Ecology

Description of study areasIGR plant (33° 42/N, 46° 13/E) is located along the edge of the mountains of Zagros forests and 25 km from Ilam city. Its main activity, to supply gas to the western provinces of Iran, started in 2007. It converts sour gas to sweet gas and also produces various products such as pastil sulfur, ethane, and liquefied gas. The refinery has two chimneys, which release waste gases into the atmosphere. Oak trees are the main tree species of the Zagros forests around the refinery; these are exposed to various air pollutants and different elements from this source. Based on random analysis of exhaust emissions, sulfur dioxide and sulfide hydrogen are the major pollutants emitted from the flare gases of this refinery plant34. The sampling points have an average altitude of about 1000–1250 m and a slope of less than 20%. The climate of the region is semiarid and influenced by Mediterranean winds. The predominant wind direction was west and southwest. The highest and lowest air temperatures were 41.4 °C and − 11.3 °C, respectively. The average annual rainfall was 71.94 mm (http://www.amarilam.ir).Samples collection and analysesAll methods were carried out in accordance with the relevant institutional, national, and international guidelines and legislation. Besides they were discussed and approved by the Research Ethics Committee of Tarbiat Modares University. The formal identification of the Quercus brantii Lindl. was performed by H. Dadkhah-Aghdash based on colorful Flora of Iran35. The permissions or licenses to collect Brant oak (Quercus brantii Lindl.) trees in Zagros forests were obtained. A voucher specimen of Brant oaks were collected and deposited at the Herbarium of department of Plant Biology of Tarbiat Modares University.We studied different distances (1000, 1500, 2000, 2500, and 10,000 m [control]) in an easterly direction from the gas refinery. The map of study area was drawn by software of ArcGIS version of 10.5, https://desktop.arcgis.com (Fig. 5). At each distance, three soil samples taken from the depth of 0–20 cm with a plastic gardening shovel, 30 healthy and mature leaves were collected from a certain height (nearly the middle of the canopy) and the outer canopy of three Brant oak trees in the late spring, summer, and autumn of 2019. These trees with average height and diameter at breast height of 5.5 m and 45 cm were selected randomly. The leaf and soil samples were put into polyethylene bags and transported to the laboratory for analysis36.Figure 5Locations of collection sites of soil samples and Brant oak leaves at five different distances (1000, 1500, 2000, 2500 and 10,000 m [control]) from the gas refinery (drawn by H. Dadkhah-Aghdash using software of ArcGIS Desktop. version of 10.5. ESRI, California, US. https://desktop.arcgis.com).Full size imageIn the lab, firstly the leaves were categorized into two types: unwashed leaves and leaves washed with ethylenediaminetetraacetic acid (EDTA) solution to remove some atmospheric dusts and particles deposition. The leaf and soil samples were dried for 10 days until they reached a constant weight at lab temperature. The leaves were grinded and homogenized, soils were sieved with ASTM mesh (DAMAVAND, Iran) with a diameter of 2 mm and homogenized.To determine the pH and electrical conductivity (EC) of soils, 2 g of the soil samples were shaken in 10 ml of double-distilled water with a ratio of 1:5; after 1 h, the pH and electrical conductivity (EC) of the solution were measured by a digital pH meter (Fan Azma Gostar Company, Iran) and EC meter (Sartorius, PT-20, USA). The analysis of the particle sizes of the soil was carried out using the hydrometer method and texture class was determined with a soil texture triangle37.According to different U.S.EPA protocols that were modified by following references, the soil and leaf samples were prepared and dissolved. The digestion of soil samples was conducted with a mixture of concentrated HF–HClO4–HNO338. Approximately 0.5 g of dry soil sample was digested with 10 mL of HCl on a hot plate at ~ 180 °C until the solution was reduced to 3 mL. Approximately 5 mL of HF (40%, w/w), 5 mL of HNO3 (63%, w/w), and 3 mL of HClO4 (70%, w/w) were then added and the solution was digested. This process was continued with adding 3 mL of HNO3, 3 mL of HF, and 1 mL of HClO4 until the silicate minerals had fully disappeared. This solution was transferred to a 25 mL volumetric tube, and 1% HNO3 was added to bring the sample up to a constant volume for the element’s determinations. After filtering the digested samples, the concentrations of sulfur (S), arsenic (As), chromium (Cr), copper (Cu), lead (Pb), zinc (Zn), manganese (Mn), and nickel (Ni) were measured via inductively coupled plasma mass spectrometry (ICP-MS,7500 CS, Agilent, US). The procedures of quality assurance and quality control (QA/QC) were performed.To quantify element contents from soil samples, external standards with calibration levels were used. The precision and the repeatability of the analysis were tested on the instrument by analyzing three replicate samples.According to Liang et al.39 leaf samples were acid digested and sieved powder samples were placed in the acid-washed tubes and 10 mL of 65% nitric acid was added to it. The solution was placed at room temperature overnight (12 h) after that, it was placed for 4 h at 100 °C and then 4 h at 140 °C until the solution color was clear. After cooling, the solution was diluted by deionized water to 50 mL and then passed through Whatman filter paper until 25 mL of the filtrate volume was provided. Each sample was digested three times and the average of measurements is reported. Total plant elements were measured by using the ICP-MS (7500 CS, Agilent, US). A control sample was also used beside each sample to determine the background pollution during digestion. To confirm the accuracy of the methodology and to ensure the extraction of trace elements from the leaf samples, the standard solution of each studied elements was used.Measuring of pollution levels of different elements in soils and leavesFor assessment of contamination levels (concentration) of different elements in soils and trees, common indices of pollution including geoaccumulation index (Igeo), pollution index (PI), pollution load index (PLI), enrichment factor of plants (EFplant), bioconcentration factor (BCF), air originated metals (AOM ), metal accumulation index (MAI) were used.Igeo was calculated using the following (Eq. 1):$${text{I}}_{{{text{geo}}}} = log_{2} left[ {{text{C}}_{{text{n}}} / 1.5{text{ B}}_{{text{n}}} } right]$$
(1)
where Cn is the measured concentration of the element n, Bn is the geoaccumulation background for this element and 1.5 is a constant coefficient used to eliminate potential variations in the baseline data40. The Igeo classifies samples into seven grades:  5 for extremely polluted30.The first PI is expressed as (Eq. 2):$${text{PI }} = {text{ C}}_{{text{i}}} /{text{S}}_{{text{i}}}$$
(2)
where Ci is the concentration of element i in the soil (mg kg−1) and Si is the soil quality standard or reference value for element i (mg kg−1). The PLI for different elements is calculated via the (Eq. 3):$${text{PLI}} = left( {{text{PI}}_{{1}} times {text{ PI}}_{{2}} times {text{ PI}}_{{3}} times cdots times {text{PI}}_{{text{n}}} } right)^{{{1}/{text{n}}}}$$
(3)
The PLI of soils is classified as follows: PLI  More

Environmental variable quantification along an altitudinal gradientThis study area included 22 sampling sites, and 66 samples, classified into three transects, namely Transect 1 (1047–1587 m), Transect 2 (876–3070 m), and Transect 3 (1602–2110 m). Significant differences in soil properties and plant parameters were observed along the three studied altitudinal transects (P  0.05%, while the remaining bacteria were merged into an “others” class. As shown in Fig. 1B, the proportion of Actinobacteria, Alphaproteobacteria and Gammaproteobacteria at each elevation was 45%, whereas Deltaproteabacteria, Acidobacteria_Subgroup_6, and Gemmatimonadetes were prevalent at low levels in most soil samples. At the genus level (Supplementary Fig. 1), 76 genera were detected in the research areas, with the dominant genera including norank_f_67-14_ o_Solirubrobacterales (5.72%), Rubrobacter (4.35%), Solirubrobacter (2.83%), Pseudonocardia (2.26%) and Bradyrhizobium (2.19%) and less than 0.01% of the bacterial genera were classified into others.Figure 1Bacterial community composition variations at the phylum (A) and class (B) levels in soil samples collected at different levels. These were done in R (v3.3.1, http://www.R-project.org).Full size imageBacterial community composition varies along elevation gradientsWe next sought to analyze the differences in relative bacterial abundance at the phylum level among Transects 1–3 (Fig. 2). Significant differences in the relative abundance of Actinobacteria, Proteobacteria, Acidobacteria, Verrucomicrobia, Firmicutes, and Rokubacteria were detected in samples from the different transects (Fig. 2A). The relative abundance of Actinobacteria and Firmicutes in Transect 1 (48.64% and 1.89%, respectively) was significantly higher than in Transect 2 (38.43% and 1.49%, respectively) and Transect 3 (39.63% and 0.98%, respectively) (P  More

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1.Carter, D. O., Yellowlees, D. & Tibbett, M. Cadaver decomposition in terrestrial ecosystems. Naturwissenschaften 94(1), 12–24 (2007).ADS 
CAS 
PubMed 

Google Scholar 
2.Weathers, K. C., Strayer, D. L. & Likens, G. E. Fundamentals of Ecosystem Science (Academic Press, 2012).
Google Scholar 
3.Payne, J. A. A summer carrion study of the baby pig Sus scrofa Linnaeus. Ecology 46(5), 592–602 (1965).
Google Scholar 
4.Anderson, G. S., Cervenka, V. J., Haglund, W. & Sorg, M. Insects associated with the body: Their use and analyses. Adv. Forens. Taphonomy 2, 1 (2002).
Google Scholar 
5.Smith, K. G. A manual of forensic entomology. (1986).6.Tomberlin, J. K., Benbow, M. E., Tarone, A. M. & Mohr, R. M. Basic research in evolution and ecology enhances forensics. Trends Ecol. Evol. 26(2), 53–55 (2011).PubMed 

