Journal articles on the topic 'Population density total hardness relative abundance correlation'

In the present study efforts have been made to ascertain the seasonal abundance and population dynamics of zooplankton community and its relationship with physico-chemical factors of the water bodies of Santragachi Jheel in the District Howrah, W.B., INDIA. The zooplankton abundance showed distinct seasonal or temporal variation in the Jheels. A total of 18 species belonging to 12 families and 15 genera were recorded. The population density of total zooplankton showed summer maxima in the Jheel. Maximum abundance of rotifer fauna was recorded during summer season in sewage sites whereas Cladocera population showed maximum density at non-sewage sites. Copepods showed postmonsoon maxima in sewage sites in the Jheels. Some species i.e., Polyarthara vulgaris were quite abundant in the polluted water of the Santragachi Jheel. Out of 15 genera of Zooplankton, Brachionus, Keratella, Polyarthra, Diaphanosoma, Ceriodaphnia, Bosmina, Heliodiaptomus and Mesocyclops were found to be dominant in Santragachi Jheel. Highest percentage of similarity was recorded between S1 and S3 sites of the Jheel. Results of ANOVA (2 - way) indicated that zooplankton population differs significantly in relation to stations and seasons of the Jheel. Population of Rotifera, the most dominant group was found to be positively influenced by Biological Oxygen demand (BOD), Free Carbon-di-oxide FCO2), CaCO3 hardness (CaHa), Total suspended solid (TSS), Chloride(Cl) and Phosphate (PO4) in the Jheels. Total Cladocera showed positive correlation with PO4 in the Jheel. The Copepods expressed positive correlation with BOD and ammonia. Increment of population density of several zooplankton organisms (i.e., Keratella tropica, Polyarthra vulgaris) and low value of community indices especially species diversity and species richness indicated the rise of pollutional stress on the Santragachi Jheel. Keywords: Zooplankton; Rotifera; BOD; COD; Shannon - Wiener index; Evenness index DOI: http://dx.doi.org/10.3126/jowe.v5i0.4595 J Wet Eco 2011 (5): 20-39

Aim: Characterization of physico-chemical parameters of Aedes mosquito breeding habitats from dengue-endemic Siliguri sub-divisional area and correlation of larval density with the habitat characteristics. Methodology: Natural and artificial water-holding containers found in four blocks, namely- Naxalbari, Matigara, Khoribari and Phansidewa of Siliguri sub-divisional area were surveyed for the collection of Aedes mosquito larvae. Water parameters such as pH, temperature, electric conductance (EC), total dissolved solid (TDS), chloride, salinity, total hardness (TH) and dissolved oxygen (DO) were measured and the larval density (LD) indices and relative abundance for Aedes spp., were calculated. Results: Out of 40 habitats, larvae of Aedes spp. were found in 20 habitats (50%). Ae. albopictus was found as the pre-dominant species (81.24%) in all the positive breeding habitats. Three habitat characteristics, like chloride (r= -0.959; p= 0.041), salinity (r= -0.958; p= 0.041)and TH (r= -0.961; p= 0.039) showed significant negative association with larval densityin the study. Whereas the remaining parameters, like-pH, temperature, EC, TDS, were positively correlated and DO showed a negative correlation with larval density. Although none of the later correlations were statistically significant. The larval density of Aedes spp. (mean ± SD) was lowest in Khoribari (27.66 ± 21.55) and highest in Phansidewa block (128.33 ± 187.26). Interpretation: Pre-dominance of Ae. albopictus reiterates its importance as a principal dengue vectorin rural, peri- or sub-urban area of the sub-division. Utilizing the obtained data, the efficacy of oviposition traps can be enhanced for locale-specific vector surveillance and development of novel techniques that may deter larval growth, development and survival in an area. Key words: Aedes mosquitoes, Dengue, Larval density, Mosquito-borne viral diseases, Siliguri

The recent black-footed ferret reintroduction into the Conata Basin area of west­central South Dakota has prompted managers of USDA Forest Service, Buffalo Gap National Grasslands and National Park Service, Badlands National Park, to reassess methods of determining population size of black-tailed prairie dogs Cynomys ludovicianus. Most agencies are currently relying on a protocol developed by Biggins et al. (1993) to assess black-footed ferret habitat, a section of which deals with prey abundance. The protocol is based on population estimates derived from counting the number of active burrows. The justification for this was a set of unpublished data that reported fair and good relationships between counts of active burrows and black- and white-tailed prairie dogs C. leucurus, respectively (Biggins et al. 1993). While there is no other correlative information relative to active burrows, Powell et al. (1994) suggested that counts of active burrows alone may not be a reliable indicator of black-tailed prairie dog populations. Menkens et al. (1988) examined relationships between populations determined by mark-recapture and total burrow counts. They reported that white­tailed prairie dog density was not significantly related to burrow density and was not a useful predictor of population density. However, Fagerstone and Biggins (1986) and Menkens et al. (1990) reported high correlation coefficients when comparing visual counts of white-tailed prairie dogs with mark-recapture densities. The purpose of this study was to examine relationships among population estimates from mark-recapture techniques with visual counts, active burrow counts, and total burrow counts derived by ground and aerial surveys all within the same experimental design.

