanescens. Biocontrol 59, 403–413 (2014).
CAS  Article  Google Scholar 

54.
Vázquez-Covarrubias, D. A., Jiménez-Pérez, A., Castrejón-Ayala, F., Figueroa-Brito, R. & Belmont, R. M. Effects of five species of Chenopodiaceae on the development and reproductive potential of Copitarsia decolora (Lepidoptera: Noctuidae). Fla. Entomol. 98, 80–85 (2015).
Article  Google Scholar 

55.
Bharti, G., Prasad, S. & Upadhyay, V. B. The influence of plant volatile of Allium sativum on the reproductive ability of multivoltine mulberry silkworm Bombyx mori Linn. Afr. J. Basic Appl. Sci. 5(6), 242–249 (2013).
Google Scholar 

56.
Ameline, A., Couty, A., Martoub, M., Sourice, S. & Giordanengo, P. Modification of Macrosiphum euphorbiae colonisation behaviour and reproduction on potato plants treated by mineral oil. Entomol. Exp. Appl. 135, 77–84 (2010).
Article  Google Scholar 

57.
Calabrese, E. J. Hormesis: why it is important to toxicology and toxicologists. Environ. Toxicol. Chem. 27, 1451–1474 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Ayyanath, M.-M., Cutler, G. C., Scott-Dupree, C. D. & Sibley, P. K. Transgenerational shifts in reproduction hormesis in green peach aphid exposed to low concentrations of imidacloprid. PLoS ONE 8, e74532 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Yu, Y., Shen, G., Zhu, H. & Lu, Y. Imidacloprid-induced hormesis on the fecundity and juvenile hormone levels of the green peach aphid Myzus persicae (Sulzer). Pestic. Biochem. Physiol. 98, 238–242 (2010).
CAS  Article  Google Scholar 

60.
Stephens, D. W. Learning and behavioral ecology: incomplete information and environmental predictability. In Insect Learning: Ecology and Evolutionary Perspectives (eds Papaj, D. R. & Lewis, A. C.) 195–218 (Springer, New York, 1993).
Google Scholar 

61.
Bedini, S. et al. Allium sativum, Rosmarinus officinalis, and Salvia officinalis essential oils: a spiced shield against blowflies. Insects 11, 143 (2020).
PubMed Central  Article  Google Scholar 

62.
Shi, J. et al. Laboratory evaluation of acute toxicity of the essential oil of Allium tuberosum leaves and its selected major constituents against Apolygus lucorum (Hemiptera: Miridae). J. Insect Sci. 15, 117 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar  More

Study area
We established field survey sites in four different habitat types (BW: breakwater wall; EB: eelgrass bed; IF: intertidal flat; SB: subtidal bottom) within Matsunaga Bay, located in the eastern Seto Inland Sea, Japan (Fig. 1). Matsunaga Bay is a small semi-closed bay with an area of approximately 12 km2. It is connected with other water bodies through the Tozaki-Seto Strait (width: approx. 400 m) and the Onomichi Strait (width: approx. 200 m)29,30. Water depths are mostly less than 20 m throughout the bay. The water depths at our four survey sites were approximately 4.5 m, but part of site SB located near a shipping channel reached depths of 10–13 m. Intertidal flats cover 35% (4.3 km2) of the bay area, whereas eelgrass beds cover 1.7% (0.2 km2)31. The bottom sediment type is mainly muddy throughout the bay, although some parts of EB and SB have sandy and muddy bottoms (see Supplementary Table S1)30.
Figure 1

Locations of sampling sites in Matsunaga Bay, Hiroshima, Japan. This map was created based on coordinate data from Google (http://www.gis-tool.com/mapview/maptocoordinates.html). The four habitats examined in this study are indicated by BW (breakwater wall; grey circle), EB (eelgrass bed; grey circle), IF (intertidal flat; grey rectangle), and SB (subtidal bottom; grey polygon).

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Although human activities along the coast of Matsunaga Bay appear to be associated with artificial structures (e.g., industrial plants, marinas, and lumber yards), natural habitats are still relatively well-preserved compared to areas in the eastern Seto Inland Sea29. The total population of towns within 5 km of the coastline of the bay can be expected to exceed 100,000 people, which ranks within the top 20% of administrative districts in Japan, including prefectures, towns, villages, and the 23 wards of Tokyo32.
Data collection
We conducted one field survey in September 2016 (summer) and another in January 2017 (winter) to collect data on benthic community structures and environmental conditions in each habitat. We established five sampling sites within each habitat to obtain replicated samples. To reduce biases due to tidal cycle, we performed all field sampling and measurements at high tide, when all habitats were underwater.
We used a standard sample area (approximately 0.15 m2 per sample) at each sampling site irrespective of sampling method to obtain comparable data on benthic communities. Some of our sampling methods involved Smith–McIntyre grab samplers and quadrats that could not cover the standard sample area in a single sample; for these methods, we combined the data from three samples to make up a single sampling site. At BW, benthic macrofauna (hereafter referred to as “benthos”) samples were collected by SCUBA divers. We established 15 sampling positions in a 5 × 3 grid (i.e., five depths [sampling sites] and three replicates) based on distance from the seafloor at the breakwater wall (Supplementary Fig. S1). The SCUBA divers used scraper blades, 0.1-mm mesh bags, and 22.5 cm × 22.5 cm quadrats because the benthic communities were mainly composed of sessile organisms. At EB, we employed different sampling methods for the above- and belowground components. SCUBA divers collected aboveground samples of eelgrass-associated benthos and eelgrass shoots using a mesh bag (mesh size: 0.1 mm; bag diameter: 45 cm). They then cut away the eelgrass shoots near each aboveground site and collected belowground samples of the benthos on top of and within the sediment by using the bucket part of a Smith–McIntyre grab sampler (sampling area: 22.5 cm × 22.5 cm). At IF and SB, benthos and bottom sediment samples were collected from a ship using a Smith–McIntyre grab sampler (sampling area: 22.5 cm × 22.5 cm).
All benthos was extracted using a 1-mm sieve and preserved in buffered 10% formalin in the field as soon as possible after sampling. The samples were identified to the lowest possible taxonomic unit and counted in the laboratory. After identification, we organized the dominant benthic species (or taxa) according to their primary feeding types and common life forms with reference to the World Register of Marine Species (http://www.marinespecies.org/) and the literature. No vertebrate species were targeted in this study. We defined the primary life forms of adult benthic species on/in their substrates as “common life forms.”
Although differences in environmental conditions were not the focus of our study, we did assess whether there were considerable water quality differences among sites. The purpose of this assessment was to try to identify sites with similar conditions so that exogenous impacts on biological communities could be discounted as much as possible in the analysis. We measured water and sediment conditions at each sampling site (except at BW, where sediment conditions were not measured due to the absence of sediment). Prior to benthos and sediment sampling, we measured water temperature, salinity, pH, and dissolved oxygen concentration at each site at a depth directly above the seafloor by using a multi-parameter water quality meter (AAQ‐RINKO, JFE Advantech Co. Ltd., Japan). At BW, where the substrate (i.e., the breakwater) is oriented vertically (see Supplementary Fig. S1), we measured water conditions in the middle of the water column. We also measured temperature, pH, oxidation–reduction potential (ORP), water content, and median particle size (D50) in the sediment. Sediment temperature, pH, and ORP were measured by using a portable ion meter (IM-32P, DKK-TOA Co., Japan) immediately after each sample was collected. Sediment water content and D50 were measured in the laboratory once benthic species had been removed from the sample.
Data analysis
First, we identified how many species were shared between all habitat pairs to understand inter-habitat species-sharing relationships. Second, we compared species compositions and abundances among habitats using similarity indices and multivariate analysis (described below).
To detect species sharing in terms of species commonality and endemism among the four habitats, we classified benthic species into the following three categories: common, endemic, and shifting. Common species were defined as species that occurred across all habitats. Endemic species are those that were found in only one habitat. Shifting species were defined as those that occurred in multiple habitats (but not across all habitats) and therefore showed a broad allowable range of habitat types or conditions. To analyse the importance of habitat sets in maintaining local species diversity, we further categorized the shifting species into two- or three-habitat users (i.e., those that occurred in two or three different habitats). Moreover, we calculated the numbers and proportions (i.e., using the Jaccard similarity index) of shared species in each habitat pair to evaluate the potential strength of any inter-habitat relationships. The Jaccard similarity index ((J)) is calculated as follows:

$$ J = frac{{S_{alpha beta } }}{{S_{alpha } + S_{beta } – S_{alpha beta } }}, $$

where (S_{alpha }) is the number of species in habitat (alpha), (S_{beta }) is the number of species in habitat (beta), and (S_{alpha beta }) is the number of species that are shared among habitats (alpha) and (beta).
In terms of the functional groups, we analysed abundance matrices of abundant species grouped by primary feeding types and common life forms. Focusing on abundant species is a useful way to reflect the functional characteristics of biological communities14. Therefore, we identified the most abundant species from each sample before constructing the abundance data matrices. To determine how many species to select for analysis, we calculated the number e of equally-abundant species that would be required to obtain the Simpson diversity index of each community (i.e., the effective number of species33). We then selected e abundant species from each sample in rank order from most to least abundant. If multiple species of the same rank occupied this cut-off threshold, we selected all of them. This selection method, which is unique to our study, successfully identified dominant species that accounted for over 70% of the total abundance in each community in all habitats.
To compare benthic macrofaunal communities among habitats, we performed non‐metric multidimensional scaling (NMDS)34 based on dissimilarity matrices obtained by using the metaMDS function in the vegan package35. To compare species compositions, we constructed Jaccard dissimilarity matrices based on presence/absence data, and to compare species abundance and functional compositions, we constructed Bray–Curtis dissimilarity matrices based on abundance datasets. All abundance datasets were square-root transformed before calculating the Bray–Curtis dissimilarity matrices to reduce the influence of abundance bias. We accepted the NMDS ordinations if stress values were less than 0.2 to maintain the accuracy of the two‐dimensional ordinations34. Then, we tested the effects of habitat type and sampling time by conducting a two-way permutational multivariate analysis of variance (two-way PERMANOVA)36 using the adonis function in the vegan package. Here, we considered four habitats (Habitat), two sampling times (Time), and their interaction term as explanatory factors. Although our main focus was differences in community compositions among habitats at each sampling time, we also examined the magnitude of variation in each habitat by comparing two stable seasonal extremes (i.e., summer and winter). If the results of the PERMANOVA were significant, we performed post-hoc tests (pairwise PERMANOVA) to identify which pairs of community structures were significantly different by using the pairwise.perm.manova function in the RVAideMemoire package37. We used 9999 permutations for the NMDS ordination, PERMANOVA, and pairwise PERMANOVA. P-values calculated during the pairwise PERMANOVA were corrected using the false discovery rate method38. For the benthic community data collected at each sampling point at EB, above- and belowground datasets were integrated to reflect spatial representativeness (see Figs. S3 and S4). All analyses were performed using R version 3.5.139. More

The Litho-technical characterization of the deposit
The litho-technical characterisation of the sediments has resulted from: the geological inspection of the cores in the liners and of the undisturbed geotechnical samples; the paleogeographic reconstruction of the soil deposition29,39; the soil geotechnical index properties; the geochemical and the mineralogical analyses. Here-forth, Fig. 7a reports the litho-technical section N–N′ whose trace is shown in Fig. 7b.
Figure 7

(a) Litho-technical section N–N′; (b) I Bay and location of all the investigated sections. Key: (1) 2017 campaign projected borehole; (2) top of the calcareous bedrock according to30 (3) bathymetry (Port authority 1947–1978); (4) significant content of organic matter; (5) fishing net (anthropogenic material); (6) coastline; (7) stratigraphic contact; (8) 1stLTU; (9) 2ndLTU, of consistency from very soft to soft and occasional presence of sand or silty sand, from very loose to loose (a); (10) 3rdLTU, of consistency increasing with depth, from very soft to soft (a), from soft to firm (b), firm (c), stiff (d)66,67, and occasional layers rich in sand (e), gravel (f) and peaty levels (g); (11) Possible disturbed top layers of the ASP formation; (12) ASP formation, with clayey silt or silty clay of very stiff consistency, and sandy levels (Su = 200–500 kPa) (a), or Grey-bluish marly-silty clay (Su  > 500 kPa) (b).