Google Scholar 
7.Benbow, M. E., Tomberlin, J. K. & Tarone, A. M. Carrion Ecology, Evolution, and Their Applications (CRC Press, 2015).
Google Scholar 
8.Wilson, E. E., Mullen, L. M. & Holway, D. A. Life history plasticity magnifies the ecological effects of a social wasp invasion. Proc. Natl. Acad. Sci. 106(31), 12809–12813 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Pechal, J. L. et al. Field documentation of unusual post-mortem arthropod activity on human remains. J. Med. Entomol. 52(1), 105–108 (2015).PubMed 

Google Scholar 
10.Campobasso, C. P., Marchetti, D., Introna, F. & Colonna, M. F. Postmortem artifacts made by ants and the effect of ant activity on decompositional rates. Am. J. Forens. Med. Pathol. 30(1), 84–87 (2009).
Google Scholar 
11.Eubanks, M. D., Lin, C. & Tarone, A. M. The role of ants in vertebrate carrion decomposition. Food Webs 18, e00109 (2019).
Google Scholar 
12.Cornaby, B. W. Carrion reduction by animals in contrasting tropical habitats. Biotropica 2, 51–63 (1974).
Google Scholar 
13.Andrade-Silva, J., Pereira, E. K. C., Silva, O., Delabie, J. H. C. & Rebelo, J. M. M. Ants (Hymenoptera: Formicidae) associated with pig carcasses in an urban area. Sociobiology 62(4), 527–532 (2015).
Google Scholar 
14.Chin, H. C. et al. Ants (Hymenoptera: Formicidae) associated with pig carcasses in Malaysia. Trop. Biomed. 26(1), 106–109 (2009).
Google Scholar 
15.Prado Castro, C., García, M. D., Palma, C. & Martínez-Ibáñez, M. D. First report on sarcosaprophagous Formicidae from Portugal (Insecta: Hymenoptera). Annales de la Société entomologique de France 50(1), 51–58 (2014).
Google Scholar 
16.Neto-Silva, A., Dinis-Oliveira, R. J. & Prado e Castro, C.,. Diversity of the Formicidae (Hymenoptera) carrion communities in Lisbon (Portugal): Preliminary approach as seasonal and geographic indicators. Forens. Sci. Res. 3(1), 65–73 (2018).
Google Scholar 
17.Payne, J. A., King, E. W. & Beinhart, G. Arthropod succession and decomposition of buried pigs. Nature 219(5159), 1180–1181 (1968).ADS 
CAS 
PubMed 

Google Scholar 
18.Meyer, F., Monroe, M. D., Williams, H. N. & Goddard, J. Solenopsis invicta x richteri (Hymenoptera: Formicidae) necrophagous behavior causes post-mortem lesions in pigs which serve as oviposition sites for Diptera. Forens. Sci. Int. Rep. 2, 100067 (2020).
Google Scholar 
19.De Jong, G. D., Meyer, F. & Goddard, J. Relative roles of blow flies (Diptera: Calliphoridae) and invasive fire ants (Hymenoptera: Formicidae: Solenopsis spp.) in carrion decomposition. J. Med. Entomol. 58(3), 1074–1082 (2021).PubMed 

Google Scholar 
20.Early, M. & Goff, M. L. Arthropod succession patterns in exposed carrion on the island of O’ahu, Hawaiian Islands, USA. J. Med. Entomol. 23(5), 520–531 (1986).CAS 
PubMed 

Google Scholar 
21.Stoker, R. L., Grant, W. E. & Bradleigh Vinson, S. Solenopsis invicta (Hymenoptera: Formicidae) effect on invertebrate decomposers of carrion in central Texas. Environ. Entomol. 24(4), 817–822 (1995).
Google Scholar 
22.Ekanem, M. S. & Dike, M. C. Arthropod succession on pig carcasses in southeastern Nigeria. Papeis Avulsos de Zoologia 50, 561–570 (2010).
Google Scholar 
23.Lindgren, N. K., Bucheli, S. R., Archambeault, A. D. & Bytheway, J. A. Exclusion of forensically important flies due to burying behavior by the red imported fire ant (Solenopsis invicta) in southeast Texas. Forensic Sci. Int. 204(1–3), e1–e3 (2011).PubMed 

Google Scholar 
24.Pereira, E. K. C. et al. Solenopsis saevissima (Smith) (Hymenoptera: Formicidae) activity delays vertebrate carcass decomposition. Sociobiology 64(3), 369–372 (2017).
Google Scholar 
25.Dussutour, A. & Simpson, S. J. Description of a simple synthetic diet for studying nutritional responses in ants. Insectes Soc. 55(3), 329–333 (2008).
Google Scholar 
26.Csata, E. & Dussutour, A. Nutrient regulation in ants (Hymenoptera: Formicidae): A review. Myrmecol. News 29, 111–124 (2019).
Google Scholar 
27.Tschinkel, W. R. The Fire Ants (Belknap Press, 2013).
Google Scholar 
28.Paula, M. C. et al. Action of ants on vertebrate carcasses and blow flies (Calliphoridae). J. Med. Entomol. 53(6), 1283–1291 (2016).PubMed 

Google Scholar 
29.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).
Google Scholar 
30.Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.2 4, (2019).31.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage publications, 2018).
Google Scholar 
32.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometr. J. 50(3), 346–363 (2008).MathSciNet 
MATH 

Google Scholar 
33.Porter, S. D., Bhatkar, A., Mulder, R., Vinson, B. S. & Clair, D. J. Distribution and density of polygyne fire ants (Hymenoptera: Formicidae) in Texas. J. Econ. Entomol. 84(3), 866–874 (1991).CAS 
PubMed 

Google Scholar 
34.Cook, S. C., Eubanks, M. D., Gold, R. E. & Behmer, S. T. Colony-level macronutrient regulation in ants: mechanisms, hoarding and associated costs. Anim. Behav. 79(2), 429–437 (2010).
Google Scholar 
35.Smith, C. R. & Tschinkel, W. R. Ant fat extraction with a Soxhlet extractor. Cold Spring Harbor Protocols 7, 5243 (2009).
Google Scholar 
36.Wills, B. D. et al. Effect of carbohydrate supplementation on investment into offspring number, size, and condition in a social insect. PLoS ONE 10(7), e0132440 (2015).PubMed 
PubMed Central 

Google Scholar 
37.Bockoven, A. A., Wilder, S. M. & Eubanks, M. D. Intraspecific variation among social insect colonies: persistent regional and colony-level differences in fire ant foraging behavior. PLoS ONE 10(7), e0133868 (2015).PubMed 
PubMed Central 

Google Scholar 
38.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67(1), 1–48 (2015).
Google Scholar 
39.Gavilanez-Slone, J. & Porter, S. D. Colony growth of two species of Solenopsis fire ants (Hymenoptera: Formicidae) reared with crickets and beef liver. Florida Entomol. 96(4), 1482–1488 (2013).
Google Scholar 
40.Sorensen, A. A., Busch, T. M. & Vinson, S. B. Factors affecting brood cannibalism in laboratory colonies of the imported fire ant, Solenopsis invicta Buren (Hymenoptera: Formicidae). J. Kansas Entomol. Soc. 2, 140–150 (1983).
Google Scholar 
41.Williams, D. F., Vander Meer, R. K. & Lofgren, C. S. Diet-induced nonmelanized cuticle in workers of the imported fire ant Solenopsis invicta Buren. Arch. Insect Biochem. Physiol. 4(4), 251–259 (1987).CAS 

Google Scholar 
42.Porter, S. D. Effects of diet on the growth of laboratory fire ant colonies (Hymenoptera: Formicidae). J. Kansas Entomol. Soc. 2, 288–291 (1989).
Google Scholar 
43.Bhatkar, A. & Whitcomb, W. H. Artificial diet for rearing various species of ants. Florida Entomol. 2, 229–232 (1970).
Google Scholar 
44.Porter, S. D., Valles, S. M. & Gavilanez-Slone, J. M. Long-term efficacy of two cricket and two liver diets for rearing laboratory fire ant colonies (Hymenoptera: Formicidae: Solenopsis invicta). Florida Entomol. 98(3), 991–993 (2015).
Google Scholar 
45.Arganda, S. et al. Parsing the life-shortening effects of dietary protein: Effects of individual amino acids. Proc. R. Soc. B Biol. Sci. 284(1846), 20162052 (2017).
Google Scholar 
46.Tschinkel, W. R. Sociometry and sociogenesis of colonies of the fire ant Solenopsis Invicta during one annual cycle. Ecol. Monogr. 63(4), 425–457 (1993).
Google Scholar 
47.Deslippe, R. J. & Savolainen, R. Sex investment in a social insect: The proximate role of food. Ecology 76(2), 375–382 (1995).
Google Scholar 
48.Rosenfeld, C. S. & Roberts, R. M. Maternal diet and other factors affecting offspring sex ratio: A review. Biol. Reprod. 71(4), 1063–1070 (2004).CAS 
PubMed 