The present study was conducted to determine the structure of benthic invertebrates community, as well as a study of some factors associated with water quality in Shatt Al-Kufa. The study was included a choice of three sites located along the Shatt Al-Kufa River, water samples and benthic invertebrates were collected during the period from February 2014 to January 2015.
 The abiotic study included measurements of chlorophyll a, salinity, total dissolved solids, biochemical oxygen demand, total hardness, nitrate, and sulfate. The biotic study included the determination the composition of the benthic invertebrates community through the study of the mean population density, the relative abundance index of these organisms and the Jaccard Coefficient was calculated to identify the value of similarity between the studied sites. In the present study, 28 taxa of benthic invertebrates were recorded belong to 4 main groups which are: 8 taxa belonged to Annelida, 7 belonged to Insecta, 10 belonged to Mollusca, 3 belonged to Nematoda. Annelida recorded the highest percentage 40.8% of the total number of benthic invertebrates, Insecta with 30.3%, Mollusca and Nematoda with 28.4 %, 0.5% respectively. Benthic invertebrate has shown positive and negative relationships with the studied physical and chemical characteristics.

Abstract Coffee berry borer, Hypothenemus hampei Ferrari (Coleoptera: Curculionidae: Scolytinae), is the most damaging insect pest of coffee worldwide. Old coffee berries (raisins) are widely acknowledged as coffee berry borer reservoirs, yet few studies have attempted to quantify coffee berry borer populations in raisins remaining on farms postharvest. We collected ground and tree raisins at six coffee farms on Hawai’i Island to assess raisin density, infestation, coffee berry borer abundance, and adult mortality in three areas of each farm: trees, driplines (ground below the tree foliage), and center aisles (ground between tree rows). We also assessed infestation of the new season’s crop by conducting whole-tree counts of infested green berries. Mean raisin density was significantly higher in the dripline compared to the center aisle and trees (131 vs 17 raisins per m2 and 12 raisins per tree, respectively). Raisin infestation was significantly higher in samples from trees (70%) relative to those from the dripline (22%) and center aisle (18%). Tree raisins had significantly higher coffee berry borer abundance compared to both areas of the ground (20 vs 3–5 coffee berry borer per raisin). Adult mortality was significantly higher on the ground (63–71%) compared to the trees (12%). We also observed a significant positive correlation between ground raisin density and infestation of the new season’s crop. Across all farms, we estimated that 49.5% of the total coffee berry borer load was present in dripline raisins, 47.3% in tree raisins, and 3.2% in center aisle raisins. Our findings confirm the importance of whole-farm sanitation in coffee berry borer management by demonstrating the negative impact that poor postharvest control can have on the following season’s crop.