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A First litho-technical unit, hereafter 1stLTU (light yellow colour in Fig. 7a), of about 1.5 m thickness, has been found to cover the whole deposit. It is formed of either clay with silt, or sandy to slightly sandy silt with clay, deposited in recent times up to present, according to the sedimentology and paleogeographic studies. The corresponding grading curves (Fig. 8) show that its clay fraction, CF, varies in the range 27–53%, its silt fraction, MF, in the range 39–57%, and its sand fraction, SF, is minor, except for site S1, close to the Porta Napoli channel (Fig. 2). It is rich in organic matter and the pocket penetrometer Su data (Su  More

Study area and organic fertilisers tested
Most of the Tebo and Geldia seasonal rivers catchments (the study area; coordinate system, UTM WGS 1984, 528600, 973100 –541700, 951600; Fig. 1) are located in the semi-arid Ethiopian Rift Valley. The catchment areas are categorised into two sub-areas in terms of major maize growing areas in Ethiopia: mid-altitude dry (1000–1600 m a.s.l.; annual rainfall 800–1000 mm) and mid-altitude moist (1600–1800 m a.s.l.; annual rainfall 1000–1250 mm) sub-areas12. These two categories cover 63% of the total maize growing area in Ethiopia. The major crops in the mid-altitude dry sub-area of the catchments area are sorghum, tef (Eragrostis tef), and maize, whereas those in the mid-altitude moist sub-area are wheat, tef, and maize12.
Figure 1

The study area (left figure) and the test-kosi pile (left in the right figure) and the fast compost pile (right in the right figure).

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Most households in the semi-arid Ethiopian Rift Valley hold continuously cropped maize fields (locally referred to as aradas26), which acquire fertility from the regular input of OFs, such as compost (locally, kosi) or household wastes27. Kosi is made from a variety of locally available organic materials, such as various types of animal dung, kitchen ash, crop residue, and feed refusals. These compost materials are piled up in the corners of house-yards for several months to a few years for decomposition27. Household wastes are substantially a variety of organic materials themselves that also comprise kosi. It is mainly a housewife who collects these organic materials through house-yard sweeping and dumps directly on the arada that adjoins the homestead every few days. It has been since the beginning of the 2000s when the district agricultural office began giving fast compost training to farmers28.
Compost maturity and stability tests
Microorganisms break down the chemical bonds of organic materials in the presence of oxygen and moisture, giving off heat. Rynk et al.29 found that maintaining a stable temperature of  > 62.8 °C within the compost pile, for more than three consecutive days had been effective for the destruction of most human pathogens, insect larvae, and weed seeds within the compost pile. Monitoring the volume of compost pile during the biodegradation process is another physical test to evaluate compost stability30,31. Thus, monitoring compost pile temperature (self-heating test) and volume can be used as a simple and rapid method for assessing compost maturity or stability32.
The pH values of compost usually increase during the early stage of composting generally to above 8, caused by the release of ammonia, and then decrease slowly but steadily as ammonium (NH4+) is nitrified to approach neutral values as compost matures33. However, numerous studies have demonstrated that pH trends and final values over the composting are highly dependent on feedstock materials32.
C:N ratio generally decreases throughout the composting process due to the C losses13; however, the wide variability in feedstocks leads to variability in the final C:N ratios in different composts, making it difficult to place an absolute limit on C:N ratio that will be applicable to all feedstocks32. The pH values and C:N ratio of compost are useful in compost maturity evaluation if initial and final values are compared and if it is monitored in conjunction with other parameters for compost maturity32.
Sánchez-Monedero et al.34, which analysed the evolution of the different forms of N during the composting of different feedstocks, found that the greatest concentration of NH4+ coincided with the most intense period of OM degradation, whereas the highest concentrations of nitrate (NO3−) were always produced at the end of maturation. They concluded that NO3− to NH4+ ratio is a clear indicator of the compost stability. Wichuk and McCartney32 recommended that, because the ratio varied in mature composts, monitoring the ratio several times throughout the different stages of the composting process, rather than relying on the final value alone. NO3−:NH4+ ratio can be an effective indicator to evaluate the stability of kosi, fast compost, and household wastes, which have the same organic materials in common but are the products being in different phases of the composting process.
Respirometry (CO2 evolution rate or O2 uptake rate) has been widely used to evaluate the microbial activity and therefore, the stability of a compost sample9. The equipment for respirometric assays based on CO2 evolution is generally simple and easy to use; however, the main disadvantage of these methods is that they are unable to distinguish between CO2 produced aerobically from that produced anaerobically9. Respirometric assays based on O2 uptake are the most accepted methods for determining the biological activity of material; however, their main disadvantage is that they need more expensive and troublesome instrumentation and more skilled labour9. Respirometric assays are not without flaws; nevertheless, many researchers recommended using either of the two respirometric assays or self-heating (in combination with a plant bioassay) to evaluate compost stability32.
Gómez-Brandón et al.13 compared several parameters and found that the change in dissolved organic carbon (DOC) with composting time gave a good indication of stability. However, the determination of DOC content requires expensive laboratory instruments such as absorption spectrophotometer. Instead, Wu et al.10 and Gómez-Brandón et al.13 found a significant correlation between DOC and microbial respiration. They referred to the way of evaluating compost stability based on CO2 evolution and assessing compost maturity based on phytotoxicity bioassay (seed germination). Compost maturity is generally determined by phytotoxicity bioassay32.
To date, no stand-alone method exists to assess compost maturity, mainly because of the wide variety of composting feedstocks and management practices35. A more thorough evaluation of both the stability and maturity states of compost could be obtained using a combination of tests32. An appropriate field test method would need to be rapid and sufficiently straightforward for operators to use32.
Considering these, (1) monitoring pile temperature and volume changes and (2) determinations of pH, OM, total N, and C:N ratio (total organic C to total N ratio) over the composting process; (3) determination of the final NO3−:NH4+ ratio; (4) CO2 evolution test; and (5) phytotoxicity bioassay were combined to evaluate stability and maturity of the OFs, i.e., kosi, fast compost, and household wastes, in this study. Besides, because weed proliferation in arada fields is the primary cause of maize yield decline36, (6) a weed seed germination test was conducted.
Monitoring of physical and chemical changes in compost piles
Five farmers who participated in the fast compost training from each of the two sub-areas were requested to make kosi and fast compost. The temperatures and volumes of the kosi and compost piles were monitored only in the mid-altitude dry sub-area over 90 days from the commencement day when the OF feedstock (organic materials) had been piled up (the kosi pile prepared was referred to as “test-kosi pile”). This test was conducted once in each 2014 and 2015. To ease the pile volume measurement, the organic materials collected from the farmers’ backyards were piled up in a rectangular wooden frame (1 m in width, 1 m in length, 1.5 m in depth; Fig. 1). Fast compost was made following the technical guidance of MoARD28: each 20-cm-deep layer of the (1) maize and sorghum stalks, (2) animal dungs, and (3) tef residue and feed refusals were piled up in turn until it reached the top of the pile. The total depth of each material layer was arranged to be the same between (1), (2), and (3). (4) Ash (0.5 kg m−2) was sprinkled over each layer of the (1) and (3). Some humic soil (1–2 cm deep) was spread on top of each layer. Water was regularly added to keep the pile moist. Once every 21 days, all the organic materials in the piles were turned over to mix the materials. This process was repeated to make fast compost ready in 3 months28. The same varieties of the organic materials were used for the kosi feedstock, but those compositions and proportions were decided by the individual farmer. The fast compost pile had a ceiling so that rainfall did not enter inside, whereas the test-kosi pile was rainfed (Fig. 1). Daily rainfall was measured by a simple rain-gauge installed near the test-kosi piles.
Farmers in Eastern and Southern Africa carry OFs from their kraals (cattle parking lot) and cattle sheds to the field and integrate it into their fields by ploughing operations carried out a couple of weeks later37. Farmers in the semi-arid Ethiopian Rift Valley plough maize fields 3–4 times and tef fields 4–5 times before seeding36. For both the crops, many farmers integrate the applied OFs into the soil at the ploughing time implemented immediately before the seeding or at the previous ploughing time. For maize, this period corresponds to the beginning of the rainy season from late-May to the beginning of June36. As soon as they carry kosi to their fields, they begin the next kosi making. Thus, the test-kosi and fast compost samples were begun to prepare on 4th June in 2014 and 2nd June in 2015.
Daily temperatures were measured at randomly selected five points in the test-kosi and fast compost piles by a temperature probe (SINWA digital thermometer H1). Those mean values were designated as the daily temperature. The depths of the piles were measured every 10 days over the monitoring period, which were converted into volume. Similarly, pile pH (HORIBA portable pH meter D-210P) and total N and OM contents (loss-on-ignition method; ignition temperature 500({}^{o}c), overnight) of the piles were determined.
Only for pH, it was measured at the 2nd day of the monitoring together with the regular measurement made every 10 days, including the 1st day of the monitoring. Total organic C was estimated from the OM content determined38, which was used to determine C:N ratio.
Total N in the sample was determined by the following on-site proximate analysis methods39 (Table S1 in Supplementary Information online): after 1.0 g (dry matter) of the sample was placed in a 500-mL tall beaker and 8 mL of sulphuric acid was added, 4 mL of 35% hydrogen peroxide was added twice, which was capped with a dish. After a vigorous chemical reaction was settled, the tall beaker was heated for 5 min. After the beaker was cooled down, 2 mL of hydrogen peroxide was added, and then heated for 3 min; this operation was repeated six times. The solution was transferred to a volumetric flask, and water was filled to the marked line of 100 mL. Because Reflectquant ammonium test (0.2–7.0 mg L−1 NH4+) requires a test solution regulated in pH 4–13, after 29 mL of water was added to 1 mL of the solution, 0.4 g of calcium hydroxide was added, which was stirred hard. The filtrate was reacted with a Reflectquant ammonium test, and NH4+ was determined with an RQFlex in a thermostat bath kept at 30 °C. A standard solution for NH4+ (3.0 μg mL−1) was simultaneously determined to correct determined NH4+ in the sample. Corrected NH4+ in the sample (X) was converted to total N (Y) using the equation39, Y = 0.830 X (Table S1 in Supplementary Information online).
In the village in the mid-altitude dry sub-area where the sample piles were established, abundant pumice flow deposits were observed in soils; this was so in the humic soil added to the compost pile. The weight of pumice in the samples collected from the test-kosi and fast compost piles, if any, was measured, which was deducted from the crude ash mass measured after combustion.
Weed seeds germination test
A weed germination test was conducted in 2015 as follow: a fast compost sample and test-kosi sample were collected from the each of the 5 fast compost and 5 test-kosi piles set in the two sub-areas in 90 days of the monitoring period. Thus, 10 fast compost samples and 10 test-kosi samples were prepared. Besides, 5 kosi and 5 household wastes samples were collected from 5 farmers’ backyards in both the sub-areas (a kosi sample collected from farmers’ backyard was referred to as a farmer-kosi sample). In collecting a household wastes sample from a farmer’s backyard, approximately a 10 g sample was collected from each of the five places in the backyard, mixed to make it a composite sample. From each of the 10 fast compost, 10 test-kosi, 10 farmer-kosi, and 10 household wastes samples, 8 samples were collected to prepare 80 fast compost, 80 test-kosi, 80 farmer-kosi, and 80 household wastes samples. A filter paper was placed on a petri dish 9 cm in diameter, on which a sample was spread. The sample in the petri dish was uniformly watered and placed in the constant temperature room (kept at 25 °C) in Melkassa Agricultural Research Center for 10 days. Species of the plants germinated were identified at the National herbarium of Ethiopia. The 10 fast compost, 10 test-kosi, 10 farmer-kosi, and 10 household wastes samples tested in the weed germination test were also used for the NO3−:NH4+ ratio determination test, CO2 evolution test, and phytotoxicity bioassay.
NO3:NH4+ ratio determination
NH4+ and NO3− in the 10 fast compost samples, 10 test-kosi samples, and 10 farmer-kosi samples were determined, from which NO3−:NH4+ ratios were calculated.
Tanahashi et al.40 found that cattle and swine manures contained the fraction of NH4+ that cannot be extracted by potassium chloride (ammonium magnesium phosphate; MAP). They examined 59 cattle manures (26 dairy, 28 beef, and 5 dairy and beef mix) and 52 swine manures made by various production methods for an appropriate extraction method of NH4+ containing MAP40. As a result, they found that inorganic N containing MAP extracted by 0.5 mol L−1 hydrochloric acid in the condition of the 1–10 ratio of dry manure weight (g) and extract volume (mL) had the strongest relationship (R2 = 0.851, including some outliers) with inorganic N available in the culture soil used for laboratory incubations (30 °C, 4 weeks). Thus, this study used hydrochloric acid to extract inorganic N from the OFs. After 0.5 mol L−1 hydrochloric acid solution (100 mL) was added to 10 g of the sample, the solution was stirred by a mixer for 2 min to make an extract. The extract was diluted, if necessary, and was reacted with Reflectquant ammonium test (measuring range of 0.2–7.0 mg L−1 NH4+) and Reflectquant nitrate test (5–225 mg L−1 NO3−) to determine NH4+ and NO3− contents with an RQFlex, respectively (Table S1 in Supplementary Information online).
CO2 evolution test
Using a simple respirometric instrument (Fig. 2)41, the 10 fast compost samples, 10 test-kosi samples, 10 farmer-kosi samples, and 10 household wastes samples were incubated at 30 °C for 21 h in the glass flask (Fig. 2), and CO2 produced was determined. A 0.5 g air-dried sample was mixed with an air-dried 10 g arada soil collected from the mid-altitude dry sub-area and water (60% soil water saturation). A small container that contained a 2 g sodium hydroxide (carbon dioxide absorbent) was placed in the flask. The volume of water sucked by the measuring pipette was measured. From the volume of water measured, the volume of water measured at the control treatment (only the culture soil) was subtracted to obtain the volume of CO2 produced. CO2 evolved is soluble in aqueous solutions, and the solubility is pH-dependent9. Thus, the original pH of each sample was determined. Calcisols42 (Endopetric Hypercalcic Calcisol; clay loam) were typical soils in the arada fields in the mid-altitude dry sub-area26.
Figure 2

The instrument for the CO2 evolution test41.