Google Scholar 
49.Hasegawa, E. Sex allocation in the ant Camponotus (Colobopsis) nipponicus (Wheeler): II. The effect of resource availability on sex-ratio variability. Insectes Soc. 60(3), 329–335 (2013).
Google Scholar 
50.Knaden, M. & Graham, P. The sensory ecology of ant navigation: from natural environments to neural mechanisms. Annu. Rev. Entomol. 61, 63–76 (2016).CAS 
PubMed 

Google Scholar 
51.Liu, W., Longnecker, M., Tarone, A. M. & Tomberlin, J. K. Responses of Lucilia sericata (Diptera: Calliphoridae) to compounds from microbial decomposition of larval resources. Anim. Behav. 115, 217–225 (2016).
Google Scholar 
52.Tomberlin, J. K. et al. Indole: An evolutionarily conserved influencer of behavior across kingdoms. BioEssays 39(2), 1600203 (2017).
Google Scholar 
53.Frederickx, C. et al. Volatile organic compounds released by blowfly larvae and pupae: New perspectives in forensic entomology. Forensic Sci. Int. 219(1–3), 215–220 (2012).CAS 
PubMed 

Google Scholar 
54.Frederickx, C., Dekeirsschieter, J., Verheggen, F. J. & Haubruge, E. Host-habitat location by the parasitoid, Nasonia vitripennis Walker (Hymenoptera: Pteromalidae). J. Forensic Sci. 59(1), 242–249 (2014).CAS 
PubMed 

Google Scholar 
55.Schettino, M. et al. Response of a predatory ant to volatiles emitted by aphid-and caterpillar-infested cucumber and potato plants. J. Chem. Ecol. 43(10), 1007–1022 (2017).CAS 
PubMed 

Google Scholar 
56.Sawyer, S. J., Rusch, T. W., Tarone, A. M. & Tomberlin, J. K. Wing buzzing as a potential antipredator defense against an invasive predator. Food Webs 27, e00192 (2021).
Google Scholar 
57.Wells, J. D. & Greenberg, B. Effect of the red imported fire ant (Hymenoptera: Formicidae) and carcass type on the daily occurrence of postfeeding carrion-fly larvae (Diptera: Calliphoridae, Sarcophagidae). J. Med. Entomol. 31(1), 171–174 (1994).CAS 
PubMed 

Google Scholar  More

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1.FAO. Superfruits: Myth or truth? in Proceedings International Symposium, Ho Chi Minh, Vietnam, 140 (2013).
2.Chamberlain, J., Darr, D. & Meinhold, K. Rediscovering the contributions of forest and trees to transition global food system. Forests 11, 1098. https://doi.org/10.3390/f11101098 (2020).Article 

Google Scholar 
3.Vanzani, P. et al. Wild mediterranean plants as traditional food: A valuable source of antioxidants. J. Food Sci. 76, 46–51 (2011).Article 

Google Scholar 
4.Genskowsky, E. et al. Determination of polyphenolic profile, antioxidant activity and antibacterial properties of maqui [Aristotelia chilensis (Molina) Stuntz] a Chilean blackberry. J. Sci. Food Agric. 96, 4235–4242 (2016).CAS 
Article 

Google Scholar 
5.Benedetti, S. Monografía de maqui, Aristotelia chilensis (Mol.) Stuntz 60 (Instituto Forestal, 2012).
Google Scholar 
6.Vogel, H., Razmilic, H., San Martin, I., Doll, U. & González, B. Plantas Medicinales Chilena. Experiencias de domesticación y cultivo de Boldo, Matico, Bailahuén, Canelo, Peumo y maqui. Editorial Universitaria de Talca, 192 (2005).7.Gironés-Vilaplana, A., Mena, P., García-Viguera, C. & Moreno, D. A novel beverage rich in antioxidant phenolics: Maqui berry (Aristotelia chilensis) and lemon juice. Food Sci. Tech. 47, 279–286 (2012).
Google Scholar 
8.Quispe-Fuentes, I., Vega-Gálvez, A., Vásquez, V., Uribe, E. & Astudillo, S. Mathematical modeling and quality properties of a dehydrated native Chilean berry. J. Food Process Eng. 40, 124–132 (2017).Article 

Google Scholar 
9.Fredes, C., Montenegro, G., Zoffoli, J., Gómez, M. & Robert, P. Polyphenol content and antioxidant activity of maqui during fruit development and maturation in central Chile. Chilean J. Agric. Res. 72, 582–589 (2012).Article 

Google Scholar 
10.Céspedes, C., El-Hafidi, M., Pavon, N. & Alarcon, J. Antioxidant and cardioprotective activities of phenolic extracts from fruits of Chilean blackberry Aristotelia chilensis (Elaeocarpaceae), Maqui. Food Chem. 107, 820–829 (2008).Article 

Google Scholar 
11.Céspedes, C., Alarcon, J., Avila, J. & Nieto, A. Anti-inflammatory activity of Aristotelia chilensis (stuntz) (Elaeocarpaceae). Boletín Latinoamericano y del Caribe de Plantas Medicinales y Aromáticas 9, 91–99 (2010).
Google Scholar 
12.Céspedes, C. et al. The chilean superfruit black-berry Aristotelia chilensis (Elaeocarpaceae), Maqui as mediator in inflammation-associated disorders. Food Chem. Toxicol. 108, 438–450 (2017).
13.Muñoz, O. et al. Chemical study and anti-inflammatory, analgesic and antioxidant activities of the leaves of Aristotelia chilensis (Mol.) Stuntz, Elaeocarpaceae. J. Pharm. Pharmacol. 63, 849–859 (2011).Article 

Google Scholar 
14.Rojo, L. et al. In vitro and in vivo anti-diabetic effects of anthocyanins from maqui berry (Aristotelia chilensis). Food Chem. 131, 387–396 (2012).CAS 
Article 

Google Scholar 
15.Zúñiga, G., Tapia, A., Arenas, A., Contreras, R. & Zuñiga-Libano, G. Phytochemistry and biological properties of Aristotelia chilensis a Chilean blackberry: A review. Phytochem. Rev. 16, 1081–1094. https://doi.org/10.1007/s11101-017-9533-1 (2017).CAS 
Article 

Google Scholar 
16.Vogel, H. et al. Maqui (Aristotelia chilensis): Morpho-phenological characterization to design high-yielding cultivation techniques. J. Appl. Res. Med. Aromat. Plants. 1, 123–133 (2014).
Google Scholar 
17.Liu, Y. & El-Kassaby, Y. Phenotypic plasticity of natural Populus trichocarpa populations in response to temporally environmental change in a common garden. BMC Evol. Biol. 19, 231. https://doi.org/10.1186/s12862-019-1553-6 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Villemereuil, P., Gaggiotti, O., Mouterde, M. & Till-Bottraud, I. Common garden experiment in the genomic era: New perspectives and opportunities. Heredity 116, 249–254 (2016).Article 

Google Scholar 
19.Torres-Ruiz, J. et al. Genetic differentiation in functional traits among European sessile oak populations. Tree Physiol. 39, 1736–1749. https://doi.org/10.1093/treephys/tpz090 (2019).Article 
PubMed 

Google Scholar 
20.Sáenz-Romero, C., Kremer, A., Nagy, L., Kehlet, J. & Mátyás, C. Common garden comparison confirm inherited differences in sensitivity to climate change between forest tree species. PerrJ. 7, 6213. https://doi.org/10.7717/peerj.6213 (2019).Article 

Google Scholar 
21.Aspinwall, M. et al. Adaptation and acclimation both influence photosynthetic and respiratory temperature responses in Corymbia calophylla. Tree Physiol. 8, 1095–1112. https://doi.org/10.1093/treephys/tpx047 (2017).CAS 
Article 

Google Scholar 
22.Knutzen, F., Meier, I. & Leuschner, C. Does reduced precipitation trigger physiological and morphological drought adaptations in European beech (Fagus sylvatica L.)? Comparing provenances across a precipitation gradient. Tree Physiol. 35, 949–963. https://doi.org/10.1093/treephys/tpv057 (2015).CAS 
Article 
PubMed 

Google Scholar 
23.Mkwezalamba, I., Munthali, C. & Missanjo, E. Phenotypic variation in fruit morphology among provenances of Sclerocarya birrea (A. Rich.) Hochst. Int. J. Forestry Res. 1, 1–8. https://doi.org/10.1155/2015/735418 (2015).Article 

Google Scholar 
24.Sudrajat, D. Genetic variation of fruit, seed, and seedling characteristics among 11 populations of white Jabon in Indonesia. For. Sci. Tech. 12(1), 9–15. https://doi.org/10.1080/21580103.2015.1007896 (2016).Article 

Google Scholar 
25.Teklehaimanot, Z., Lanek, J. & Tomlinson, H. Provenance variation in morphology and leaflet anatomy of Parkia biglobosa and its relation to drought tolerance. Trees 13, 96–102. https://doi.org/10.1007/pl00009742 (1998).Article 

Google Scholar 
26.Åkerström, A., Jaakola, L., Bång, U. & Jäderlund, A. Effects of latitude-related factors and geographical origin on anthocyanidin concentrations in fruits of Vaccinium myrtillus L. (Bilberries). J. Agric. Food Chem. 58, 11939–11945. https://doi.org/10.1021/jf102407n (2010).CAS 
Article 
PubMed 