Faba beans (Vicia faba L.) face threats from pests like the black bean aphid (Aphis fabae S.). By understanding the intricate interactions between environmental factors and pest dynamics, we aim to enhance pest management practices in leguminous crop production for improved efficiency and sustainability. A field experiment spanning three growing seasons (2021–2023) explored the link between meteorological parameters and A. fabae abundance in V. faba. Weekly field inspections documented aphid levels alongside concurrent meteorological data. Correlation and multiple linear regression were used to evaluate these relationships. Aphid infestation varied annually, appearing in 2021 and 2023 but not in 2022. Peak density aligned with specific growth stages, indicating temporal variability. In 2023, a significant surge of 1157.4% to 2126.0% compared to 2021 levels highlighted population dynamics in response to environmental factors. Negative correlations with total rainfall were consistent in both years, while positive correlations with maximum temperature and relative humidity were observed. Multiple linear regression attributed 67.1% to 99.9% of aphid abundance variance to the meteorological parameters, emphasizing their role in predicting aphid populations. Our study sheds light on the complex relationship between meteorological parameters and A. fabae dynamics, offering valuable insights into factors impacting aphid abundance in V. faba.

Abstract The number density and correlation function of galaxies are two key quantities to characterize the distribution of the observed galaxy population. High-z spectroscopic surveys, which usually involve complex target selection and are incomplete in redshift sampling, present both opportunities and challenges to measure these quantities reliably in the high-z Universe. Using realistic mock catalogs, we show that target selection and redshift incompleteness can lead to significantly biased results, especially due to the flux-limit selection criteria. We develop a new method to correct the flux-limit effect, using information provided by the parent photometric data from which the spectroscopic sample is constructed. Our tests using realistic mock samples show that the method is able to reproduce the true stellar mass function and correlation function reliably. Mock catalogs are constructed for the existing zCOSMOS and VIPERS surveys, as well as for the forthcoming Prime Focus Spectrograph (PFS) galaxy evolution survey. The same set of mock samples are used to quantify the total variance expected for different sample sizes. We find that the total variance decreases very slowly when the survey area reaches about 4 deg2 for the abundance and about 8 deg2 for the clustering, indicating that the cosmic variance is no longer the dominant source of error for PFS-like surveys. We also quantify the improvements expected in the PFS-like galaxy survey relative to zCOSMOS and VIPERS surveys.

(1) Aims: This study was aimed to investigate the effects of organic and inorganic fertilizer application on the soil nutrients and microbiota in tea garden soil. (2) Method: Illumina Hiseq sequencing technique was conducted to analyze the microbial diversity and density in different fertilizer-applied tea garden soil. (3) Results: The results showed that Acidobacteria, Proteobacteria and Actinobacteria were the predominant bacterial species observed in the tea garden soil. Besides, the relative abundance of Basidiomycota, Ascomycota and Zygomycota fungal species were higher in the tea garden soil. Correlation analysis revealed that Acidibacter and Acidothermus were significantly correlated with chemical properties (such as total organic carbon (TOC), total phosphorus (TP) and available phosphorus (AP) contents) of the tea garden soil. Furthermore, all these microbes were abundant in medium rapeseed cake (MRSC) + green manure (GM) treated tea garden soil. (4) Conclusion: Based on the obtained results, we conclude that the application of MRSC + GM could be a preferred fertilizer to increase the soil nutrients (TOC, TP and AP content) and microbial population in the tea garden soil.

AbstractChanges in the soil nematofauna community structure were followed in nine millet fields on seven farms in two villages in Senegal during one cropping cycle. Cultivation practices were done by field owners. One plot in each field was divided into two subplots; in one of these, manure (20 t ha-1) was added at sowing. Before the manure input, at mid-cycle and at millet harvest, the structure of the nematode fauna was studied. Soil physico-chemical characteristics, microbial carbon and plant production were measured at sowing and at millet harvest. In the sub-plots where manure was added, millet yield increased by 155%, the mineral nitrogen content of the soil increased by about 45%, while nitrogen flux increased by 150% and microbial biomass by 65%. The significant enrichment of soil by manure led to a 75% increase in total nematode population density at mid-cycle and to a 30% increase at harvest time. The density of opportunistic bacterial-feeding and fungal-feeding nematodes was significantly larger with than without manure. This result is similar to those of comparable studies in temperate areas; however the relative abundance of enrichment opportunists was extremely low with regard to that found under similar conditions in temperate ecosystems. Furthermore, the abundance of the c-p 2 bacterialfeeding nematodes, belonging mainly to the family Cephalobidae, was strongly correlated with soil microbial biomass. The other c-p feeding guilds showed no correlation with nitrogen flux, or soil microbial biomass.