Full size image

Phytotoxicity bioassay (garden cress germination test)
For the 10 fast compost, 10 test-kosi, 10 farmer-kosi, 10 household wastes, and 10 arada soil samples, phytotoxicity bioassays were conducted.
In a garden cress germination test, the presence of phytotoxic substances is usually determined by a garden cress germination index (GI) that is calculated by the following equation:

$${text{GI}}, = ,left( {{text{G}}_{{text{t}}} /{text{G}}_{{text{c}}} } right), times ,({text{L}}_{{text{t}}} /{text{L}}_{{text{c}}} )$$

where Gt = mean germination for treatment, Gc = mean germination for distilled water control, Lt = mean radicle length for treatment, and Lc = mean radicle length for distilled water control. The germination index was rated as follows43: 1.0–0.8, no inhibition of plant growth; 0.8–0.6, mild inhibition; 0.6–0.4, strong inhibition;  More

1.
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).
Article  Google Scholar 

4.
Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).
ADS  Article  Google Scholar 

5.
Smale, D. A., Wernberg, T. & Vanderklift, M. A. Regional-scale variability in the response of benthic macroinvertebrate assemblages to a marine heatwave. Mar. Ecol. Prog. Ser. 568, 17–30 (2017).
ADS  Article  Google Scholar 

6.
Benthuysen, J. A., Oliver, E. C. J., Feng, M. & Marshall, A. G. Extreme marine warming across tropical Australia during austral summer 2015–2016. J. Geophys. Res. Oceans 123, 1301–1326 (2018).
ADS  Article  Google Scholar 

7.
Della-Marta, P., Haylock, M., Luterbacher, J. & Wanner, H. Doubled length of western European summer heat waves since 1880. J. Geophys. Res. 112, D15103 (2007).
ADS  Article  Google Scholar 

8.
Oswald, E. & Rood, R. A trend analysis of the 1930–2010 extreme heat events in the Continental United States. J. Appl. Meteorol. Climatol. 53, 565–582 (2014).
ADS  Article  Google Scholar 

9.
Perkins, S. & Alexander, L. V. On the measurement of heat waves. J. Clim. 26, 4500–4517 (2013).
ADS  Article  Google Scholar 

10.
Lima, F. P. & Wethey, D. S. Three decades of high-resolution coastal sea surface temperatures reveal more than warming. Nat. Commun. 3, 704 (2012).
ADS  PubMed  Article  CAS  Google Scholar 

11.
Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanography 31, 162 (2018).
Article  Google Scholar 

12.
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
ADS  Article  Google Scholar 

13.
Harley, C. D. G. et al. The impacts of climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).
ADS  PubMed  Article  Google Scholar 

14.
Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).
ADS  Article  Google Scholar 

15.
Caputi, N. et al. Management adaptation of invertebrate fisheries to an extreme marine heat wave event at a global warming hot spot. Ecol. Evol. 6, 3583–3593 (2016).
PubMed  PubMed Central  Article  Google Scholar 

16.
Caputi, N. et al. Factors affecting the recovery of invertebrates stocks from the 2011 Western Australian extreme marine heatwave. Front. Mar. Sci. 6, 484 (2019).
Article  Google Scholar 

17.
Seuront, L., Nicastro, K. R., Zardi, G. I. & Goberville, E. Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis. Sci. Rep. 9, 17498 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 

18.
Murphy, E. A. K. & Reidenbach, M. A. Oxygen transport in periodically ventilated polychaete burrows. Mar. Biol. 163, 208 (2016).
Article  CAS  Google Scholar 

19.
Goulletquer, P. et al. Summer mortality of the Pacific cupped oyster Crassostrea gigas in the Bay of Marennes-Oléron (France). In Mariculture Committee CM 1998/CC: 14 (1998).

20.
Li, M., Lei, Y., Li, T. & Jian, Z. Impact of temperature on intertidal foraminifera: Results from laboratory culture experiment. J. Exp. Mar. Biol. Ecol. 520, 151224 (2019).
Article  Google Scholar 

21.
Pörtner, H. O. Climate change and temperature-dependent biogeography: Oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88, 137–146 (2001).
ADS  PubMed  Article  Google Scholar 

22.
Pörtner, H. O. Integrating climate-related stressor effects on marine organisms: Unifying principles linking molecule to ecosystem-level changes. Mar. Ecol. Prog. Ser. 470, 273–290 (2012).
ADS  Article  CAS  Google Scholar 

23.
Straub, S. C. et al. Resistance, extinction, and everything in between—The diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).
Article  Google Scholar 

24.
Stillman, J. H. & Somero, G. N. Adaptation to temperature stress and aerial exposure in congeneric species of intertidal porcelain crabs (genus Petrolisthes): Correlation of physiology, biochemistry and morphology with vertical distribution. J. Exp. Biol. 199, 1845–1855 (1996).
CAS  PubMed  Google Scholar 

25.
Joint, I. & Smale, D. A. Marine heatwaves and optimal temperatures for microbial assemblage activity. FEMS Microbiol. Ecol. 93, 243 (2017).
Article  CAS  Google Scholar 

26.
Pörtner, H. O. & Farrell, A. P. Physiology and climate change. Nature 322, 690–692 (2008).
Google Scholar 

27.
Wu, F. et al. Effects of seawater pH and temperature on foraging behavior of the Japanese stone crab Charybdis japonica. Mar. Pollut. Bull. 120, 99–108 (2017).
CAS  PubMed  Article  Google Scholar 

28.
da Vianna, B. S., Miyai, C. A., Augusto, A. & Costa, T. M. Effects of temperature increase on the physiology and behavior of fiddler crabs. Physiol. Behav. 215, 112765 (2020).
CAS  PubMed  Article  Google Scholar 

29.
François, F., Poggiale, J.-C., Durbec, J.-P. & Stora, G. A new approach for the modelling of sediment reworking induced by a macrobenthic community. Acta. Biotheor. 45, 295–319 (1997).
Article  Google Scholar 

30.
Kristensen, E. et al. What is bioturbation? the need for a precise definition for fauna in aquatic sciences. Mar. Ecol. Prog. Ser. 446, 285–302 (2012).
ADS  Article  Google Scholar 

31.
Piot, A., Nozais, C. & Archambault, P. Meiofauna affect the macrobenthic biodiversity—Ecosystem functioning relationship. Oikos 123, 1–11 (2013).
Google Scholar 

32.
Bonaglia, S. et al. Meiofauna improve oxygenation and accelerate sulfide removal in the seasonally hypoxic seabed. Mar. Environ. Res. 159, 104968 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Bonaglia, S., Nascimento, F. J. A., Bartoli, M., Klawonn, I. & Brüchert, V. Meiofauna increases bacterial denitrification in marine sediments. Nat. Commun. 5, 5133 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Mermillod-Blondin, F. & Rosenberg, R. Ecosystem engineering: The impact of bioturbation on biogeochemical processes in marine and freshwater benthic habitats. Aquat. Sci. 68, 434–442 (2006).
CAS  Article  Google Scholar 

35.
Kristensen, E. Mangrove crabs as ecosystem engineers; with emphasis on sediment processes. J. Sea Res. 59, 30–43 (2008).
ADS  Article  Google Scholar 

36.
Pascal, L., Maire, O., Deflandre, B., Romero-Ramirez, A. & Grémare, A. Linking behaviours, sediment reworking, bioirrigation and oxygen dynamics in a soft-bottom ecosystem engineer: The mud shrimp Upogebia pusilla (Petagna 1792). J. Exp. Mar. Biol. Ecol. 516, 67–78 (2019).
Article  Google Scholar 

37.
Risgaard-Petersen, N. et al. Evidence for complete denitrification in a benthic foraminifer. Nature 443, 93–96 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

38.
Høgslund, S., Revsbech, N. P., Cedhagen, T., Nielsen, L. P. & Gallardo, V. A. Denitrification, nitrate turnover, and aerobic respiration by benthic foraminiferans in the oxygen minimum zone off Chile. J. Exp. Mar. Biol. Ecol. 359, 85–91 (2008).
Article  CAS  Google Scholar 

39.
Pike, J., Bernhard, J. M., Moreton, S. & Butler, I. Microbiorrigation of marine sediments in dysoxic environments: Implication for early sediment fabric formation and diagenetic processes. Geology 29, 923–926 (2001).
ADS  Article  Google Scholar 

40.
Woulds, C. et al. Oxygen as a control on seafloor biological communities and their roles in sedimentary carbon cycling. Limnol. Oceanogr. 52, 1698–1709 (2007).
ADS  CAS  Article  Google Scholar 

41.
Bernhard, J. M., Mollo-Christensen, E., Eisenkolb, N. & Starczak, V. R. Tolerance of allogromid Foraminifera to severaly elevated carbon dioxide concentrations: Implications to future ecosystem functioning and paleoceanographic interpretations. Glob. Planet. Change 65, 107–114 (2009).
ADS  Article  Google Scholar 

42.
Bradshaw, J. Laboratory experiments on the ecology of foraminifera. Contrib. Cushman Found. Foramin. Res. 12, 87–106 (1961).
Google Scholar 

43.
Pascal, P.-Y., Dupuy, C., Richard, P. & Niquil, N. Bacterivory in the common foraminifer Ammonia tepida: Isotope tracer experiment and the controlling factors. J. Exp. Mar. Biol. Ecol. 359, 55–61 (2008).
CAS  Article  Google Scholar 

44.
Wukovits, J., Enge, A. J., Wanek, W., Watzka, M. & Heinz, P. Increased temperature causes different carbon and nitrogen processing patterns in two common intertidal foraminifera (Ammonia tepida and Haynesina germanica). Biogeosciences 14, 2815–2829 (2017).
ADS  CAS  Article  Google Scholar 

45.
Schmidt, C., Heinz, P., Kucera, M. & Uthicke, S. Temperature-induced stress leads to bleaching in larger benthic foraminifera hosting endosymbiotic diatoms. Limnol. Oceanogr. 56, 1587–1602 (2011).
ADS  Article  Google Scholar 

46.
Stuhr, M. et al. Variable thermal stress tolerance of the reef-associated symbiont-bearing foraminifera Amphistegina linked to differences in symbiont type. Coral Reefs 37, 811–824 (2018).
ADS  Article  Google Scholar 

47.
Gross, O. Influence of temperature, oxygen and food availability on the migrational activity of bathyal benthic foraminifera: Evidence by microcosm experiments. Hydrobiologia 426, 123–137 (2000).
Article  Google Scholar 

48.
Deldicq, N., Seuront, L., Langlet, D. & Bouchet, V. Assessing behavioural traits of benthic foraminifera: Implications for sediment mixing. Mar. Ecol. Prog. Ser. 643, 21–31 (2020).
ADS  Article  Google Scholar 

49.
Seuront, L. & Bouchet, V. M. P. The devil lies in details: New insights into the behavioural ecology of intertidal foraminifera. J. Foramin. Res. 45, 390–401 (2015).
Article  Google Scholar 