Google Scholar 
27.Lätti, A., Riihinen, K. & Kainulainen, P. Analysis of anthocyanin variation in wild populations of bilberry (Vaccinium myrtillus L.) in Finland. J. Agric. Food Chem. 56, 190–196. https://doi.org/10.1021/jf072857m (2008).CAS 
Article 
PubMed 

Google Scholar 
28.Uleberg, E. et al. Effects of temperature and photoperiod on yield and chemical composition of Northern and Southern Clones of Bilberry (Vaccinium myrtillus L.). J. Agric. Food Chem. 60, 10406–10414. https://doi.org/10.1021/jf302924m (2012).CAS 
Article 
PubMed 

Google Scholar 
29.Moya, M., González, B., Doll, U., Yuri, J. A. & Vogel, H. Different covers affect growth and development of three maqui clones (Aristotelia chilensis [Molina] Stuntz). J. Berry Res. 1, 1–10. https://doi.org/10.3233/jbr-180377 (2019).Article 

Google Scholar 
30.Cona, M. et al. New polymorphic nuclear microsatellites from Aristotelia chilensis (Mol.) Stuntz (Elaeocarpaceae). Chilean J. Agri. Res. 80, 153–160. https://doi.org/10.4067/S0718-58392020000200153 (2020).Article 

Google Scholar 
31.Hamrick, J. Response of forest trees to global environmental changes. For. Ecol. Manag. 197, 323–335. https://doi.org/10.1016/j.foreco.2004.05.023 (2004).Article 

Google Scholar 
32.Salgado, P., Prinz, K., Finkeldey, R., Ramírez, C. & Vogel, H. Genetic variability of Aristotelia chilensis (“maqui”) based on AFLP and chloroplast microsatellite markers. Gen. Resour. Crop Evol. 64, 2083–2091 (2017).CAS 
Article 

Google Scholar 
33.Holderegger, R., Kamm, U. & Gugerli, F. Adaptive vs. neutral genetic diversity: Implications for landscape genetics. Landsc. Ecol. 21, 797–807. https://doi.org/10.1007/s10980-005-5245-9 (2006).Article 

Google Scholar 
34.O’Brien, E., Mazanex, R. & Krauss, S. Provenance variation of ecologically important traits of forest trees: implications for restoration. J. Appl. Ecol. 44, 583–593. https://doi.org/10.1111/j.1365-2664.2007.01313.x (2007).Article 

Google Scholar 
35.Singleton, V. & Rossi, J. Colorimetry of total phenolics withphosphomolybdic-phosphotungstic acid reagents. Am. J. Enol. Vitic. 16, 144–158 (1965).CAS 

Google Scholar 
36.Giusti, M. & Wrolstad, R. Current protocols in food analytical chemistry. In Current Protocols in Food Analytical Chemistry (eds Wrolstad, R. et al.) F1.2.1-F1.2.13 (Wiley, 2001).
Google Scholar 
37.González, B., Vogel, H., Razmilic, I. & Wolfram, E. Polyphenol, anthocyanin and antioxidant content in different parts of maqui fruits (Aristotelia chilensis) during ripening and conservation treatments after harvest. Ind. Crops Prod. 76, 158–165. https://doi.org/10.1016/j.indcrop.2015.06.038 (2015).CAS 
Article 

Google Scholar 
38.Winn, M., Araman, P. & Lee, S-M. UrbanCrowns: An assessment and monitoring tool for urban trees. Gen. Tech. Rep. SRS-135. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 10 (2011).39.Welham, S., Cullis, B., Gogel, B., Gilmour, A. & Thompson, R. Prediction in linear mixed models. Aust. N. Z. J. Stat. 46, 325–347 (2004).MathSciNet 
Article 

Google Scholar 
40.Bastías, A. et al. Identification and characterization of microsatellite loci in Maqui (Aristotelia chilensis (Molina) Stuntz) using next-generation sequencing (NGS). PLoS ONE 11, e0159825. https://doi.org/10.1371/journal.pone.0159825 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Espinoza, S. et al. Influence of provenance origin on the early performance of two sclerophyllous Mediterranean species established in burned drylands. Sci. Rep. 11, 6212. https://doi.org/10.1038/s41598-021-85599-3 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Vander Mijnsbrugge, K., Bischoff, A. & Smith, B. A question of origin: Where and how to collect seed for ecological restoration. Basic Appl. Ecol. 11, 300–311. https://doi.org/10.1016/j.baae.2009.09.002 (2010).Article 

Google Scholar 
43.Gao, S. B. et al. Phenotypic plasticity vs. local adaptation in quantitative traits differences of Stipa grandis in semi-arid steppe, China. Sci. Rep. 8, 3148. https://doi.org/10.1038/s41598-018-21557-w (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
44.Lusk, C. & Del Pozo, A. Survival and growth of seedlings of 12 Chilean rainforest trees in two light environments: Gas exchange and biomass distribution correlates. Aust. Ecol. 27, 173–182. https://doi.org/10.1046/j.1442-9993.2002.01168.x (2002).Article 

Google Scholar 
45.Brito, C., Bown, H., Fuentes, J., Franck, N. & Perez-Quezada, J. Mesophyll conductance constrains photosynthesis in three common sclerophyllous species in Central Chile. Rev. Chilena de Historia Natural. https://doi.org/10.1186/s40693-014-0008-0 (2014).Article 

Google Scholar 
46.Prado, C. & Damascos, M. Gas exchange and leaf specific mass of different foliar cohorts of the wintergreen shrub Aristotelia chilensis (Mol.) Stuntz (Eleocarpaceae) fifteen days before the flowering and the fall of the old cohort. Braz. Arch. Biol. Tech. 44, 277–282 (2001).Article 

Google Scholar 
47.Repetto-Giavalli, F., Cavieres, L. & Simonetti, J. Respuestas foliares de Aristotelia chilensis (Molina) Stuntz (Elaeocarpaceae) a la fragmentación del bosque maulino. Revista Chilena Hist. Nat. 80, 469–477 (2007).
Google Scholar 
48.Bustan, A. et al. Fruit load governs transpiration of olive trees. Tree Physiol. 36, 380–391. https://doi.org/10.1093/treephys/tpv138 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Wünsche, J. & Lakso, A. Apple tree physiology—Implications for orchard and tree management. Compact Fruit Tree 33, 82–88 (2000).
Google Scholar 
50.Kelc, D., Vindis, P., Lakota, M. Measurements of Photosynthesis and Transpiration on Apple Trees, Chapter 18 in DAAAM International Scientific Book 2015. in (ed. Katalinic, B.), 199–208. (DAAAM International, 2015). https://doi.org/10.2507/daaam.scibook.2015.18. (ISBN 978-3-902734-05-1, ISSN 1726–9687).51.Lortie, C. & Aarssen, L. The specialization hypothesis for phenotypic plasticity in plants. Int. J. Plant Sci. 157, 484–487. https://doi.org/10.1086/297365 (1996).Article 

Google Scholar 
52.Nemeskéri, E. & Helyes, L. Physiological responses of selected vegetable crop species to water stress. Agronomy 9, 447. https://doi.org/10.3390/agronomy9080447 (2019).CAS 
Article 

Google Scholar 
53.Tian, M., Yu, G., He, N. & Hou, J. Leaf morphological and anatomical traits from tropical to temperate coniferous forests: Mechanisms and influencing factors. Sci. Rep. https://doi.org/10.1038/srep19703 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Poorter, H., Niinemets, Ü., Poorter, L., Wright, I. J. & Villar, R. Causes and consequences of variation in leaf mass per area (LMA): A meta-analysis. New Phytol. 182, 565–588. https://doi.org/10.1111/j.1469-8137.2009.02830.x (2009).Article 
PubMed 

Google Scholar 
55.Allegro, G., Pastore, C., Valentini, G. & Filippetti, I. The evolution of phenolic compounds in Vitis vinifera L. red berries during ripening: Analysis and role on wine sensory—A review. Agronomy 11, 999. https://doi.org/10.3390/agronomy11050999 (2021).CAS 
Article 

Google Scholar 
56.Chagné, D. et al. Genetic and environmental control of fruit maturation, dry matter and firmness in apple (Malus × domestica Borkh.). Hortic. Res. 1, 14046. https://doi.org/10.1038/hortres.2014.46 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Gashu, K. et al. Temperature shift between vineyards modulates berry phenology and primary metabolism in a varietal collection of wine grapevine. Front. Plant Sci. 11, 588739. https://doi.org/10.3389/fpls.2020.588739 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
58.Suter, B., Destrac Irvine, A., Gowdy, M., Dai, Z. & van Leeuwen, C. Adapting wine grape ripening to global change requires a multi-trait approach. Front. Plant Sci. 12, 624867. https://doi.org/10.3389/fpls.2021.624867 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Nesmith, D. Fruit development period of several Southern Highbush Blueberry Cultivars. Int. J. Fruit Sci. 12, 249–255. https://doi.org/10.1080/15538362.2011.619430 (2012).Article 

Google Scholar 
60.Romero-Román, M. et al. Native species facing climate changes: Response of Calafate Berries To Low Temperature and UV radiation. Foods. 10, 196. https://doi.org/10.3390/foods10010196 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Cabrera, S., Bozzo, S. & Fuenzalida, H. Variations in UV radiation in Chile. J. Photochem. Photobiol. 28, 137–142 (1995).CAS 
Article 