This research examined the influence of interspecific interactions among shrew species on their territorial distribution and trophic conditions within larch and poplar-chosenia forests along the upper Kolyma River. The study specifically analyzed the biotopic characteristics associated with the relative abundance and energy reserve content, including fat tissue mass and glycogen levels in the liver, of Sorex caecutiens and S. isodon. Fieldwork was conducted in the Seymchan- Buyunda depression during July and August in 2003 and 2010. A total of 1,588 individuals from both species were captured using pitfall traps. The assessment of energy reserve content in S. caecutiens (n = 736) and S. isodon (n = 113) was carried out between 2006 and 2010. The abundance of S. caecutiens in the examined habitats exhibited consistent trends and a strong correlation (RS = 0.95, p < 0.01). Conversely, S. isodon displayed asynchronous fluctuations in abundance across various habitats, likely attributable to competition with Laxmann’s shrew, which tends to displace S. isodon from its preferred larch forest during periods of elevated population density. The interannual variations in energy reserve content for both shrew species were remarkably similar, with no significant differences observed between habitats. The nature of these variations suggested insufficient feeding conditions for the animals during years of high overall abundance, with the dominant species, S. caecutiens, contributing most significantly to this phenomenon in both habitats of the upper Kolyma. The results indicate that interspecific relationships play a crucial role in shaping both territorial distribution and food availability for these shrew species.

Macrophytes taxa composition determines microinvertebrates utilized as environmental indicators in freshwater ecosystems. This study was conducted in Shengjin Lake. In this lake, local communities have been practicing using sine fishing nets for fishing and this has a disrupting effect on macrophyte vegetation, even though it was the major for the disappearance of submerged vegetation before it was banned. As a result of this sine fishing net ban by the local authorities, the vegetation that had disappeared began to recover. Thus, this study investigated the role of architecturally differentiated macrophytes restoration effect on zooplankton communities’ diversity, abundance, and species composition; open water was used as a control. For this, the data were collected from different habitats via site 1 (open water) site 2, (free-floating), site 3 (emergent and submerged), site 4 (submerged), and site 5 (emergent) macrophytes. In the present study, the results demonstrated that the relative mean density of Rotifer was measured high which ranged from (219 ± 141–678 ± 401 ind L−1), mainly dominated by Keratella cochlearis and Lecane cornuta species. Following Rotifera, Cladocera population density was reported high and ranged within (36 ± 6.2–262.5 ± 49.4 ind L−1). The Cladocera group was dominated by Daphnia spp., Moina micura, Ceriodaphnia reticulata, and Chydorus latus species. Compared to Rotifer and Cladocera, Copepod community were recoded least with relative mean density ranged within (11.52 ± 2.22–85.5 ± 27 ind L−1) and dominated by Microcyclops javanus, Thermodiaptomus galebi, and Sinocalanus doerrii species. From environmental variables and the zooplankton density relationship analyzed, the redundancy analysis (RDA) results indicated that Water Temperature, Chlorophyll a, Dissolved Oxygen, Total Phosphorus, and Ammonium Nitrogen were found the most influential variables on zooplankton communities. Stepwise regression correlation showed that Copepod and Cladocera were found more dependent on environmental factors. For instance, Nitrate Nitrogen was negatively correlated with Cladocera, Copepod, and total zooplankton biomass but positively with Cladocera diversity. Water Temperature showed a positive relationship with Rotifer diversity; however, both Chlorophyll a and Electrical Conductivity were correlated positively with Cladocera biomass. Species diversity by the Shannon–Wiener index (H) illustrated a dynamic trend among the monitored sites which ranged between (0.65–4.25). From the three groups of zooplankton communities in contrast to Cladocera and Copepod, Rotifer species obtained more diversity across the studied sites. The Cladocera diversity (H′) index indicated a similar tendency in all sites. However, more Copepod diversity (H′) was observed in site 4. In conclusion, this study results can provide valuable insights into the health and dynamics of the aquatic ecosystem to understand factors deriving ecological imbalance and develop an integrated approach for effective strategies for management and conservation.