50.
van Dam, J. W., Negri, A. P., Mueller, J. F., Altenburger, R. & Uthicke, S. Additive pressures of elevated sea surface temperatures and herbicides on symbiont-bearing foraminifera. PLoS ONE 7, e33900 (2012).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Sinutok, S., Hill, R., Kühl, M., Doblin, M. A. & Ralph, P. J. Ocean acidification and warming alter photosynthesis and calcification of the symbiont-bearing foraminifera Marginopora vertebralis. Mar. Biol. 161, 2143–2154 (2014).
CAS  Article  Google Scholar 

52.
Alve, E. & Murray, J. W. Temporal variability in vertical distributions of live (stained) intertidal foraminifera, Southern England. J. Foramin. Res. 31, 12–24 (2001).
Article  Google Scholar 

53.
Debenay, J.-P., Bicchi, E., Goubert, E. & Armynot-du-Châtelet, E. Spatio-temporal distribution of benthic foraminifera in relation to estuarine dynamics (Vie estuary, Vendée, W France). Estuar. Coast. Shelf Sci. 67, 181–197 (2006).
ADS  Article  Google Scholar 

54.
Morvan, J. et al. Patchiness and life cycle of intertidal foraminifera: Implication for environmental and paleoenvironmental interpretation. Mar. Micropaleontol. 61, 131–154 (2006).
ADS  Article  Google Scholar 

55.
Francescangeli, F. et al. Multidisciplinary study to monitor consequences of pollution on intertidal benthic ecosystems (Hauts de France, English Channel, France): Comparison with natural areas. Mar. Environ. Res. 160, 105034 (2020).
CAS  PubMed  Article  Google Scholar 

56.
Amara, R., Meziane, T., Gilliers, C., Hermel, G. & Laffargues, P. Growth and condition indices in juveniles sole Solea solea measured to assess the quality of essential fish habitat. Mar. Ecol. Prog. Ser. 351, 201–208 (2007).
ADS  Article  Google Scholar 

57.
Langlet, D., Bouchet, V. M. P., Delaeter, C. & Seuront, L. Motion behavior and metabolic response to microplastic leachates in the benthic foraminifera Haynesina germanica. J. Exp. Mar. Biol. Ecol. 529, 151395 (2020).
Article  Google Scholar 

58.
Cesbron, F. et al. Sequestered chloroplasts in the benthic foraminifer Haynesina germanica: Cellular organization, oxygen fluxes and potential ecological implications. J. Foramin. Res. 47, 268–278 (2017).
Article  Google Scholar 

59.
Geslin, E. et al. Oxygen respiration rates of benthic foraminifera as measured with oxygen microsensors. J. Exp. Mar. Biol. Ecol. 396, 108–114 (2011).
Article  Google Scholar 

60.
Schindelin, J. et al. Fiji : An open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
CAS  PubMed  Article  Google Scholar 

61.
Seuront, L. Fractals and Multifractals in Ecology and Aquatic Science (CRC Press, Boca Raton, 2010).
Google Scholar 

62.
Seuront, L. On uses, misuses and potential abuses of fractal analysis in zooplankton behavioral studies: A review, a critique and a few recommendations. Phys. A 432, 410–434 (2015).
MathSciNet  MATH  Article  Google Scholar 

63.
Seuront, L. & Cribb, N. Fractal analysis provides new insights into the complexity of marine mammal behavior: A review, two methods, their application to diving and surfacing patterns, and their relevance to marine mammal welfare assessment. Mar. Mamm. Sci. 33, 847–879 (2017).
Article  Google Scholar 

64.
Revsbech, N. P. An oxygen microsensor with a guard cathode. Limnol. Oceanogr. 34, 474–478 (1989).
ADS  CAS  Article  Google Scholar 

65.
Glock, N. et al. Metabolic preference of nitrate over oxygen as an electron acceptor in foraminifera from the Peruvian oxygen minimum zone. PNAS 116, 2860–2865 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

66.
Choquel, C. et al. Denitrification by benthic foraminifera and their contribution to N-loss from a fjord environment. Biogeosciences 18, 327–341 (2021).
ADS  Article  Google Scholar 

67.
Ramsing, N. & Gundersen, J. Seawater and Gases-Tabulated Physical Parameters of Interest to People Working with Microsensors in Marine Systems. (Unisense Internal Report, 1994).

68.
Zar, J. Biostatistical Analysis 5th edn. (Pearson Education, London, 2009).
Google Scholar 

69.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2019).

70.
Bouchet, V. M. P. & Seuront, L. Strength may lie in numbers: Intertidal foraminifera non-negligible contribution to surface sediment reworking. OJMS 10, 131–140 (2020).
Article  Google Scholar 

71.
Seuront, L. Behavioral fractality in marine copepods: Endogenous rhythms versus exogenous stressors. Phys. A 390, 250–256 (2011).
Article  Google Scholar 

72.
Seuront, L. Hydrocarbon contamination decreases mating success in a marine planktonic copepod. PLoS ONE 6, e26283 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Seuront, L. When complexity rimes with sanity: Loss of fractal and multifractal behavioural complexity as an indicator of sub-lethal contaminations in zooplankton. In Marine Productivity: Perturbation and Resilience of Socio-ecosystems (eds Ceccaldi, H.-J. et al.) 129–137 (Springer, Berlin, 2015).
Google Scholar 

74.
Harrison, S. & Phizacklea, A. Vertical temperature gradient in muddy intertidal sediments in the Forth estuary, Scotland. Limnol. Oceanogr. 32, 954–963 (1987).
ADS  Article  Google Scholar 

75.
Bouchet, V. M. P., Debenay, J.-P., Sauriau, P.-G., Radford-Knoery, J. & Soletchnik, P. Effects of short-term environmental disturbances on living benthic foraminifera during the Pacific oyster summer mortality in the Marennes-Oléron Bay (France). Mar. Environ. Res. 64, 358–383 (2007).
CAS  PubMed  Article  Google Scholar 

76.
Somero, G. N. Thermal physiology and vertical zonation of intertidal animals: Optima, limits, and costs of living. Integr. Comp. Biol. 42, 780–789 (2002).
PubMed  Article  Google Scholar 

77.
Stillman, J. H. Causes and consequences of thermal tolerance limits in rocky intertidal porcelain crabs, genus Petrolisthes. Integr. Comp. Biol. 42, 790–796 (2002).
PubMed  Article  Google Scholar 

78.
Pörtner, H. O., Peck, L. & Somero, G. Thermal limits and adaptation in marine Antarctic ectotherms: An integrative view. Phil. Trans. R. Soc. B 362, 2233–2258 (2007).
PubMed  Article  CAS  Google Scholar 

79.
Przeslawski, R., Zhu, Q. & Aller, R. Effects of abiotic stressors on infaunal burrowing and associated sediment characteristics. Mar. Ecol. Prog. Ser. 392, 33–42 (2009).
ADS  CAS  Article  Google Scholar 

80.
Chapperon, C. & Seuront, L. Behavioral thermoregulation in a tropical gastropod: Links to climate change scenarios. Glob. Change Biol. 17, 1740–1749 (2011).
ADS  Article  Google Scholar 

81.
Tsubokura, T., Goshima, S. & Nakao, S. Seasonal horizontal and vertical distribution patterns of the supralittoral amphipod Trinorchestia trinitatis in relation to environmental variables. J. Crust. Biol. 17, 674–686 (1997).
Article  Google Scholar 

82.
Lardies, M. A., Clasing, E., Navarro, J. M. & Stead, R. A. Effects of environmental variables on burial depth of two infaunal bivalves inhabiting a tidal flat in southern Chile. J. Mar. Biol. Assoc. U.K. 81, 809–816 (2001).
Article  Google Scholar 

83.
Diaz, J. A. & Cabezas-Diaz, S. Seasonal variation in the contribution of different behavioural mechanisms to lizard thermoregulation. Funct. Ecol. 18, 867–875 (2004).
Article  Google Scholar 

84.
Lencioni, V. Survival strategies of freshwater insects in cold environments. J. Limnol. 63, 45–55 (2004).
Article  Google Scholar 

85.
Dubois, Y., Blouin-Demers, G., Shipley, B. & Thomas, D. Thermoregulation and habitat selection in wood turtles Glyptemys insculpta: Chasing the sun slowly. J. Anim. Ecol. 78, 1023–1032 (2009).
CAS  PubMed  Article  Google Scholar 

86.
Chapperon, C. & Seuront, L. Keeping warm in the cold: On the thermal benefits of aggregation behaviour in an intertidal ectotherm. J. Therm. Biol. 37, 640–647 (2012).
Article  Google Scholar 

87.
Koo, B. J., Kim, S.-H. & Hyun, J.-H. Feeding behavior of the ocypodid crab Macrophthalmus japonicus and its effects on oxygen-penetration depth and organic-matter removal in intertidal sediments. Estuar. Coast. Shelf Sci. 228, 106366 (2019).
CAS  Article  Google Scholar 

88.
Gosling, E. Bivalve Molluscs Biology, Ecology and Culture (Blackwell Publishing Ltd, Oxford, 2004).
Google Scholar 

89.
Verdelhos, T., Marques, J. C. & Anastácio, P. Behavioral and mortality responses of the bivalves Scrobicularia plana and Cerastoderma edule to temperature, as indicator of climate change’s potential impacts. Ecol. Ind. 58, 95–103 (2015).
Article  Google Scholar 

90.
Angilletta, M. J. Looking for answers to questions about heat stress: Researchers are getting warmer. Funct. Ecol. 23, 231–232 (2009).
Article  Google Scholar 

91.
Lombard, F., Labeyrie, L., Michel, E., Spero, H. J. & Lea, D. W. Modelling the temperature dependent growth rates of planktic foraminifera. Mar. Micropaleontol. 70, 1–7 (2009).
ADS  Article  Google Scholar 

92.
Fraser, K. P. P., Clarke, A. & Peck, L. S. Low-temperature protein metabolism: Seasonal changes in protein synthesis and RNA dynamics in the Antarctic limpet Nacella concinna Strebel 1908. J. Exp. Biol. 205, 3077–3086 (2002).
CAS  PubMed  Google Scholar 

93.
Gilbert, C. et al. One for all and all for one: The energetic benefits of huddling in endotherms. Biol. Rev. 85, 545–569 (2010).
PubMed  Google Scholar 

94.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
ADS  Article  Google Scholar 

95.
Lou, F., Gao, T. & Han, Z. Transcriptome analyses reveal alterations in muscle metabolism, immune responses and reproductive behavior of Japanese mantis shrimp (Oratosquilla oratoria) at different cold temperature. Comp. Biochem. Physiol. D Genomics Proteomics 32, 100615 (2019).
CAS  PubMed  Article  Google Scholar 

96.
Wieser, W. Temperature relations of ectotherms: A speculative review. In Effects of Temperature on Ectothermic Organisms: Ecological Implications and Mechanisms of Compensation (ed. Wieser, W.) 1–23 (Springer, Berlin, 1973).
Google Scholar 

97.
Price, R. & Warwick, R. M. The effect of temperature on the respiration rate of meiofauna. Oecologia 44, 145–148 (1980).
ADS  CAS  PubMed  Article  Google Scholar 

98.
Stillman, J. H. & Somero, G. N. A comparative analysis of the upper thermal tolerance limits of Eastern Pacific porcelain crabs, genus Petrolisthes : Influences of latitude, vertical zonation, acclimation, and phylogeny. Physiol. Biochem. Zool. 73, 200–208 (2000).
CAS  PubMed  Article  Google Scholar 

99.
Vernberg, W. & Vernberg, F. Environmental Physiology of Marine Organisms (Springer, Berlin, 1972).
Google Scholar 

100.
Mestre, N. C., Brown, A. & Thatje, S. Temperature and pressure tolerance of larvae of Crepidula fornicata suggest thermal limitation of bathymetric range. Mar. Biol. 160, 743–750 (2013).
Article  Google Scholar 

101.
Meysman, F. J. R., Galaktionov, O. S., Glud, R. N. & Middelburg, J. J. Oxygen penetration around burrows and roots in aquatic sediments. J. Mar. Res. 68, 309–336 (2010).
CAS  Article  Google Scholar 

102.
Mouret, A. et al. Oxygen and organic carbon fluxes in sediments of the Bay of Biscay. Deep-Sea Res. I(57), 528–540 (2010).
Article  CAS  Google Scholar 

103.
Bernhard, J. M. Experimental and field evidence of Antarctic foraminiferal tolerance to anoxia and hydrogen sulfide. Mar. Micropaleontol. 20, 203–213 (1993).
ADS  Article  Google Scholar 