Google Scholar 
62.Ebel, R. C., Proebsting, E. L. & Evans, R. G. Deficit irrigation to control vegetative growth in apple and monitoring fruit growth to schedule irrigation. HortScience 30, 1229–1232. https://doi.org/10.21273/hortsci.30.6.1229 (1995).Article 

Google Scholar 
63.Fereres, E. & Soriano, M. A. Deficit irrigation for reducing agricultural water use. J. Exp. Bot. 58(2), 147–159. https://doi.org/10.1093/jxb/erl165 (2007).CAS 
Article 
PubMed 

Google Scholar 
64.Barnuud, N., Zerihun, A., Gibberd, M. & Bates, B. Berry composition and climate: Responses and empirical models. Inter. J. Biometeor. 58, 1207–1223. https://doi.org/10.1007/s00484-013-0715-2 (2014).ADS 
Article 

Google Scholar 
65.Spinardi, A., Cola, G., Gardana, C. & Mignani, I. Variation of anthocyanin content and profile throughout fruit development and ripening of highbush blueberry cultivars grown at two different altitudes. Front. Plant Sci. 10, 1045. https://doi.org/10.3389/fpls.2019.01045 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Stevenson, D. & Scalzo, J. Anthocyanin composition and content of blueberries from around the world. J. Berry Res. 2, 179–189. https://doi.org/10.3233/JBR-2012-038 (2012).CAS 
Article 

Google Scholar 
67.Zarrouk, O. et al. Grape ripening is regulated by deficit irrigation/elevated temperatures according to cluster position in the canopy. Front. Plant Sci. https://doi.org/10.3389/fpls.2016.01640 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
68.Prange, R. K. & DeEll, J. R. Preharvest factors affecting postharvest quality of berry crops. HortScience 32, 824–830. https://doi.org/10.21273/hortsci.32.5.824 (1997).Article 

Google Scholar 
69.Mignard, O., Beguería, S., Reig, G. & Fonti, C. Genetic origin and climate determine fruit quality and antioxidant traits on apple (Malus × domestica Borkh). Sci. Hortic. 285, 110142. https://doi.org/10.1016/j.scienta.2021.110142 (2021).CAS 
Article 

Google Scholar 
70.González-Villagra, J., Rodrigues-Salvador, A., Nunes-Nesi, A., Cohen, J. & Reyes-Díaz, M. Age-related mechanism and its relationship with secondary metabolism and abscisic acid in Aristotelia chilensis plants subjected to drought stress. Plant Physiol. Biochem. 124, 136–145. https://doi.org/10.1016/j.plaphy.2018.01.010 (2018).CAS 
Article 
PubMed 

Google Scholar 
71.Calderan, A. et al. Managing moderate water deficit increased anthocyanin concentration and proanthocyanidin galloylation in “Refošk” grapes in Northeast Italy. Agric. Water Manage. 246, 106684. https://doi.org/10.1016/j.agwat.2020.106684 (2021).Article 

Google Scholar 
72.Yáñez, M., Seiler, J. & Fox, T. Crown physiological responses of loblolly pine clones and families to silvicultural intensity: Assessing the effect of crown ideotype. For. Ecol. Manage. 398, 25–36. https://doi.org/10.1016/j.foreco.2017.05.002 (2017).Article 

Google Scholar  More

1.Vareda, J. P., Valente, A. J. M. & Durães, L. Assessment of heavy metal pollution from anthropogenic activities and remediation strategies: A review. J. Environ. Manage. 246, 101–118 (2019).CAS 
PubMed 

Google Scholar 
2.Chanamé, F., Custodio, M., Poma-Chávez, C. & Huamán, A. Nutrient concentrations and trophic state of three Andean lakes from Junín, Perú. Rev. Ambient Agua 15, 1–9 (2020).
Google Scholar 
3.Bhardwaj, R., Gupta, A. & Garg, J. K. Evaluation of heavy metal contamination using environmetrics and indexing approach for River Yamuna, Delhi stretch, India. Water Sci. 31, 52–66 (2017).
Google Scholar 
4.Custodio, M. et al. Human risk from exposure to heavy metals and arsenic in water from rivers with mining influence in the Central Andes of Peru. Water (Switzerland) 12, 1–20 (2020).
Google Scholar 
5.Arisekar, U., Jeya, R., Shalini, R. & Jeyasekaran, G. Human health risk assessment of heavy metals in aquatic sediments and freshwater fish caught from Thamirabarani River, the Western Ghats of South Tamil Nadu. Mar. Pollut. Bull. 159, 111496 (2020).CAS 
PubMed 

Google Scholar 
6.Chabukdhara, M. & Nema, A. K. Assessment of heavy metal contamination in Hindon River sediments: A chemometric and geochemical approach. Chemosphere 87, 945–953 (2012).CAS 
PubMed 
ADS 

Google Scholar 
7.Chai, L. et al. Heavy metals and metalloids in the surface sediments of the Xiangjiang River, Hunan, China: Distribution, contamination, and ecological risk assessment. Environ. Sci. Pollut. Res. 24, 874–885 (2017).CAS 

Google Scholar 
8.Liu, T. T. & Yang, H. Comparative analysis of the total and active bacterial communities in the surface sediment of Lake Taihu. FEMS Microbiol. Ecol. 96, 1–11 (2020).CAS 
ADS 

Google Scholar 
9.Custodio, M. et al. Evaluation of surface sediment quality in rivers with fish farming potential (Peru) using indicators of contamination, accumulation and ecological risk of heavy metals and arsenic. J. Ecol. Eng. 22, 78–87 (2021).
Google Scholar 
10.Zhang, Z. et al. Assessment of heavy metal contamination, distribution and source identification in the sediments from the Zijiang River, China. Sci. Total Environ. 645, 235–243 (2018).CAS 
PubMed 
ADS 

Google Scholar 
11.Sojka, M., Jaskula, J. & Siepak, M. Heavy metals in bottom sediments of reservoirs in the lowland area of western Poland: Concentrations, distribution, sources and ecological risk. Water (Switzerland) 11, 1–20 (2018).
Google Scholar 
12.Xu, Z., Te, S. H., Xu, C., He, Y. & Gin, K. Y. H. Variations of bacterial community composition and functions in an estuary reservoir during spring and summer alternation. Toxins (Basel) 10, 1–22 (2018).CAS 

Google Scholar 
13.Xiao, F. et al. The impact of anthropogenic disturbance on bacterioplankton communities during the construction of Donghu Tunnel (Wuhan, China). Microb. Ecol. 77, 277–287 (2019).CAS 
PubMed 

Google Scholar 
14.Wang, B. et al. Bacterial community responses to tourism development in the Xixi National Wetland Park, China. Sci. Total Environ. 720, 137570 (2020).CAS 
PubMed 
ADS 

Google Scholar 
15.Deng, W. et al. Heavy metals, antibiotics and nutrients affect the bacterial community and resistance genes in chicken manure composting and fertilized soil. J. Environ. Manage. 257, 109980 (2020).CAS 
PubMed 

Google Scholar 
16.Gubelit, Y. et al. Nutrient and metal pollution of the eastern Gulf of Finland coastline: Sediments, macroalgae, microbiota. Sci. Total Environ. 550, 806–819 (2016).CAS 
PubMed 
ADS 

Google Scholar 
17.Wang, J. et al. Contribution of heavy metal in driving microbial distribution in a eutrophic river. Sci. Total Environ. 712, 136295 (2020).CAS 
PubMed 
ADS 

Google Scholar 
18.Liao, H. et al. Profiling microbial communities in a watershed undergoing intensive anthropogenic activities. Sci. Total Environ. 647, 1137–1147 (2019).CAS 
PubMed 
ADS 

Google Scholar 
19.Liu, J. et al. Spatiotemporal dynamics of the archaeal community in coastal sediments: Assembly process and co-occurrence relationship. ISME J. 14, 1463–1478 (2020).PubMed 
PubMed Central 

Google Scholar 
20.Liao, H., Yen, J. Y., Guan, Y., Ke, D. & Liu, C. Differential responses of stream water and bed sediment microbial communities to watershed degradation. Environ. Int. 134, 105198 (2020).CAS 
PubMed 

Google Scholar 
21.Song, H., Li, Z., Du, B., Wang, G. & Ding, Y. Bacterial communities in sediments of the shallow Lake Dongping in China. J. Appl. Microbiol. 112, 79–89 (2012).CAS 
PubMed 

Google Scholar 
22.Ligi, T. et al. Characterization of bacterial communities in soil and sediment of a created riverine wetland complex using high-throughput 16S rRNA amplicon sequencing. Ecol. Eng. 72, 56–66 (2014).
Google Scholar 
23.Wilmes, P. et al. Natural acidophilic biofilm communities reflect distinct organismal and functional organization. ISME J. 3, 266–270 (2009).CAS 
PubMed 

Google Scholar 
24.Mavromatis, K. et al. Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nat. Methods. 4, 495–500 (2007).CAS 
PubMed 

Google Scholar 
25.Yuan, X., Zhang, L., Li, J., Wang, C. & Ji, J. Sediment properties and heavy metal pollution assessment in the river, estuary and lake environments of a fluvial plain, China. CATENA 119, 52–60 (2014).CAS 