Abstract. Ice-nucleating particles (INPs) are an essential class of aerosols found worldwide that have far-reaching but poorly quantified climate feedback mechanisms through interaction with clouds and impacts on precipitation. These particles can have highly variable physicochemical properties in the atmosphere, and it is crucial to continuously monitor their long-term concentration relative to total ambient aerosol populations at a wide variety of sites to comprehensively understand aerosol–cloud interactions in the atmosphere. Hence, our study applied an in situ forced expansion cooling device to measure ambient INP concentrations and test its automated continuous measurements at atmospheric observatories, where complementary aerosol instruments are heavily equipped. Using collocated aerosol size, number, and composition measurements from these sites, we analyzed the correlation between sources and abundance of INPs in different environments. Toward this aim, we have measured ground-level INP concentrations at two contrasting sites, one in the Southern Great Plains (SGP) region of the United States with a substantial terrestrially influenced aerosol population and one in the Eastern North Atlantic Ocean (ENA) region with a primarily marine-influenced aerosol population. These measurements examined INPs mainly formed through immersion freezing and were performed at a ≤ 12 min resolution and with a wide range of heterogeneous freezing temperatures (Ts above −31 °C) for at least 45 d at each site. The associated INP data analysis was conducted in a consistent manner. We also explored the additional offline characterization of ambient aerosol particle samples from both locations in comparison to in situ data. From our ENA data, on average, INP abundance ranges from ≈ 1 to ≈ 20 L−1 (−30 °C ≤ T ≤ −20 °C) during October–November 2020. Backward air mass trajectories reveal a strong marine influence at ENA with 75.7 % of air masses originating over the Atlantic Ocean and 96.6 % of air masses traveling over open water, but analysis of particle chemistry suggests an additional INP source besides maritime aerosols (e.g., sea spray aerosols) at ENA. In contrast, 90.8 % of air masses at the SGP location originated from the North American continent, and 96.1 % of the time, these air masses traveled over land. As a result, organic-rich SGP aerosols from terrestrial sources exhibited notably high INP abundance from ≈ 1 to ≈ 100 L−1 (−30 °C ≤ T ≤ −15 °C) during October–November 2019. The probability density function of aerosol surface area-scaled immersion freezing efficiency (ice nucleation active surface site density; ns) was assessed for selected freezing temperatures. While the INP concentrations measured at SGP are higher than those of ENA, the ns(T) values of SGP (≈ 105 to ≈ 107 m−2 for −30 °C ≤ T ≤ −15 °C) are reciprocally lower than ENA for approximately 2 orders of magnitude (≈ 107 to ≈ 109 m−2 for −30 °C ≤ T ≤ −15 °C). The observed difference in ns(T) mainly stems from varied available aerosol surface areas, Saer, from two sites (Saer,SGP > Saer,ENA). INP parameterizations were developed as a function of examined freezing temperatures from SGP and ENA for our study periods.

<strong>ABSTRACT</strong> Tree community is the structural and functional basis of forest ecosystems. Forest ecosystem on hills is influenced by elevation due to variation in temperature, aspect and topographic features. Can the understanding of tree species occurrence guided by altitude help in finding the distributional pattern in different elevational bands? Gandhamardan hills belong to Eastern Ghats in Bargarh and Balangir districts of Odisha, India (20&deg;53<strong>&#39;</strong>29.7<strong>&#39;&#39;</strong>N, 82&deg;49<strong>&#39;</strong>57.8<strong>&#39;&#39;</strong>E). One hundred quadrates of 20m&times;20m size were laid during the year 2008 to study tree community&nbsp; with trees &ge; 15cm GBH in the 100ha protected forest. Relative frequency, relative density and relative abundance of tree species were