104.
Maire, O. et al. How does macrofaunal bioturbation influence the vertical distribution of living benthic foraminifera?. Mar. Ecol. Prog. Ser. 561, 83–97 (2016).
ADS  Article  Google Scholar 

105.
Richirt, J. et al. Foraminiferal community response to seasonal anoxia in Lake Grevelingen (the Netherlands). Biogeosciences 17, 1415–1435 (2020).
ADS  Article  Google Scholar 

106.
Moens, T. & Vincx, M. Temperature, salinity and food thresholds in two brackish-water bacterivorous nematode species: Assessing niches from food absorption and respiration experiments. J. Exp. Mar. Biol. Ecol. 243, 137–154 (2000).
Article  Google Scholar 

107.
Pinko, D., Abramovich, S. & Titelboim, D. Foraminiferal holobiont thermal tolerance under climate change—Roommates problems or successful collaboration?. Biogeosciences 17, 2341–2348 (2020).
ADS  Article  Google Scholar 

108.
Maire, O., Duchêne, J., Bigot, L. & Grémare, A. Linking feeding activity and sediment reworking in the deposit-feeding bivalve Abra ovata with image analysis, laser telemetry, and luminophore tracers. Mar. Ecol. Prog. Ser. 351, 139–150 (2007).
ADS  Article  Google Scholar 

109.
Ouellette, D. et al. Effects of temperature on in vitro sediment reworking processes by a gallery biodiffusor, the polychaete Neanthes virens. Mar. Ecol. Prog. Ser. 266, 185–193 (2004).
ADS  Article  Google Scholar 

110.
Guarini, J., Blanchard, G., Gros, P., Gouleau, D. & Bacher, C. Dynamic model of the short-term variability of microphytobenthic biomass on temperate intertidal mudflats. Mar. Ecol. Prog. Ser. 195, 291–303 (2000).
ADS  Article  Google Scholar 

111.
Jauffrais, T. et al. Effect of light on photosynthetic efficiency of sequestered chloroplasts in intertidal benthic foraminifera (Haynesina germanica and Ammonia tepida). Biogeosciences 13, 2715–2726 (2016).
ADS  Article  Google Scholar 

112.
Jauffrais, T. et al. Response of a kleptoplastidic foraminifer to heterotrophic starvation: Photosynthesis and lipid droplet biogenesis. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiz046 (2019).
Article  PubMed  Google Scholar  More

1.
Avise, J. C. Phylogeography: retrospect and prospect. J. Biogeogr. 36, 3–15 (2009).
Article  Google Scholar 
2.
Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999).
Article  Google Scholar 

3.
Linder, P. H. Phylogeography. J. Biogeogr. 44, 243–244 (2017).
Article  Google Scholar 

4.
Song, H., Buhay, J. E., Whiting, M. F. & Crandall, K. A. Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proc. Natl. Acad. Sci. 105, 13486–13491 (2008).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

5.
Philippe, H. et al. Pitfalls in supermatrix phylogenomics. Pitfalls supermatrix phylogenomics. Eur. J. Taxon. 28, 3. https://doi.org/10.5852/ejt.2017.283 (2017).
Article  Google Scholar 

6.
Villaverde, T. et al. Bridging the micro- and macroevolutionary levels in phylogenomics: Hyb-Seq solves relationships from populations to species and above. New Phytol. 220, 636–650 (2018).
PubMed  Article  PubMed Central  Google Scholar 

7.
Vos, P. et al. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Res. 23, 4407–4414 (1995).
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Meudt, H. M. & Clarke, A. C. Almost forgotten or latest practice? AFLP applications, analyses and advances. Trends Plant Sci. 12, 106–117 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Paun, O. & Schönswetter, P. Amplified fragment length polymorphism: an invaluable fingerprinting technique for genomic, transcriptomic, and epigenetic studies. Methods Mol. Biol. 862, 75–87 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Dejaco, T., Gassner, M., Arthofer, W., Schlick-Steiner, B. C. & Steiner, F. M. Taxonomist’s nightmare … evolutionist’s delight: an integrative approach resolves species limits in jumping bristletails despite widespread hybridization and parthenogenesis. Syst. Biol. 65, 947–974 (2016).
PubMed  PubMed Central  Article  Google Scholar 

11.
Sefc, K. M. et al. Shifting barriers and phenotypic diversification by hybridisation. Ecol. Lett. 20, 651–662 (2017).
PubMed  PubMed Central  Article  Google Scholar 

12.
Suchan, T., Malicki, M. & Ronikier, M. Relict populations and Central European glacial refugia: the case of Rhododendron ferrugineum (Ericaceae). J. Biogeogr. 46, 392–404 (2019).
Article  Google Scholar 

13.
Schneeweiss, G. M. & Schönswetter, P. A re-appraisal of nunatak survival in arctic-alpine phylogeography. Mol. Ecol. 20, 190–192 (2011).
PubMed  Article  PubMed Central  Google Scholar 

14.
Lemmon, A. R. & Lemmon, E. M. High-throughput identification of informative nuclear loci for shallow-scale phylogenetics and phylogeography. Syst. Biol. 61, 745–761 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, 1–7 (2008).
Article  CAS  Google Scholar 

16.
Andrews, K. R., Good, J. M., Miller, M. R., Luikart, G. & Hohenlohe, P. A. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 17, 81–92 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Jeffries, D. L. et al. Comparing RADseq and microsatellites to infer complex phylogeographic patterns, an empirical perspective in the Crucian carp, Carassius carassius L.. Mol. Ecol. 25, 2997–3018 (2016).
PubMed  Article  PubMed Central  Google Scholar 

18.
Bohling, J., Small, M., Von Bargen, J., Louden, A. & DeHaan, P. Comparing inferences derived from microsatellite and RADseq datasets: a case study involving threatened bull trout. Conserv. Genet. 20, 329–342 (2019).
CAS  Article  Google Scholar 

19.
Lemopoulos, A. et al. Comparing RADseq and microsatellites for estimating genetic diversity and relatedness—implications for brown trout conservation. Ecol. Evol. 9, 2106–2120 (2019).
PubMed  PubMed Central  Article  Google Scholar 

20.
Mesak, F., Tatarenkov, A., Earley, R. L. & Avise, J. C. Hundreds of SNPs vs. dozens of SSRs: which dataset better characterizes natural clonal lineages in a self-fertilizing fish?. Front. Ecol. Evol. 2, 74 (2014).
Article  Google Scholar 

21.
Fay, M. F., Cowan, R. S. & Leitch, I. J. The effects of nuclear DNA content (C-value) on the quality and utility of AFLP fingerprints. Ann. Bot. 95, 237–246 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Karam, M.-J., Lefèvre, F., Dagher-Kharrat, M. B., Pinosio, S. & Vendramin, G. G. Genomic exploration and molecular marker development in a large and complex conifer genome using RADseq and mRNAseq. Mol. Ecol. Resour. 15, 601–612 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Etter, P. D., Bassham, S., Hohenlohe, P. A., Johnson, E. A. & Cresko, W. A. SNP Discovery and Genotyping for Evolutionary Genetics Using RAD Sequencing. Methods in Molecular Biology (Clifton, N.J.) Vol. 772, 157–178 (Springer, Berlin, 2011).
Google Scholar 

24.
Davey, J. L. & Blaxter, M. W. RADseq: next-generation population genetics. Brief. Funct. Genomics 9, 416–423 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Głowacka, K. et al. Genetic variation in Miscanthus × giganteus and the importance of estimating genetic distance thresholds for differentiating clones. GCB Bioenergy 7, 386–404 (2015).
Article  CAS  Google Scholar 

26.
Leaché, A. D., Banbury, B. L., Felsenstein, J., De Oca, A. N. M. & Stamatakis, A. Short tree, long tree, right tree, wrong tree: new acquisition bias corrections for inferring SNP phylogenies. Syst. Biol. 64, 1032–1047 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

27.
Wu, C.-H. & Drummond, A. J. Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo. Genetics 188, 151–164 (2011).
PubMed  PubMed Central  Article  Google Scholar 

28.
Emerson, K. J. et al. Resolving postglacial phylogeography using high-throughput sequencing. Proc. Natl. Acad. Sci. 107, 16196–16200 (2010).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

29.
Sboner, A., Mu, X., Greenbaum, D., Auerbach, R. K. & Gerstein, M. B. The real cost of sequencing: higher than you think!. Genome Biol. 12, 125 (2011).
PubMed  PubMed Central  Article  Google Scholar 

30.
Muir, P. et al. The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biol. 17, 53 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

32.
Mittermeier, R. A. & Mittermeier, C. G. Megadiversity: Earth’s Biologically Wealthiest Nations. in 501 (CEMEX, 1997).

33.
Trimble, M. J. & van Aarde, R. J. Geographical and taxonomic biases in research on biodiversity in human-modified landscapes. Ecosphere 3, art119 (2012).
Article  Google Scholar 

34.
Waldron, A. et al. Targeting global conservation funding to limit immediate biodiversity declines. Proc. Natl. Acad. Sci. USA 110, 12144–12148 (2013).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

35.
Adenle, A. et al. Stakeholder visions for biodiversity conservation in developing countries. Sustainability 7, 271–293 (2014).
Article  Google Scholar 

36.
Adenle, A. A., Stevens, C. & Bridgewater, P. Global conservation and management of biodiversity in developing countries: an opportunity for a new approach. Environ. Sci. Policy 45, 104–108 (2015).
Article  Google Scholar 

37.
Barber, P. H. et al. Advancing biodiversity research in developing countries: the need for changing paradigms. Bull. Mar. Sci. 90, 187–210 (2014).
Article  ADS  Google Scholar 

38.
Byrne, M. Phylogeography provides an evolutionary context for the conservation of a diverse and ancient flora. Aust. J. Bot. 55, 316 (2007).
Article  Google Scholar 

39.
Dufresnes, C. et al. Conservation phylogeography: does historical diversity contribute to regional vulnerability in European tree frogs (Hyla arborea)?. Mol. Ecol. 22, 5669–5684 (2013).
PubMed  Article  PubMed Central  Google Scholar 

40.
Coates, D. J., Byrne, M. & Moritz, C. Genetic diversity and conservation units: dealing with the species-population continuum in the age of genomics. Front. Ecol. Evol. 6, 165 (2018).
Article  Google Scholar 

41.
Trimble, M. J. & van Aarde, R. J. Species inequality in scientific study. Conserv. Biol. 24, 886–890 (2010).
PubMed  Article  PubMed Central  Google Scholar 

42.
Kirschner, P. et al. Long-term isolation of European steppe outposts boosts the biome’s conservation value. Nat. Commun. 11, 1–10 (2020).
Article  CAS  Google Scholar 

43.
Záveská, E. et al. Multiple auto- and allopolyploidisations marked the Pleistocene history of the widespread Eurasian steppe plant Astragalus onobrychis (Fabaceae). Mol. Phylogenet. Evol. https://doi.org/10.1016/J.YMPEV.2019.106572 (2019).
Article  PubMed  PubMed Central  Google Scholar 

44.
Luo, M.-C. et al. Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature 551, 498–502 (2017).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

45.
Wang, X. X. et al. The locust genome provides insight into swarm formation and long-distance flight. Nat. Commun. 5, 2957 (2014).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

46.
Hensen, I. et al. Low genetic variability and strong differentiation among isolated populations of the rare steppe grass Stipa capillata L. Central Europe. Plant Biol. 12, 526–536 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Huang, H. & Knowles, L. L. Unforeseen consequences of excluding missing data from next-generation sequences: simulation study of RAD sequences. Syst. Biol 65, 1–9 (2014).
Google Scholar 

48.
Crotti, M., Barratt, C. D., Loader, S. P., Gower, D. J. & Streicher, J. W. Causes and analytical impacts of missing data in RADseq phylogenetics: insights from an African frog (Afrixalus). Zool. Scr. 48, 157–167 (2019).
Article  Google Scholar 

49.
Sinclair, E. A. & Hobbs, R. J. Sample size effects on estimates of population genetic structure: implications for ecological restoration. Restor. Ecol. 17, 837–844 (2009).
Article  Google Scholar 

50.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Althoff, D. M., Gitzendanner, M. A. & Segraves, K. A. The utility of amplified fragment length polymorphisms in phylogenetics: a comparison of homology within and between genomes. Syst. Biol. 56, 477–484 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Felsenstein, J. Inferring Phylogenies (Oxford University Press Inc., Oxford, 2004).
Google Scholar 