Google Scholar 
26.Lin, Q., Liu, E., Zhang, E., Li, K. & Shen, J. Spatial distribution, contamination and ecological risk assessment of heavy metals in surface sediments of Erhai Lake, a large eutrophic plateau lake in southwest China. CATENA 145, 193–203 (2016).CAS 

Google Scholar 
27.Guo, T. et al. Distribution of arsenic and its biotransformation genes in sediments from the East China Sea. Environ. Pollut. 253, 949–958 (2019).CAS 
PubMed 

Google Scholar 
28.Taylor, S. R. & Mclennan, S. M. The geochemical the continental evolution crust. Rev. Miner. Geochem. 33, 241–265 (1995).
Google Scholar 
29.Lastauskienė, E. et al. The impact of intensive fish farming on pond sediment microbiome and antibiotic resistance gene composition. Front. Vet. Sci. 8, 1–12 (2021).
Google Scholar 
30.Ragab, S., Sikaily, A. E., Nemr, A. E. & Sea, R. Concentrations and sources of pesticides and PCBs in surficial sediments of the Red Sea coast, Egypt. Egypt. J. Aquat. Res. 42, 365–374 (2016).
Google Scholar 
31.Kavita, V. & Pandey, J. Heavy metal accumulation in surface sediments of the Ganga River (India): Speciation, fractionation, toxicity, and risk assessment. Environ. Monit. Assess. 191, 20 (2019).
Google Scholar 
32.Haghnazar, H. et al. Chemosphere Potentially toxic elements contamination in surface sediment and indigenous aquatic macrophytes of the Bahmanshir River, Iran: Appraisal of phytoremediation capability. 285, (2021).33.Perera, P. C. T., Sundarabarathy, T. V., Sivananthawerl, T., Kodithuwakku, S. P. & Edirisinghe, U. Arsenic and cadmium contamination in water, sediments and fish is a consequence of paddy cultivation: Evidence of river pollution in Sri Lanka. Achiev. Life Sci. 10, 144–160 (2016).
Google Scholar 
34.Kalantzi, I., Rico, A., Mylona, K., Pergantis, S. A. & Tsapakis, M. Fish farming, metals and antibiotics in the eastern Mediterranean Sea: Is there a threat to sediment wildlife?. Sci. Total Environ. 764, 142843 (2021).CAS 
PubMed 
ADS 

Google Scholar 
35.Monroy, M., Maceda-Veiga, A. & de Sostoa, A. Metal concentration in water, sediment and four fish species from Lake Titicaca reveals a large-scale environmental concern. Sci. Total Environ. 487, 233–244 (2014).CAS 
PubMed 
ADS 

Google Scholar 
36.Rodbell, D. T., Delman, E., Abbott, M., Besonen, M. & Tapia, P. The heavy metal contamination of Lake Junín National Reserve, Peru: An unintended consequence of the juxtaposition of hydroelectricity and mining. GSA Today 24, 4–10 (2014).
Google Scholar 
37.Ni, C. et al. High concentrations of bioavailable heavy metals impact freshwater sediment microbial communities. Ann. Microbiol. 66, 1003–1012 (2016).CAS 

Google Scholar 
38.Huang, W. et al. Comparison among the microbial communities in the lake, lake wetland, and estuary sediments of a plain river network. Microbiologyopen https://doi.org/10.1002/mbo3.644 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Abia, A. L. K., Alisoltani, A., Keshri, J. & Ubomba-Jaswa, E. Metagenomic analysis of the bacterial communities and their functional profiles in water and sediments of the Apies River, South Africa, as a function of land use. Sci. Total Environ. 616–617, 326–334 (2018).PubMed 
ADS 

Google Scholar 
40.Guo, X. et al. Characteristics of microbial community indicate anthropogenic impact on the sediments along the Yangtze Estuary and its coastal area, China. Sci. Total Environ. 648, 306–314 (2019).CAS 
PubMed 
ADS 

Google Scholar 
41.Betiku, O. C. et al. Evaluation of microbial diversity of three recreational water bodies using 16S rRNA metagenomic approach. Sci. Total Environ. 771, 144773 (2021).CAS 
PubMed 
ADS 

Google Scholar 
42.Zhang, T. et al. Suspended particles phoD alkaline phosphatase gene diversity in large shallow eutrophic Lake Taihu. Sci. Total Environ. 728, 138615 (2020).CAS 
PubMed 
ADS 

Google Scholar 
43.Shen, M. et al. Trophic status is associated with community structure and metabolic potential of planktonic microbiota in Plateau Lakes. Front. Microbiol. 10, 1–15 (2019).
Google Scholar 
44.Quero, G. M., Cassin, D., Botter, M., Perini, L. & Luna, G. M. Patterns of benthic bacterial diversity in coastal areas contaminated by heavy metals, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs). Front. Microbiol. 6, 1–15 (2015).
Google Scholar 
45.Wang, Y. et al. Comparison of the levels of bacterial diversity in freshwater, intertidal wetland, and marine sediments by using millions of illumina tags. Appl. Environ. Microbiol. 78, 8264–8271 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
46.Long, Y. et al. The response of microbial community structure and sediment properties to anthropogenic activities in Caohai wetland sediments. Ecotoxicol. Environ. Saf. 211, 111936 (2021).CAS 
PubMed 

Google Scholar 
47.Yao, X., Zhang, J., Tian, L. & Guo, J. The effect of heavy metal contamination on the bacterial community structure at Jiaozhou Bay, China. Braz. J. Microbiol. 48, 71–78 (2017).CAS 
PubMed 

Google Scholar 
48.Hur, M. & Park, S. J. Identification of microbial profiles in heavy-metal-contaminated soil from full-length 16s rRNA reads sequenced by a pacbio system. Microorganisms 7, 25 (2019).
Google Scholar 
49.Zhuang, M., Sanganyado, E., Li, P. & Liu, W. Distribution of microbial communities in metal-contaminated nearshore sediment from Eastern Guangdong, China. Environ. Pollut. 250, 482–492 (2019).CAS 
PubMed 

Google Scholar 
50.Gu, Y. et al. Degradation shaped bacterial and archaeal communities with predictable taxa and their association patterns in Zoige wetland at Tibet plateau. Sci. Rep. 8, 1–11 (2018).ADS 

Google Scholar 
51.Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 25 (2011).
Google Scholar 
52.Hu, A. et al. Strong impact of anthropogenic contamination on the co-occurrence patterns of a riverine microbial community. Environ. Microbiol. 19, 4993–5009 (2017).CAS 
PubMed 

Google Scholar 
53.Ren, Z. et al. Taxonomic and functional differences between microbial communities in Qinghai Lake and its input streams. Front. Microbiol. 8, 1–14 (2017).
Google Scholar 
54.Yin, X. et al. Cadmium isotope constraints on heavy metal sources in a riverine system impacted by multiple anthropogenic activities. Sci. Total Environ. 750, 141233 (2021).CAS 
PubMed 
ADS 

Google Scholar 
55.Yan, C. et al. Integrating high-throughput sequencing and metagenome analysis to reveal the characteristic and resistance mechanism of microbial community in metal contaminated sediments. Sci. Total Environ. 707, 136116 (2020).CAS 
PubMed 
ADS 

Google Scholar 
56.Coclet, C. et al. Trace metal contamination impacts predicted functions more than structure of marine prokaryotic biofilm communities in an anthropized coastal area. Front. Microbiol. 12, 1–16 (2021).
Google Scholar 
57.Esri Inc. ArcMap 10.8. Esri Inc. (2020). https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview.58.Avalos, G. et al. Climate Change in the Mantaro River Basin (MINEN, 2013).
Google Scholar 
59.APHA. Standard methods for the examination of water and wastewater. Stand. Methods 541, 25 (2012).
Google Scholar 
60.Singh, H., Pandey, R., Singh, S. K. & Shukla, D. N. Assessment of heavy metal contamination in the sediment of the River Ghaghara, a major tributary of the River Ganga in Northern India. Appl. Water Sci. 7, 4133–4149 (2017).CAS 
ADS 

Google Scholar 
61.El-Amier, Y. A., Elnaggar, A. A. & El-Alfy, M. Evaluation and mapping spatial distribution of bottom sediment heavy metal contamination in Burullus Lake, Egypt. Egypt. J. Basic Appl. Sci. https://doi.org/10.1016/j.ejbas.2016.09.005 (2016).Article 

Google Scholar 
62.Miller, D. N., Bryant, J. E., Madsen, E. L. & Ghiorse, W. C. Evaluation and optimization of DNA extraction and purification procedures for soil and sediment samples. Appl. Environ. Microbiol. 65, 4715–4724 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
63.Custodio, M. et al. Metagenomic data on the composition of bacterial communities in lake environment sediments for fish farming by next generation Illumina sequencing. Data Br. 32, 106228 (2020).
Google Scholar 
64.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Wood, D. E. & Salzberg, S. L. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, 25 (2014).
Google Scholar 
66.Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
67.Gan, Y. et al. Multiple factors impact the contents of heavy metals in vegetables in high natural background area of China. Chemosphere 184, 1388–1395 (2017).CAS 
PubMed 
ADS 

Google Scholar 
68.Diallo, M. D. et al. Polymerase chain reaction denaturing gradient gel electrophoresis analysis of the N2-fixing bacterial diversity in soil under Acacia tortilis ssp. raddiana and Balanites aegyptiaca in the dryland part of Senegal. Environ. Microbiol. 6, 400–415 (2004).CAS 