calculated and summed up to get importance value index (IVI). Abundance to Frequency (A/F) ratio of each species was determined to get distribution pattern as regular (&lt;0.025), random (0.025-0.050) and contiguous (&gt;0.050). Dominance-Diversity (D-D) curves were plotted taking species rank on abscissa axis and IVI value on ordinate axis for the determination of species correlationship. Spearman&rsquo;s rank correlation (<em>&rho;</em><em>)</em> of IVI to relative frequency, relative density and relative abundance were calculated using Spearman&rsquo;s Rank formula. A total of 49 species belonging to 42 genera and 29 families were recorded throughout ten elevational bands within 300m to 550m.&nbsp; Species occurring at only single altitude range are <em>Cochlospermum religiosum</em> (L.) Alston (425-450m), <em>Dalbergia latifolia </em>Roxb.(400-425m), <em>Diospyros montana </em>Roxb. (500-525m),<em> Ficus benghalensis </em>L.(500-525m), <em>Garuga pinnata </em>Roxb. (350-375m), <em>Morinda pubescens </em>Sm. in Rees (425-450m), <em>Wrightia arborea </em>(Dennst) Mabb. (400-425m) and <em>Ziziphus mauritiana </em>Lam. (450-475m). All these species show contiguous type of distribution. Five species viz. <em>Buchanania lanzan </em>Spreng., <em>Cleistanthus collinus </em>(Roxb.) Benth. Ex Planch., <em>Diospyros melanoxylon </em>Roxb., <em>Terminalia alata </em>Heyne ex Roth and <em>Haldinia cordifolia</em> (Roxb.) Ridsdak were found in all the studied altitude bands. Out of 272 occurrences of species across all altitude bands, 136 occurrences of species are contiguous distribution type while the rest 136 occurrences are of regular (48 numbers) and random (88 numbers) distribution type. Random and contiguous distribution increase from lower altitude to mid altitude and again decrease from mid to higher altitudes whereas the opposite trend is observed for regular distribution. In the mid altitude band (400-425m) highest thirty eight species are observed. The Spearman&rsquo;s rank correlation value (<em>&rho;</em><em>)</em> shows that IVI is highly correlated with RD (<em>&rho;</em> =0.90 to 0.98) compared to that of RF (<em>&rho;</em> =0.66 to 0.85) and RA (<em>&rho;</em> =0.68 to 0.92). The theory of mid-domain effect with hard boundary concept for plant species distribution along altitude appears to be valid for Gandhamardan hill ecosystem. <strong>Key words :</strong> Eastern Ghats; Gandhamardan hill; Tree species; Dominance-Diversity; Mid-domain effect <strong>REFERENCES</strong> Andel, T.V. (2001). Floristic composition and diversity of mixed primary and secondary forests in northwest Guyana. <em>Biodiversity and conservation</em>.10:1645-1682. Armesto, J.J., Mitchell, J.D. and Villagran, C. (1986). A comparision of spatial pattern of trees in some tropical and temperate forests. <em>Biotropica </em>18: 1-11. Aubert, M., Alard, D. and Bureau, F. (2003). Diversity of plant assemblages in managed temperate forests: a case study in Normandy (France)<em> Forest Ecology and</em> <em>Management</em>. 175:321-337. Bhattarai, K.R. and Vetaas, O.R. (2005).&nbsp; Do fern and fern-allies show a similar response along the ecological gradient in the Himalayas? <em>Bulletin Department of Plant Resources</em>. 26:24-29. Bhattarai, K.R. and Vetaas, O.L. (2003). Variation in plant species richness of different life forms along a subtropical elevation gradient in the Himalayas, east Nepal. <em>Global Ecology and Biogeography</em>.12:327-340. Bhattarai, K.R., Vetaas, O.R. and Grytnes, J.A. (2004). Fern species richness along a central Himalayan elevation gradient, Nepal. <em>Journal of Biogeography</em>.31:398-400. Brown, J.H. (2001). Mammals on mountainsides, elevational patterns of diversity. <em>Global Ecology and Biogeography.</em>10:101-109. Cannon, C.H., Peart, D.R. and Leihton, M. (1998). Tree species diversity in commercially logged Bornean rainforest. <em>Science</em>. 28:1366-1368. Carpenter, C. (2005). The environmental control of plant species density on a Himalaya elevation gradient. <em>Journal of Biogeography</em>. 