54.
Eaton, D. A. R., Spriggs, E. L., Park, B. & Donoghue, M. J. Misconceptions on missing data in RAD-seq phylogenetics with a deep-scale example from flowering plants. Syst. Biol. 66, 399–412 (2016).
Google Scholar 

55.
Hodel, R. G. J. et al. The report of my death was an exaggeration: a review for researchers using microsatellites in the 21st century. Appl. Plant Sci. 4, 1600025 (2016).
Article  Google Scholar 

56.
Puritz, J. B. et al. Demystifying the RAD fad. Mol. Ecol. 23, 5937–5942 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Lowry, D. B. et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17, 142–152 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Wagner, H. C. et al. Light at the end of the tunnel: Integrative taxonomy delimits cryptic species in the Tetramorium caespitum complex (Hymenoptera: Formicidae). Myrmecol. News 25, 95–129 (2017).
Google Scholar 

59.
Wheeler, Q. D. Taxonomic Shock and Awe. In The New Taxonomy (ed. Wheeler, Q. D.) 211–226 (CRC Press, Boca Raton, FL, 2008). https://doi.org/10.1201/9781420008562.ch10.
Google Scholar 

60.
Holderegger, R. et al. Conservation genetics: linking science with practice. Mol. Ecol. 28, 3848–3856 (2019).
PubMed  Article  PubMed Central  Google Scholar 

61.
Tel-Zur, N., Abbo, S., Myslabodski, D. & Mizrahi, Y. Modified CTAB procedure for DNA isolation from epiphytic cacti of the genera Hylocereus and Selenicereus (Cactaceae). Plant Mol. Biol. Rep. 17, 249–254 (1999).
CAS  Article  Google Scholar 

62.
Wachter, G. A. et al. Pleistocene survival on central Alpine nunataks: genetic evidence from the jumping bristletail Machilis pallida. Mol. Ecol. 21, 4983–4995 (2012).
PubMed  Article  PubMed Central  Google Scholar 

63.
Arthofer, W., Schlick-Steiner, B. C. & Steiner, F. M. optiFLP: software for automated optimization of amplified fragment length polymorphism scoring parameters. Mol. Ecol. Resour. 11, 1113–1118 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Arthofer, W. TinyFLP and tinyCAT: software for automatic peak selection and scoring of AFLP data tables. Mol. Ecol. Resour. 10, 385–388 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Oksanen, J., Guillaume Blanchet, F., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E. & Wagner, H. Vegan: Community Ecology Package. R package. (2017).

66.
Doležel, J., Greilhuber, J. & Suda, J. Estimation of nuclear DNA content in plants using flow cytometry. Nat. Protoc. 2, 2233–2244 (2007).
PubMed  Article  CAS  PubMed Central  Google Scholar 

67.
Davey, F. & RADseq counter. (2012). https://www.wiki.ed.ac.uk/display/RADSequencing/Home. (Accessed: 15th June 2014)

68.
Paun, O. et al. Processes driving the adaptive radiation of a tropical tree (Diospyros, Ebenaceae) in New Caledonia, a biodiversity hotspot. Syst. Biol. 65, 212–227 (2016).
PubMed  Article  PubMed Central  Google Scholar 

69.
Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).
PubMed  PubMed Central  Article  Google Scholar 

70.
Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-4.0. http://www.repeatmasker.org. (Accessed: 1st September 2016)

71.
Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Felsenstein, J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17, 368–376 (1981).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

73.
Jakobsson, M. & Rosenberg, N. A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Rosenberg, N. A. DISTRUCT: a program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138 (2004).
Article  Google Scholar 

75.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

76.
Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Kosman, E. & Leonard, K. J. Similarity coefficients for molecular markers in studies of genetic relationships between individuals for haploid, diploid, and polyploid species. Mol. Ecol. 14, 415–424 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Miclaus, K., Wolfinger, R. & Czika, W. SNP selection and multidimensional scaling to quantify population structure. Genet. Epidemiol. 33, 488–496 (2009).
PubMed  Article  PubMed Central  Google Scholar 

79.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
Article  Google Scholar 

80.
Wickham, H. ggplot2 (Springer, Berlin, 2009). https://doi.org/10.1007/978-0-387-98141-3.
Google Scholar  More

1.
Mousseau, T. A. & Fox, C. W. The adaptive significance of maternal effects. Trends Ecol. Evol. 13, 403–407 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Franklin, T. B., Linder, N., Russig, H., Thöny, B. & Mansuy, I. M. Influence of early stress on social abilities and serotonergic functions across generations in mice. PLoS ONE 6, e21842. https://doi.org/10.1371/journal.pone.0021842 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

3.
Gapp, K. et al. Implication of sperm RNAs in transgenerational inheritance of the effects of early trauma in mice. Nat. Neurosci. 17, 667–669 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
McCarthy, D. M. et al. Nicotine exposure of male mice produces behavioral impairment in multiple generations of descendants. PLoS Biol. 16, e2006497. https://doi.org/10.1371/journal.pbio.2006497 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Alfonso, S. et al. Examining multi- and transgenerational behavioral and molecular alterations resulting from parental exposure to an environmental PCB and PBDE mixture. Aquat. Toxicol. 208, 29–38 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Anway, M. D., Memon, M. A., Uzumcu, M. & Skinner, M. K. Transgenerational effect of the endocrine disruptor vinclozolin on male spermatogenesis. J. Androl. 27, 868–879 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Crews, D. et al. Transgenerational epigenetic imprints on mate preference. PNAS 104, 5942–5946 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Crews, D. et al. Epigenetic transgenerational inheritance of altered stress responses. PNAS 109, 9143–9148 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Gillette, R. et al. Sexually dimorphic effects of ancestral exposure to vinclozolin on stress reactivity in rats. Endocrinology 155, 3853–3866 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

10.
Gillette, R., Son, M. J., Ton, L., Gore, A. C. & Crews, D. Passing experiences on to future generations: endocrine disruptors and transgenerational inheritance of epimutations in brain and sperm. Epigenetics 13, 1106–1126 (2018).
PubMed  PubMed Central  Article  Google Scholar 

11.
Bhandari, R., Saal, F. & vom Tillitt, D. Transgenerational effects from early developmental exposures to bisphenol A or 17α-ethinylestradiol in medaka Oryzias latipes. Sci. Rep. 5, 9303. https://doi.org/10.1038/srep09303 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
Kidd, K. A. et al. Collapse of a fish population after exposure to a synthetic estrogen. PNAS 104, 8897–8901 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Skinner, M. K. et al. Gene bionetworks involved in the epigenetic transgenerational inheritance of altered mate preference: environmental epigenetics and evolutionary biology. BMC Genom. 15, 377. https://doi.org/10.1186/1471-2164-15-377 (2014).
Article  Google Scholar 

14.
Pembrey, M. E. et al. Sex-specific, male-line transgenerational responses in humans. Eur. J. Hum. Genet. 14, 159–166 (2006).
PubMed  Article  PubMed Central  Google Scholar 

15.
Moisiadis, V. G. & Matthews, S. G. Glucocorticoids and fetal programming part 1: outcomes. Nature 10, 391–402 (2014).
CAS  Google Scholar 

16.
Crean, A. J. & Bondurianksy, R. What is a paternal effect?. Trends Ecol. Evol. 29, 554–559 (2014).
PubMed  Article  PubMed Central  Google Scholar 

17.
Champagne, F. A. Interplay between paternal germline and maternal effects in shaping development: the overlooked importance of behavioural ecology. Funct. Ecol. 34, 401–413 (2019).
Article  Google Scholar 

18.
Sheldon, B. C. Differential allocation: tests, mechanisms and implications. Trends Ecol. Evol. 15, 397–402 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Reznik, S. Y., Vaghina, N. P. & Voinovich, N. D. Multigenerational maternal effect on diapause induction in Trichogramma species (Hymenoptera: Trichogrammatidae). Biocontrol Sci. Technol. 22, 429–445 (2012).
Article  Google Scholar 

20.
Rechavi, O. et al. Starvation-induced transgenerational inheritance of small RNAs in C. elegans. Cell 158, 277–287 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Shama, L. N. S. et al. Transgenerational effects persist down the maternal line in marine sticklebacks: gene expression matches physiology in a warming ocean. Evol. Appl. 9, 1096–1111 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Dunn, G. A. & Bale, T. L. Maternal high-fat diet effects on third-generation female body size via the paternal lineage. Endocrinology 152, 2228–2236 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Skinner, M. K. et al. Ancestral dichlorodiphenyltrichloroethane (DDT) exposure promotes epigenetic transgenerational inheritance of obesity. BMC Med. 11, 228. https://doi.org/10.1186/1741-7015-11-228 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

24.
Zhu, J., Lee, K. P., Spencer, T. J., Biederman, J. & Bhide, P. G. Transgenerational transmission of hyperactivity in a mouse model of ADHD. J. Neurosci. 34, 2768–2773 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Leroux, S. et al. Embryonic environment and transgenerational effects in quail. Genet. Sel. Evol. 49, 14. https://doi.org/10.1186/s12711-017-0292-7 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Vera-Chang, M. N. et al. Transgenerational hypocortisolism and behavioral disruption are induced by the antidepressant fluoxetine in male zebrafish Danio rerio. PNAS 115, E12435–E12442 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Sheriff, M. J., McMahon, E. K., Krebs, C. J. & Boonstra, R. Risk severity predicts generational impact. J. Zool. 296, 305–310 (2015).
Article  Google Scholar 

28.
Dias, B. G. & Ressler, K. J. Parental olfactory experience influences behavior and neural structure in subsequent generations. Nat. Neurosci. 17, 89–96 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
He, N. et al. Parental life events cause behavioral difference among offspring: adult pre-gestational restraint stress reduces anxiety across generations. Sci. Rep. 6, 39497. https://doi.org/10.1038/srep39497 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

30.
Pentinat, T., Ramon-Krauel, M., Cebria, J., Diaz, R. & Jimenez-Chillaron, J. C. Transgenerational inheritance of glucose intolerance in a mouse model of neonatal overnutrition. Endocrinology 151, 5617–5623 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Wei, Y. et al. Paternally induced transgenerational inheritance of susceptibility to diabetes in mammals. PNAS 111, 1873–1878 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Cropley, J. E. et al. Male-lineage transmission of an acquired metabolic phenotype induced by grand-paternal obesity. Mol. Metab. 5, 699–708 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Dunn, G. A., Morgan, C. P. & Bale, T. L. Sex-specificity in transgenerational epigenetic programming. Horm. Behav. 59, 290–295 (2011).
PubMed  Article  PubMed Central  Google Scholar 

34.
Glover, V. & Hill, J. Sex differences in the programming effects of prenatal stress on psychopathology and stress responses: an evolutionary perspective. Physiol. Behav. 106, 736–740 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Saavedra-Rodríguez, L. & Feig, L. A. Chronic social instability induces anxiety and defective social interactions across generations. Biol. Psychiatry 73, 44–53 (2013).
PubMed  Article  PubMed Central  Google Scholar 

36.
Moisiadis, V. G., Constantinof, A., Kostaki, A., Szyf, M. & Matthews, S. G. Prenatal glucocorticoid exposure modifies endocrine function and behaviour for 3 generations following maternal and paternal transmission. Sci. Rep. 7, 11814. https://doi.org/10.1038/s41598-017-11635-w (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Hellmann, J. K., Carlson, E. R. & Bell, A. M. Sex-specific plasticity across generations II: grandpaternal effects are lineage specific and sex specific. J. Anim. Ecol. 89, 2800–2812 (2020).
Article  Google Scholar 

38.
gene duplications and functional diversification in Craniates. Le Crom, S., Kapsimali, M., Barome, P-O. & Vernier, P. Dopamine receptors for every species. J. Struct. Funct. Genomics 3, 161–176 (2003).
Article  Google Scholar 

39.
Melis, M. R. & Argiolas, A. Dopamine and sexual behavior. Neurosci. Biobehav. R. 19, 19–38 (1995).
CAS  Article  Google Scholar 

40.
Pfaus, J. G., Ismail, N. & Coria-Avila, G. A. Sexual motivation. In Encyclopedia of Behavioral Neuroscience (eds. Koob, G. F., Le Moal, M. & Thompson, R. F.) 201–-209 (Oxford, Oxford Academic Press, 2010).