Google Scholar 
69.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2020). https://www.R-project.org/.70.Li, C. et al. Effects of heavy metals on microbial communities in sediments and establishment of bioindicators based on microbial taxa and function for environmental monitoring and management. Sci. Total Environ. 749, 141555 (2020).CAS 
PubMed 
ADS 

Google Scholar 
71.Murtaza, N. et al. Analysis of the effects of dietary pattern on the oral microbiome of elite endurance athletes. Nutrients 11, 1–12 (2019).MathSciNet 

Google Scholar  More

Species dataSpecies CPUE data were obtained from the National Oceanographic and Atmospheric Administration (NOAA) Northeast Fishery Science Center (NEFSC) U.S. NES bottom trawl survey, which, for almost 50 years, has collected abundance data for over 250 fish species in the spring and fall. The survey employs a stratified random design, with stations allocated proportionally to the stratum area. A 12 mm mesh coded liner is used to retain small-bodied and juvenile fish. All fish caught are weighed and counted18. We downloaded the data from OceanAdapt.com, which calibrates the CPUE for each species depending on survey ship. We cleaned the data for the years from 1998 to 2020, excluding years prior to 1997 due to many missing values for chlorophyll (Chla). We only included strata that were consistently sampled in the spring and fall. To account for the seasonal migrations of many of the studied species, we modeled spring and fall seasons separately. We present the results for the fall CPUE, with the spring results and presence/absence results in the supplemental materials. We selected species that were present in at least 400 tows and with a biomass of at least 0.5 kg/tow (CPUE) in more than 100 tows. Finally, we removed roughly 400 tows per season with missing environmental covariates (see below). In the fall, we selected 30 species with 5217 observations, and in the spring, we selected 24 species with 5935 observations (see Supplemental Tables S1, S2).Environmental dataThe study region includes Southern New England and The Gulf of Maine. We selected environmental covariates known to influence marine fish distributions and abundances. Depth, temperature (bottom and surface) and salinity (bottom and surface) were measured in situ during trawl surveys. Missing values were augmented with the data-assimilative HYbrid Coordinate Ocean Model (HYCOM) daily and then monthly data. HYCOM is an oceanographic model that produces 32 vertical layers including ocean temperature, salinity, sea surface height, and wind stress as well as other 3- and 4-dimensional variables. The system uses the Navy Coupled Ocean Data Assimilation (NCODA) system19 for data assimilation. NCODA uses the model forecast as a first guess in a multivariate optimal interpolation (MVOI) scheme and assimilates available satellite altimeter observations (along track obtained via the Naval Oceanographic Office Altimeter Data Fusion Center satellite) and in situ sea surface temperature as well as available in situ vertical temperature and salinity profiles from expendable bathythermographs, Argo floats, and moored buoys20. Seven HYCOM models (HYCOM + NCODA Global 1/12° Reanalysis GLBu0.08 Expts 19.0, 19.1, 90.9, 91.0, 91.1, 91.2) were temporally concatenated to create a continuous dataset of BT and salinity, ranging from 1992 to 2017. These model runs differed slightly in their configurations (time steps, advection scheme, mixing, vertical structure, slight change in NCODA, and MVOI transition to 3-dimensional analysis in 2013), but the differences are not expected to influence the applicability of the output21. The numbers of filled in missing values were 787 (7.0%) surface salinity (SSAL), 735 (6.5%) surface temperature (SST), 809 (7.2%) bottom temperature (BT), and 850 (7.6%) bottom salinity (BSAL). Chla was obtained from the MODIS satellite (monthly rasters from 2003 to 2019) on a monthly time step22, with missing values filled using the SeaWIFS satellite23 (1998 to 2009). Temperature, salinity and Chla data that were not collected in situ were downloaded using Google Earth Engine (HYCOM and MODIS)24. Benthic substrate (grain size in mm, referred to as SEDSIZE), subregion (Gulf of Maine or Southern New England), benthic land position (high, low, mid), and seabed form data (depression, high flat, high slope, low slope, mid flat, side slope, steep) were obtained from the Nature Conservancy’s Northwest Atlantic Marine Ecoregional Assessment25 (Supplemental Fig. S1).GJAMTo study the influence of the environmental covariates on the joint distribution of marine fish and invertebrate species we use the generalized joint attribute model (GJAM)12 and the corresponding R package (Version 2.5)26. Briefly, this multivariate Bayesian model allows us to jointly model the marine fish community and accounts for responses to the environment that can include combinations of continuous and discrete responses (e.g., CPUE and zeros) and the dependence between species. GJAM returns all parameters on the observation scale, in this case, CPUE and presence-absence. Products of model fitting include a species‐by‐species covariance matrix (Σ), species responses to predictor variables (B), and predicted responses. The species‐by‐species covariance matrix Σ captures residual codependence between species after removing the main structure explained by the model (also referred to as the residual correlation matrix). As a result, Σ allows for conditional prediction of some species under different scenarios for the abundances of others27.CPUE is termed continuous abundance (CA) data in GJAM, meaning that observations are continuous with discrete zeros. Let yis be the CPUE for species s at location i. For CA data GJAM expands the tobit model for (univariate) regression to the multivariate setting, where a latent variable wis is equal to yis when yis is positive and zero otherwise,$$y_{i,s}^{0} = left{ {begin{array}{*{20}l} {w_{is} ,} hfill & {w_{is} > 0quad {text{continuous}}} hfill \ {0,} hfill & {w_{is} le 0quad {text{discrete zero}}} hfill \ end{array} } right.$$
(1)
The length-S vector of all species responses wi is continuous on the real line, and thus can be modeled with a multivariate normal. The model for wi is$$begin{aligned} left. {{mathbf{w}}_{i} } right|{mathbf{x}}_{i, } {mathbf{y}}_{i} & sim ,MVNleft( {{varvec{mu}}_{i} ,{Sigma }} right) times mathop prod limits_{s = 1}^{S} {mathcal{I}}_{is} \ u_{{varvec{i}}} & = {mathbf{B}}^{prime } {mathbf{x}}_{{varvec{i}}} \ {mathcal{I}}_{is} & = mathop prod limits_{k in C} I_{is,k}^{{Ileft( {y_{is} = k} right)}} left( {1 – I_{is,k} } right)^{{Ileft( {y_{is} ne k} right)}} \ end{aligned}$$
(2)
$$begin{aligned} {mathcal{I}}_{is} & = I(w_{is} le 0)^{{Ileft( {y_{is} = 0} right)}} left[ {1 – Ileft( {w_{is} le 0} right)} right]^{{Ileft( {y_{is} > 0} right)}} \ & quad I(w_{is} > 0)^{{Ileft( {y_{is} > 0} right)}} left[ {(1 – I(w_{is} > 0)} right]^{{Ileft( {y_{is} = 0} right)}} \ end{aligned}$$where the indicator function (I(cdot )) is equal to 1 when its argument is true and 0 otherwise. For presence-absence data, ({mathbf{p}}_{{varvec{i}}{varvec{s}}}boldsymbol{ }=boldsymbol{ }left(-boldsymbol{infty },boldsymbol{ }0,boldsymbol{ }boldsymbol{infty }right).) This is equivalent to Chib and Greenberg’s28 probit model which can be written as ({mathcal{I}}_{is}=I({w}_{is} >{0)}^{Ileft({y}_{is} >0right)}I({w}_{is}le {0)}^{1-{y}_{is}}).The mean vector ({varvec{mu}}_{i} = {mathbf{B}}^{prime } {mathbf{x}}_{{varvec{i}}}) contains the Q × S matrix of coefficients B and the length-Q design vector xi. Σ is a S × S covariance matrix. There is a correlation matrix associated with Σ,$${mathbf{R}}_{{S,S^{prime } }} = frac{{{{varvec{Sigma}}}_{{S,S^{prime } }} }}{{sqrt {{{varvec{Sigma}}}_{S,S} {{varvec{Sigma}}}_{{S^{prime } ,S^{prime } }} } }}$$
(3)
The predictive distribution is obtained as$$left[tilde{Y }left| tilde{X }right.right]=int left[ tilde{Y }left| tilde{X }right.,widehat{theta }right]left[widehat{theta } left|X, Yright.right]$$
(4)
The integrand contains the likelihood (Eq. (2)) followed by the posterior distribution for parameters, (widehat{theta }= left{widehat{mathbf{B}},boldsymbol{ }widehat{{varvec{Sigma}}}right}). Input (tilde{X }) can equal X (in-sample prediction) or not (out-of-sample prediction). We fitted both CPUE (continuous abundance) and presence-absence versions of the model. As a Bayesian method, GJAM provides probabilistic estimates of parameters with full dependence in data, including jointly distributed species. Model fitting is performed using Gibbs sampling, which is a Markov chain Monte Carlo (MCMC) technique.The sensitivity of an individual response variable s to an individual predictor q is given by the coefficient βqs (individual coefficients from the B matrix). The sensitivity that applies to the full response matrix is given by$${mathbf{f}} = diagleft( {{mathbf{B}}{Sigma }^{ – 1} {mathbf{B}}^{prime } } right)$$
(5)
The Q × S matrix B contains relationships of each species to the environment, the “signal”, but not to each another. Matrix E summarizes species similarities in terms of their response to an environment (stackrel{sim }{mathbf{x}}) and is given by$${mathbf{E}=mathbf{B}}^{boldsymbol{^{prime}}}mathbf{V}mathbf{B}$$
(6)