32:999-1018. Champion, H.G. and Seth, S.K. (1968). <em>A</em> <em>Revised survey of the forest types of India</em>. Manager of Publications, Government of India, New Delhi. Chiarucci, A., Dominics, V.D. and Wilson, J.B. (2001).Structure and floristic diversity in permanent monitoring plots in forest ecosystems of Tuscany. <em>Forest Ecology and</em>&nbsp;&nbsp; <em>Management</em>.141:201-210. Colwell, R.K., Rahbek, C. And Gotelli, N.J. (2004). The mid-domain effect and species richness patterns: What have we learned so far? <em>American Naturalist</em>. 163:E1-E23. Colwell, R.K. and Hurtt, G.C. (1994). Nonbiological gradients in species richness and a spurious Rapoport effect. <em>The American Naturalist</em>. 144:570-595. Curtis, J.T. (1959). <em>The vegetation of Wisconsin university</em>. Wisconsin University.&nbsp; Wisconsin Press, Madison. Curtis, J.T. and Cotton, G. (1956). <em>Plant Ecology Workbook, Laboratory Field</em> <em>Manual</em>. Burgess publishing, Minnesota.pp.193 Dallmeier, F. and Comiskey, J.A. (1998). Forest biodiversity assessment, monitoring and evaluation for adaptive management. In <em>Forest biodiversity research, Monitoring and Modelling: Conceptual Background and Old World Case Studies</em> (eds Dallmeier, F. and Comiskey, J.A.), Partheon Publishing, Paris. pp. 529-540. Eltih, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, a., Li, J., Lohmann, L.G., Loizelle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J., townsend Peterson, A., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberon, J., Williams, S., Wisz, M.S. and Zimmermann, N.E. (2006). Novel methods improve prediction of species&rsquo; distributions from occurrence data. <em>Ecography</em>. 29:129-51. Good, N.F. and Good, R.E. (1972). Population dynamics of tree seedlings and saplings in mature Eastern hardwood forest. <em>Bull Torrey Bot. Club</em>. 99 Greig-Smith, P. (1957). Quantitative Plant Ecology, 2^ndedition. Butterworth, London. Grubb, P.J. (1977). The maintenance of species-richness in plant communities: the importance of the regeneration niche. <em>Biological Reviews</em>. 52: 107&ndash;145. Grytness, J.A. (2003a). Species richness patterns of vascular plants along seven altitudinal transects in Norway. <em>Ecography</em>. 26:291-300. Grytness, J.A. (2003b). Ecological interpretations of the mid-domain effect. Ecological Letter. 6:883-888. Grytness, J.A. and Vetaas, O.R. (2002). Species richness and altitude, a comparision between simulation models and interpolated plant species richness along the Himalayas altitudinal gradient, Nepal. <em>The American Naturalist</em>. 159:294-304. Guisan, A. and Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. <em>Ecology Letters</em>. 8:993-1009. Haines, H.H. (1921-25). <em>The botany of Bihar and Orissa</em>, 6 parts. Adlard &amp; Son and West New man Ltd. London. Heaney, L.R. (2001). Small mammal diversity along elevational gradients in the Philippines: an assessment of patterns and hypotheses. <em>Global Ecology and Biogeography</em>.10:15-39. Hooker, J.D. (1872-97). <em>The flora of British India</em>. London. Huang, W., Pohjonenen, V., Johansson, S., Nashanda, M., Katigula, M.I.L. and Luukkanen, O. (2003). Species diversity, forest structure and species composition in Tanzanian tropical forests. <em>Forest Ecology and Management</em>. 173:11-24. Huston, M.A. (1994). Biological diversity: the coexistence of species on changing landscape, Cambridge University Press, Cambridge. Kelly, C.K. and Bowler, M.G. (2002). Coexistence and relative abundance in forest trees. <em>Nature</em>. 417:437&ndash; 440. Kershw, K.K. (1973). Quantitative and Dynamic Plant Ecology. 2^ndedition, FLBS and Edwards Arnold (Publ.) London, pp.308. Koellner, T., Hersperger, A.M. and Wohlgemuth, T. (2004).&nbsp; Rerefraction method for assessing plant species diversity on a regional scale. <em>Ecography</em>. 27:532-544. Kumar, A., Bruce, G.M. and Ajai, S. (2006). Tree species diversity and distribution patterns in tropical forests of Garo hills. <em>Current Science</em>. 