41.
Bardo, M. T., Donohew, R. L. & Harrington, N. G. Psychobiology of novelty seeking and drug seeking behavior. Behav. Brain Res. 77, 23–43 (1996).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Mällo, T. et al. Rats with persistently low or high exploratory activity: behaviour in tests of anxiety and depression and extracellular levels of dopamine. Behav. Brain Res. 177, 269–281 (2006).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

43.
Smith, B. R. & Blumstein, D. T. Fitness consequences of personality: a meta-analysis. Behav. Ecol. 19, 448–455 (2007).
Article  Google Scholar 

44.
Csoka, A. B. & Szyf, M. Epigenetic side-effects of common pharmaceuticals: a potential new field in medicine and pharmacology. Med. Hypotheses 73, 770–780 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Kuczenski, R. & Segal, D. S. Effects of methylphenidate on extracellular dopamine serotonin, and norepinephrine: comparison with amphetamine. J. Neurochem. 68, 2032–2037 (1997).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Gamo, N. J., Wang, M. & Arnsten, A. F. T. Methylphenidate and atomoxetine enhance prefrontal function through α2-adrenergic and dopamine D1 receptors. J. Am. Acad. Child Adolesc. Psychiatry 49, 1011–1023 (2010).
PubMed  PubMed Central  Article  Google Scholar 

47.
Greenhill, L. L. et al. Guidelines and algorithms for the use of methylphenidate in children with attention-deficit/hyperactivity disorder. J. Atten. Disord. 6, S89–S100 (2002).
PubMed  Article  PubMed Central  Google Scholar 

48.
Kessler, R. C. et al. The prevalence and correlates of adult ADHD in the United States: results from the national comorbidity survey replication. Am. J. Psychiatry 163, 716–723 (2006).
PubMed  PubMed Central  Article  Google Scholar 

49.
Visser, S. N. et al. Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003–2011. J. Am. Acad. Child. Psychiatry 53, 34–46 (2014).
Article  Google Scholar 

50.
Karlstad, Ø. et al. Use of drugs for ADHD among adults—a multinational study among 15.8 million adults in the Nordic countries. Eur. J. Clin. Pharmacol. 72, 1507–1514 (2016).
PubMed  PubMed Central  Article  Google Scholar 

51.
Biederman, J. Attention-deficit/hyperactivity disorder: a selective overview. Biol. Psychiatry 57, 1215–1220 (2005).
PubMed  Article  PubMed Central  Google Scholar 

52.
McFadyen-Leussis, M. P., Lewis, S. P., Bond, T. L. Y., Carrey, N. & Brown, R. E. Prenatal exposure to methylphenidate hydrochloride decreases anxiety and increases exploration in mice. Pharmacol. Biochem. Behav. 77, 491–500 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Levin, E. D. et al. 2011. Persistent behavioral impairment caused by embryonic methylphenidate exposure in zebrafish. Neurotoxicol. Teratol. 33, 668–673 (2011).

54.
Lloyd, S. A. et al. Prenatal exposure to psychostimulants increases impulsivity, compulsivity, and motivation for rewards in adult mice. Physiol. Behav. 119, 43–51 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Lepelletier, F. X. et al. Prenatal exposure to methylphenidate affects the dopamine system and the reactivity to natural reward in adulthood in rats. Int. J. Neuropsychoph. https://doi.org/10.1093/ijnp/pyu044 (2015).
Article  Google Scholar 

56.
Montagnini, B. G. et al. Effects of repeated administration of methylphenidate on reproductive parameters in male rats. Physiol. Behav. 133, 122–129 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
He, F., Lidow, I. A. & Lidow, M. S. Consequences of paternal cocaine exposure in mice. Neurotoxicol. Teratol. 28, 198–209 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Killinger, C. E., Robinson, S. & Stanwood, G. D. Subtle biobehavioral effects produced by paternal cocaine exposure. Synapse 66, 902–908 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Vassoler, F. M., White, S. L., Schmidt, H. D., Sadri-Vakili, G. & Pierce, R. C. Epigenetic inheritance of a cocaine-resistance phenotype. Nat. Neurosci. 16, 42–67 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Fischer, D. K., Rice, R. C., Rivera, A. M., Donohoe, M. & Rajadhyaksha, A. M. Altered reward sensitivity in female offspring of cocaine-exposed fathers. Behav. Brain Res. 332, 23–31 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Wimmer, M. E. et al. Paternal cocaine taking elicits epigenetic remodeling and memory deficits in male progeny. Mol. Psychiatry 22, 1641–1650 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Yano, M. & Steiner, H. Methylphenidate and cocaine: the same effects on gene regulation?. Trends Pharmacol. Sci. 28, 588–596 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Hall, Z. J., De Serrano, A. R., Rodd, F. H. & Tropepe, V. Casting a wider fish net on animal models in neuropsychiatric research. Prog. Neuropsychopharmacol. Biol. Psychiatry 55, 7–15 (2014).
PubMed  Article  PubMed Central  Google Scholar 

64.
Fontana, B. D., Mezzomo, N. J., Kalueff, A. V. & Rosemberg, D. B. The developing utility of zebrafish models of neurological and neuropsychiatric disorders: a critical review. Exp. Neurol. 299, 157–171 (2018).
PubMed  Article  PubMed Central  Google Scholar 

65.
Reznick, D. N. The impact of predation on life history evolution in Trinidadian guppies: genetic basis of observed life history patterns. Evolution 36, 1236–1250 (1982).
PubMed  Article  PubMed Central  Google Scholar 

66.
DeMarais, A. & Oldis, D. Matrotrophic transfer of fluorescent microspheres in Poeciliid fishes. Copeia 3, 632–636 (2005).
Article  Google Scholar 

67.
Hughes, K. A., Du, L., Rodd, F. H. & Reznick, D. N. Familiarity leads to female mate preference for novel males in the guppy Poecilia reticulata. Anim. Behav. 58(907), 916 (1999).
Google Scholar 

68.
Rodd, F. H., Hughes, K. A., Grether, G. F. & Baril, C. T. A possible non-sexual origin of mate preference: are male guppies mimicking fruit?. Proc. R. Soc. B Biol. Sci. 269, 475–481 (2002).
Article  Google Scholar 

69.
Valvo, J., Rodd, F. H. & Hughes, K. A. Consistent female preference for rare and unfamiliar male color patterns in wild guppy populations. Behav. Ecol. 30, 1672–1681 (2019).
Article  Google Scholar 

70.
Daniel, M. J., Koffinas, L. & Hughes, K. A. Mating preference for novel phenotypes can be explained by general neophilia in female guppies. Am. Nat. 196, 414–428 (2020).
PubMed  Article  PubMed Central  Google Scholar 

71.
Deacon, A. E., Ramnarine, I. W. & Magurran, A. E. How reproductive ecology contributes to the spread of a globally invasive fish. PLoS ONE 6, e24416. https://doi.org/10.1371/journal.pone.0024416 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

72.
Hughes, K. A., Houde, A. E., Price, A. C. & Rodd, F. H. Mating advantage for rare males in wild guppy populations. Nature 503, 108–110 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
De Serrano, A. R., Fong, C. & Rodd, F. H. Effects of methylphenidate on responses to novelty in a teleost fish (Poecilia reticulata). Behav. Brain Res. 302, 53–59 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

74.
Schmitz, F. et al. Methylphenidate causes behavioral impairments and neuron and astrocyte loss in the hippocampus of juvenile rats. Mol. Neurobiol. 54, 4201–4216 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

75.
Bolaños, C. A., Barrot, M., Berton, O., Wallace-Black, D. & Nestler, E. J. Methylphenidate treatment during pre- and periadolescence alters behavioral responses to emotional stimuli at adulthood. Biol. Psychiatry 54, 1317–1329 (2003).
PubMed  Article  CAS  PubMed Central  Google Scholar 

76.
Bell, A. M. & Hellman, J. K. An integrative framework for understanding the mechanisms and multigenerational consequences of transgenerational plasticity. Annu. Rev. Ecol. Evol. S. 50, 97–118 (2019).
Article  Google Scholar 

77.
Walsh, R. N. & Cummins, R. A. Open-field test—critical review. Psychol. Bull. 83, 482–504 (1976).
CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Hill, M. O. Correspondence analysis: a neglected multivariate method. J. R. Stat. Soc. C Appl. 23, 340–354 (1974).
MathSciNet  Google Scholar 

79.
Godin, J. G. J. Evading predators. In Behavioural Ecology of Teleost Fishes (ed. Godin, J. G. J.) 191–236 (Oxford, Oxford University Press, 1997).

80.
Sih, A. Foraging strategies and the avoidance of predation by an aquatic insect Notonecta Hoffmanni. Ecology 63(786), 796 (1982).
Google Scholar 

81.
McPeek, M. A., Grace, M. & Richardson, J. M. L. Physiological and behavioral responses to predators shape the growth/predation risk trade-off in damselflies. Ecology 82, 1535–1545 (2001).
Article  Google Scholar 

82.
Burns, J. G. The validity of three tests of temperament in guppies (Poecilia reticulata). J. Comp. Psychol. 122, 344–356 (2008).
PubMed  Article  PubMed Central  Google Scholar 

83.
Morris, S. M. et al. The genetic toxicity of methylphenidate: a review of the current literature. J. Appl. Toxicol. 32, 756–764 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
SAS Institute. SAS/STAT 9.4 User’s Guide (SAS Institute, Cary, 2013).

85.
Seghers, B. H. Feeding behavior and terrestrial locomotion in the cyprinodontid fish, Rivulus harti (Boulenger). Verh. Internat. Verein. Limnol. 20, 2055–2059 (1978).
Google Scholar 

86.
Mattingly, H. T. & Butler, M. J. Laboratory predation on the Trinidadian guppy: implications for the size-selective predation hypothesis and guppy life history evolution. OIKOS 69, 54–64 (1994).
Article  Google Scholar 

87.
Reznick, D. N., Butler, M. J., Rodd, F. H. & Ross, P. N. Life history evolution in guppies (Poecilia reticulata): 6—differential mortality as a mechanism for natural selection. Evolution 50, 1651–1660 (1996).
PubMed  PubMed Central  Google Scholar 

88.
Bijlsma, L., Emke, E., Hernandez, F. & de Voogt, P. Investigation of drugs of abuse and relevant metabolites in Dutch sewage water by liquid chromatography coupled to high resolution mass spectrometry. Chemosphere 89, 1399–1406 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

89.
Racamonde, I., Rodil, R., Quintana, J. B., Villaverde-de-Saa, E. & Cela, R. Determination of benzodiazepines, related pharmaceuticals and metabolites in water by solid-phase extraction and liquid-chromatography-tandem mass spectrometry. J. Chromatogr. A 1352, 69–79 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

90.
Laland, K. et al. Does evolutionary theory need a rethink?. Nature 514, 161–164 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

91.
Horsthemke, B. A critical view on transgenerational epigenetic inheritance in humans. Nat. Commun. 9, 2973. https://doi.org/10.1038/s41467-018-05445-5 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

92.
Soubry, A., Hoyo, C., Jirtle, R. L. & Murphy, S. K. A paternal environmental legacy: evidence for epigenetic inheritance through the male germ line. BioEssays 36, 359–371 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

93.
Hughes, L. C. et al. Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data. PNAS 115, 6249–6254 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

94.
Wang, X. & Bhandari, R. K. DNA methylation dynamics during epigenetic reprogramming of medaka embryo. Epigenetics 14, 611–622 (2019).
PubMed  PubMed Central  Article  Google Scholar 

95.
Wang, X. & Bhandari, R. K. The dynamics of DNA methylation during epigenetic reprogramming of primordial germ cells in medaka (Oryzias latipes). Epigenetics 15, 483–498 (2020).
PubMed  Article  PubMed Central  Google Scholar 

96.
Furchtgott, E., Dees, J. W. & Wechkin, S. Open-field exploration as a function of age. J. Comp. Physiol. Psychol. 54, 386–388 (1961).
CAS  PubMed  Article  PubMed Central  Google Scholar 

97.
Werboff, J. & Havlena, J. The effects of aging on open-field behavior. Psychol. Rep. 10, 395–398 (1962).
Article  Google Scholar 

98.
Valle, F. P. Rats performance on repeated tests in open field as a function of age. Psychon. Sci. 23, 333–335 (1971).
Article  Google Scholar 

99.
Franklin, T. B. et al. Epigenetic transmission of the impact of early stress across generations. Biol. Psychiatry 68, 408–415 (2010).
PubMed  Article  PubMed Central  Google Scholar 

100.
McBirney, M. et al. Atrazine induced epigenetic transgenerational inheritance of disease, lean phenotype and sperm epimutation pathology biomarkers. PLoS One 12, e0184306. https://doi.org/10.1371/journal.pone.0184306 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

101.
Becker, J. B. & Chartoff, E. Sex differences in neural mechanisms mediating reward and addiction. Neuropsychopharmacology 44, 166–183 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

102.
Rubinow, D. R. & Schmidt, P. J. Sex differences and the neurobiology of affective disorders. Neuropsychopharmacology 44, 111–128 (2019).
PubMed  Article  PubMed Central  Google Scholar 

103.
Eriksson, K., Halkka, O., Lokki, J. & Saura, A. Enzyme polymorphism in feral, outbred and inbred rats (Rattus norvegicus). Heredity 37, 341–349 (1976).
CAS  PubMed  Article  PubMed Central  Google Scholar 

104.
Connor, J. L. & Belucci, M. J. Natural selection resisting inbreeding depression in captive wild housemice (Mus musculus). Evolution 33, 929–940 (1979).
PubMed  Article  PubMed Central  Google Scholar 

105.
Mina, N. S., Sheldon, B. L., Yoo, B. H. & Frankham, R. Heterozygosity at protein loci in inbred and outbred lines of chickens. Poult. Sci. 70, 1864–1872 (1991).
CAS  PubMed  Article  PubMed Central  Google Scholar 

106.
Turissini, D. A., Gamez, S. & White, B. J. Genome-wide patterns of polymorphism in an inbred line of the African malaria mosquito Anopheles gambiae. Genome Biol. Evol. 6, 3094–3104 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

107.
Gray, J. D. et al. Methylphenidate administration to juvenile rats alters brain areas involved in cognition, motivated behaviors, appetite, and stress. J. Neurosci. 27, 7196–7207 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

108.
Marco, E. M. et al. Neurobehavioral adaptations to methylphenidate: the issue of early adolescent exposure. Neurosci. Biobehav. Rev. 35, 1722–1739 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

109.
American Psychiatric Association. Attention-deficit/hyperactivity disorder. In Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (American Psychiatric Association, Philadelphia, 2014).