where V is a covariance matrix for (stackrel{sim }{mathbf{x}})(a vector of predictors) and contributes the environmental component of variation in (stackrel{sim }{mathbf{y}}). Similar species in E have similar columns in B. Those similarities and differences are amplified for predictors (stackrel{sim }{mathbf{x}}) with large variance. Conversely, species differences in B do not matter for variables in X that do not vary. The covariance in predictors could come from observed data, i.e., the variance of X (see12 for more details).Prior distributions for this study are non-informative. This is particularly helpful for the covariance, lending stability to Gibbs sampling and avoiding dominance by a prior. In cases this particular case, the direction of the prior effect may be known, but the magnitude is not.Variable selectionUnlike the familiar univariate setting, variable selection has to consider which species are included in the model. In a univariate model, there is one response and perhaps a number of potential predictor variables from which to choose. As in a univariate model, variable selection focuses on predictors held in the n by p design matrix X. Rather than a response vector, the multivariate model includes the n by S response matrix Y. Unlike the univariate model, the overall fit and predictive capacity depends not only on what is in X, but also on the species that are included in Y, each of which would be best explained by a different combination of variables. Rare species having no signal will not provide cross-correlations and thus can offer little learning from an analysis. For this reason, there may be no reason to include them in model fitting. Given that many species may be rare, and rare types will not be explained by the model, there will be decisions about what variables to include on both sides of the likelihood (i.e., predictors and responses).These considerations mean that simple rules for variable selection, such as the combination yielding the lowest DIC, may not be sensible. The combination of variables that yields the lowest DIC could miss variables that are important for subsets of species. In principle, one poorly-fitted species could dominate variable selection. The best model for responses ranging from rare to abundant will depend on precisely which species are included, both rare and abundant. Thus, in order to select variables, we utilize inverse prediction—predicting the environment from species – and the overall community sensitivity12.Inverse prediction provides a comprehensive estimate of the environmental importance for the entire community, because it determines the capacity of the community to predict (through the fitted model) the environment; it inverts the model12. A variable predicted by the community explains important variation in one to many species. A variable that is not predicted by the community does not explain important variation in any of them. To look at the importance of environmental variables for the entire community, we started with the saturated model that included the predictors BT, SST, depth, BSAL, SSAL, Chla, SEDSIZE, subregion, benthic position and an interaction between depth and BT, BSAL, SST and SSAL (Fig. 1a). Sensitivity was highest for the interaction between BT and depth and lowest for Chla and sediment size (see right subpanel on Fig. 1a for sensitivity). Inverse prediction confirmed that sediment size and Chla contribute little to community biomass, because the community cannot “predict” them (see left and middle subpanels on Fig. 1a for sensitivity). Inverse prediction results from a second model (Fig. 1b) showed that SSAL and the third model for benthic position also (Fig. 1c) contribute little to the community response. Using the combination of sensitivity and inverse prediction we obtained the final model that includes BT, depth, BSAL, SST, subregion and an interaction between depth and BT, BSAL and SST (Fig. 1d). Inverse prediction indicates that the CPUE predicts the environment well. In the final model, sensitivity is highest for depth. Subregion remains as a two-level factor and there is strong inverse prediction for that variable as well (Fig. 1d). In the variable-selection stage, each model was run on the entire fall dataset for 5000 iterations and a burn-in of 800. Inverse prediction results from the spring model indicated similar patterns; thus, the same variables were used for the spring and fall.Figure 1Inverse prediction and sensitivity for combinations of environmental parameters in GJAM. Starting with the most complicated model (a), sensitivity was highest for the interaction between BT and depth and lowest for Chla and sediment size (a). Inverse prediction confirms that sediment size and Chla contribute little to community biomass (a) and those are removed in the second model (b). SSAL contributes little to community response and are removed in the third model (c), The final model (d) includes terms that have strong inverse prediction and overall sensitivity. Inverse prediction for continuous and factor variables is on the left and center of each box, and overall sensitivity is on the right.Full size imageWe compare the model selected above using inverse prediction to a model selected using the more traditional method of out-of-sample prediction. For out-of-sample prediction, we fitted all combinations of 11 environmental variables (BT, BSAL, SST, SSAL, Chla, depth, sediment size, subregion, position, seabed form) plus interaction terms between depth and SEDSIZE, BT, BSAL, SST, SSAL and chlorophyll. These models were run with 1000 iterations and a burn-in of 400. All models included BT, BSAL, SST, SSAL, chlorophyll A and depth, as these variables have been shown to be important for these species. In total, 1,024 possible models were evaluated by training each potential model on 70% of the data (n = 3652 in the fall, n = 4155 in the spring), evaluating in-sample performance with DIC, and then testing out-of-sample performance on the remaining 30% (n = 1565 in the fall, n = 1780 in the spring). The 10 models with the lowest DIC in-sample were selected, and the final model was selected out of those 10 with the lowest out-of-sample R2. The selected model for fall CPUE had the following terms: ~ BT + depth + BSAL + SST + SSAL + chla + depth*BT + depth*SEDSIZE + depth*SSAL + depth*chla + SEDSIZE + Benthic position. Recall that inverse prediction selected a simpler model including the following terms: BT + depth + BSAL + SST + Subregion + depth*BT + depth*BSAL + depth*SST. The inclusion of SEDSIZE and benthic position in the model selected via out-of-sample prediction is probably a result of these predictor variables being important for a subset of species (i.e. benthic species29), but not the community as a whole. When we have a large number of response variables, as in this study, we need to consider the variables that are more important on a community level, rather than just for a few species. Thus, we use the model selected via inverse prediction for the remainder of the study.We fitted the selected model with 70% of the data for 20,000 iterations with a burn-in of 8,000 iterations (n = 3652 in the fall, n = 4155 in the spring). Out-of-sample prediction was performed on the remaining 30% (n = 1565 in the fall, n = 1780 in the spring) of the dataset and predicted versus observed values were evaluated (Supplemental Figs. S2 and S3) as well as residual versus fitted values (Supplemental Figs. S4 and S5). As has been shown in other research30,31, aggregating noisy predictions based on similar environmental preferences can improve performance, especially for larger datasets. Thus, we generated an aggregated data set that uses a k-means clustering of predictors (Supplemental Figs. S8 and S9). We performed the same analysis for the spring and the fall as well as with the presence absence data and recorded AUC as well as precision for each species (Supplemental Figs. S6 and S7). Precision is defined as the arithmetic mean of precision (proportion of predicted presences actually observed as presences) across all threshold values (at an interval of 0.01).Final modelWe ran the final model on 100% of the data with 20,000 iterations and a burn-in of 8000 iterations for the spring and fall for CPUE as well as presence absence for a total of 4 models. From the final model we obtained coefficients for the species-environment responses, β, covariance between species in how they respond to the environment E, and the residual correlation from the fitted model, R. We subtracted the absolute values from the presence/absence residual correlation matrix from the absolute values of the CPUE residual correlation matrix to observe where these results diverged. For MCMC chains and convergence of the final model as well as example models from both methods of variable selection see Supplemental Figs. S10–S12).Comparison to SSDMsWe built single species distribution models for each species in the form of GAMs using the mgcv package in R32. GAMs are a semiparametric extension of the generalized linear model and are a common modeling technique for species distribution modeling in this ecosystem33. For each species, we ran one GAM with CPUE as the response variable with a log-linked tweedie distribution that had penalized regression splines, a REML smoothing parameter with an outer Newton optimizer, 10 knots, and omitted NAs. We also ran GAMs for each species with a binary response variable indicating species presence with a binomial error distribution and a logit link function, penalized regression splines, a REML smoothing parameter with an outer Newton optimizer, 10 knots, and omitted NAs. We compared the out of sample observed versus predicted values for GAMs versus GJAM using RMSPE, R2, AUC, and precision. Root Mean Squared Prediction Error (RMSPE) is a measure of the average squared difference between the observed and predicted values, measured in the same units as the input data (kg/tow). R2 is a measure of the average squared difference between the observed and predicted values and is unitless. R2 is calculated as (1 − sum((predicted − observed)2)/sum((observed − mean(observed))2)) The ROC curve is a measure of model performance which plots true positive rate versus false positive rate, and the area under the ROC curve (AUC) provides a single measure of accuracy. A pairwise Wilcoxon test was used to compare means. We also compare the significance of predictors in both the GJAM model and GAM models. In this example, significance is defined for GJAM as a credible interval of the beta estimation that does not cross zero, and for the GAM as a p-value less than 0.0534.Spatial and temporal autocorrelationExamining the spatial and temporal autocorrelation of the modeled residuals can help specify missing endogenous (habitat selection or density dependence) and exogenous (covariate) effects that may be missing from the model. Thus, for each species modeled, we plot the spatial autocorrelation of residuals using a semi-variogram for the year 2015 and the temporal autocorrelation of the residuals using a partial autocorrelation function (PACF). We present the results for each species in the fall in the Supplemental materials (Supplemental Figs. S27–S57).All analysis and figure creation was performed in R version 3.6.235. Figures were created using the following R packages: ggplot236, ggpubr37, corrplot38, gridExtra39, cowplot40, lessR41, and ggcorrplot42. More