110.
Novartis Pharmaceuticals Canada Inc. Product monograph for Ritalin and Ritalin SR (2017).

111.
Brenhouse, H. C. & Andersen, S. L. Developmental trajectories during adolescence in males and females: a cross-species understanding of underlying brain changes. Neurosci. Biobehav. Rev. 35, 1687–1703 (2011).
PubMed  PubMed Central  Article  Google Scholar 

112.
Houde, A. E. Sex, Color, and Mate Choice in Guppies (Princeton, Princeton University Press, 1997).
Google Scholar 

113.
Yoshida, M., Nagamine, M. & Uematsu, K. Comparison of behavioral responses to a novel environment between three teleosts, bluegill Lepomis macrochirus, crucian carp Carassius langsdorfii, and goldfish Carassius auratus. Fisheries Sci. 71, 314–319 (2005).
CAS  Article  Google Scholar 

114.
Blumstein, D. T., Evans, C. S. & Daniels, J. C. JWatcher (v. 1.0, 2006).

115.
Ahmad, F. & Richardson, M. K. Exploratory behaviour in the open field test adapted for larval zebrafish: impact of environmental complexity. Behav. Process. 92, 88–98 (2013).
Article  Google Scholar 

116.
Burns, J. G., Price, A. C., Thomson, J. D., Hughes, K. A. & Rodd, F. H. Environmental and genetic effects on exploratory behavior of high- and low-predation guppies (Poecilia reticulata). Behav. Ecol. Sociobiol. 70, 1187–1196 (2016).
Article  Google Scholar 

117.
Marriott, A. S. The effects of amphetamine, caffeine and methylphenidate on the locomotor activity of rats in an unfamiliar environment. Int. J. Neuropharmacol. 7, 487–491 (1968).
CAS  PubMed  Article  PubMed Central  Google Scholar 

118.
Dyne, L. J. & Hughes, R. N. Effects of methylphenidate on activity and reactions to novelty in rats. Psychon. Sci. 19, 267–268 (1970).
Article  Google Scholar 

119.
R Core Team. R: A Language and Environment for Statistical Computing (Vienna, R Foundation for Statistical Computing, 2018).

120.
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, Berlin, 2002).
Google Scholar 

121.
Volkow, N. D. et al. Dopamine transporters decrease with age. J. Nucl. Med. 37, 554–559 (1996).
CAS  PubMed  PubMed Central  Google Scholar 

122.
Andersen, S. L. & Teicher, M. H. Sex differences in dopamine receptors and their relevance to ADHD. Neurosci. Biobehav. Rev. 24, 137–141 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

123.
Arvidsson, E., Viereckel, T., Mikulovic, S. & Wallén-Mackenzie, Å. Age- and sex-dependence of dopamine release and capacity for recovery identified in the dorsal striatum of C57/Bl6J mice. PLoS One 9, e99592. https://doi.org/10.1371/journal.pone.0099592 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

124.
Faraone, S. V., Biederman, J., Morley, C. P. & Spencer, T. J. Effect of stimulants on height and weight: a review of the literature. J. Am. Acad. Child Adolesc. Psychiatry 47, 994–1009 (2008).
PubMed  PubMed Central  Google Scholar 

125.
Tempelman, R. J. & Rosa, G. J. M. Empirical Bayes approaches to mixed model inference in quantitative genetics. In Genetic Analysis of Complex Traits Using SAS (ed. Saxton, A.) (SAS Institute, Cary, 2004).

126.
Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).
Article  Google Scholar 

127.
Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D. & Schabenberger, O. SAS for Mixed Models (SAS Institute, Cary, 2006).
Google Scholar  More

1.
Hrdy, S. B. Infanticide among animals: A review, classification, and examination of the implications for the reproductive strategies of females. Ethol. Sociobiol. 1, 13–40 (1979).
Article  Google Scholar 
2.
Lukas, D. & Huchard, E. The evolution of infanticide by males in mammalian societies. Science 346, 841–844 (2014).
ADS  CAS  Article  Google Scholar 

3.
Berger, J. Induced abortion and social factors in wild horses. Nature 303, 59–61 (1983).
ADS  CAS  Article  Google Scholar 

4.
Packer, C. & Pusey, A. E. Infanticide in carnivores. In Infanticide: Comparative and Evolutionary Perspectives (eds Hausfater, G. & Hrdy, S. B.) 31–42 (Aldine, New York, 1984).
Google Scholar 

5.
Zipple, M. N. et al. Conditional fetal and infant killing by male baboons. Proc. R. Soc. B 284(1847), 20162561 (2017).
Article  Google Scholar 

6.
Zipple, M. N., Roberts, E. K., Alberts, S. C. & Beehner, J. C. Male-mediated prenatal loss: Functions and mechanisms. Evol. Anthropol. Issues News Rev. 28(3), 114–125 (2019).
Article  Google Scholar 

7.
Bruce, H. M. An exteroceptive block to pregnancy in the mouse. Nature 184, 105 (1959).
ADS  CAS  Article  Google Scholar 

8.
Schwagmeyer, P. L. The Bruce effect: An evaluation of male/female advantages. Am. Nat. 114(6), 932–938 (1979).
Article  Google Scholar 

9.
Labov, J. B. Pregnancy blocking in rodents: Adaptive advantages for females. Am. Nat. 118, 361–371 (1981).
Article  Google Scholar 

10.
Roberts, E. K., Lu, A., Bergman, T. J. & Beehner, J. C. A Bruce effect in wild geladas. Science 335, 1222–1225 (2012).
ADS  CAS  Article  Google Scholar 

11.
Busse, C. & Hamilton, W. J. Infant carrying by male chacma baboons. Science 212(4500), 1281–1283 (1981).
ADS  CAS  Article  Google Scholar 

12.
Palombit, R. A., Seyfarth, R. M. & Cheney, D. L. The adaptive value of “friendships” to female baboons: Experimental and observational evidence. Anim. Behav. 54, 599–614 (1997).
CAS  Article  Google Scholar 

13.
Palombit, R. A. Male infanticide in wild savanna baboons: Adaptive significance and intraspecific variation. In Sexual Selection and Reproductive Competition in Primates: New Perspectives and Directions (ed. Jones, C. B.) 367–412 (The American Society of Primatologists, Norman, 2003).
Google Scholar 

14.
Weingrill, T. Infanticide and the value of male-female relationships in mountain chacma baboons. Behaviour 137, 337–359 (2000).
Article  Google Scholar 

15.
Packer, C. Male dominance and reproductive activity in Papio anubis. Anim. Behav. 27, 37–45 (1979).
Article  Google Scholar 

16.
Smuts, B. B. Sex and Friendship in Baboons (Aldine, New York, 1985).
Google Scholar 

17.
Bercovitch, F. B. Coalitions, cooperation and reproductive tactics among adult male baboons. Anim. Behav. 36, 1198–1209 (1988).
Article  Google Scholar 

18.
Packer, C. Male care and exploitation of infants in Papio anubis. Anim. Behav. 28, 512–520 (1980).
Article  Google Scholar 

19.
Alberts, S. C., Sapolsky, R. M. & Altmann, J. Behavioral, endocrine, and immunological correlates of immigration by an aggressive male into a natural primate group. Horm. Behav. 26, 167–178 (1992).
CAS  Article  Google Scholar 

20.
Packer, C., Collins, D., Sindimwo, A. & Goodall, J. Reproductive constraints on aggressive competition in female baboons. Nature 373, 60–63 (1995).
ADS  CAS  Article  Google Scholar 

21.
Pusey, A., Williams, J. & Goodall, J. The influence of dominance rank on the reproductive success of female chimpanzees. Science 277, 828–831 (1997).
CAS  Article  Google Scholar 

22.
Storey, A. E. & Snow, D. T. Postimplantation pregnancy disruptions in meadow voles: Relationship to variation in male sexual and aggressive behavior. Physiol. Behav. 47(1), 19–25 (1990).
CAS  Article  Google Scholar 

23.
Beehner, J. C., Nguyen, N., Wango, E. O., Alberts, S. C. & Altmann, J. The endocrinology of pregnancy and fetal loss in wild baboons. Horm. Behav. 49, 688 (2006).
CAS  Article  Google Scholar 

24.
Ransom, T. Beach Troop of the Gombe (Bucknell Press, Lewisburg, 1981).
Google Scholar 

25.
Bailey, A., Eberly, L. E. & Packer, C. Does pregnancy coloration reduce female conspecific aggression in the presence of maternal kin?. Anim. Behav. 108, 199–206 (2015).
Article  Google Scholar 

26.
Pratt, N. C. & Lisk, R. D. Effects of social stress during early pregnancy on litter size and sex ratio in the golden hamster (Mesocricetus auratus). J. Reprod. Fertil. 87, 763–769 (1989).
CAS  Article  Google Scholar 

27.
Young, A. J. et al. Stress and the suppression of subordinate reproduction in cooperatively breeding meerkats. Proc. Natl. Acad. Sci. U.S.A. 103, 12005–12010 (2006).
ADS  CAS  Article  Google Scholar 

28.
Arck, P., Hansen, P. J., Mulac Jericevic, B., Piccinni, M. & Szekeres-Bartho, J. Progesterone during pregnancy: endocrine–immune cross talk in mammalian species and the role of stress. Am. J. Reprod. Immunol. 58, 268–279 (2007).
CAS  Article  Google Scholar 

29.
Beehner, J. C. & Lu, A. Reproductive suppression in female primates: A review. Evol. Anthropol. Issues News Rev. 22, 226–238 (2013).
Article  Google Scholar 

30.
Sapolsky, R. M. Endocrine aspects of social instability in the olive baboon (Papio anubis). Am. J. Primatol. 5, 365–379 (1983).
CAS  Article  Google Scholar 

31.
van Lawick-Goodall, J. The behavior of free-living chimpanzees in the Gombe stream reserve. Anim. Behav. Monogr. 1, 161–311 (1968).
Article  Google Scholar 

32.
Altmann, S. A. The pregnancy sign in savannah baboons. J. Zoo Anim. Med. 4, 8–12 (1973).
Article  Google Scholar 

33.
Beehner, J. C., Onderdonk, D. A., Alberts, S. C. & Altmann, J. The ecology of conception and pregnancy failure in wild baboons. Behav. Ecol. 17(5), 741–750 (2006).
Article  Google Scholar 

34.
Higham, J. The reproductive ecology of female olive baboons (Papio hamadryas anubis) at Gashaka-Gumti National Park, Nigeria. PhD Thesis. Roehampton University: London (2006).

35.
Tinsley Johnson, E., Snyder-Mackler, N., Lu, A., Bergman, T. J. & Beehner, J. C. Social and ecological drivers of reproductive seasonality in geladas. Behav. Ecol. 29(3), 574–588 (2018).
Article  Google Scholar  More