91:1370-1381. Kumar, M. and Bhatt V.P. (2006). Plant biodiversity and conservation of forests in foot hills of Garhwal Himalaya. <em>Journal of Ecology and Application</em>. 11(2):43-59. Lomolino, M.V. (2001). Elevation gradients of species-richness, historical and prospective views. <em>Global Ecology and Biogeography</em>. 10:3-13. Lovett, J.C., Rudd, S., Taplin, J. and Frimont-Moller, C. (2000). Patterns of plant diversity in African south of the Sahara and their implications for conservation management.<em> Biodiversity and Conservation</em>. 9:37-46. Mac Arthur, R.H. (1972). Geographical ecology: Patterns in the distribution of species. Harper &amp; Row, New York. Md. Nor, S. (2001). Elevational diversity patterns of small mammals on Mount Kinabalu, Sabah, Malaysia. <em>Global ecology and Biogeography</em>. Mahajan, D.M. and Kale, V.S. (2006). Spatial characteristics of vegetation cover based on remote sensing and geographical information system (GIS). <em>Tropical Ecology</em>. 47(1): 71-79. Marimon, B.S., Felfili, J.M. and Lima, E.S. (2002). Floristics and phytosociology of the gallery forest of the Bacaba stream, Nova Xavantina, Mato Grosso, Brazil.<em> Edinberg Journal of Botany</em>. 59(2): 303- 318. Mishra, B.P., Tripathi, O.P. and Laloo, R.C. (2005). Community characteristics of a climax subtropical humid forest of Meghalaya and population structure of ten important tree species. <em>Tropical Ecology. </em>&nbsp;46(2):241-251. Mishra, R. (1968). <em>Ecology Work Book</em>. Oxford and IBH Publications, Co. New&nbsp;&nbsp; Delhi. Misra, R.C., and Das, P. (2004). Vegetation stratification of Gandhamardan Hill ranges, Orissa using remote sensing technique. <em>Journal of Economic and Taxonomic Botany</em>. 28(2):429- 438. Misra, R.C. and Das, P. (1998a). Phytogeographical affinities of Plants of Gandhamardan Hill range of Orissa with major Indian mountains. <em>Journal of Economic and Taxonomic.&nbsp; Botany</em>. 22 (1):207-210. Misra, R.C., and Das, P. (1998b). Vegetation Status of Nrusinghanath-Harishankar complex, Orissa. <em>Journal of Economic and Taxonomic Botany</em>. 22(3):547-554. Misra, R.C. and Das, P. (1998c). Inventory of Rare and endangered vascular plants of Gandhamardan Hill ranges in western Orissa. <em>Journal of Economic and Taxonomic .Botany</em>. 22 (2):353-357. Misra, R.C. (1990). Ethnobotanical studies on some plants of Nrusinghanath- Harishankar complex, Orissa<em>. Journal of Environmental Sciences.</em> 3(2): 36-42. Mooney, H. (1950). <em>Supplement to the Botany of Bihar and Orissa, International Book</em> <em>Distributors</em>, Dehradun. Nebel, G., Kvist, L.P. and Vanclay, J.K. (2001). Structure and floristic composition of flood plain forests in the Peruvian Amazon, I. Overstorey.<em> Forest Ecology and</em> <em>Management</em>.150:27- 57. Odum, E.P. (1971). Fundamentals of Ecology. III ed. W.B. Saunders Co., Philadelphia. USA. Pacala, S.W. and Roughgarden, J. (1982). Spatial heterogeneity and interspecific competition. <em>Theoretical Population Biology</em>. 31:92 &ndash;113. Panigrahi, G., Chowdhury, S., Raju, D.C.S. and Deka, G.K. (1964). A contribution to the botany of Orissa. <em>Bulletin of Botanical Survey of India</em>. 6(2-4):237-266. Parthasarathy, N. (2001). Changes in forest composition and structure in three sites of tropical evergreen forest around Sengaltheri, Western Ghats. <em>Current </em>Science. 80:389-393. Pearson, R.G., Dawson, T.P. and Liu, C. (2004). Modelling species distribution in Britain: a hierarchical integration of climate and land cover data. <em>Ecography</em>. 27:285-98. Pianka, E.R. (1966). Latitudinal gradients in species diversity: a review of concepts. <em>American Naturalist.</em> 100:33-46. Rahbek, C. (1997). The relationship among area, elevation and regional species richness in neotropical birds. <em>The American Naturalist</em>.149: 875-902. .<strong>Rahbek, C. (1995).</strong> The elevational gradient of species richness, a uniform pattern? <em>Ecography.</em> 18:200-205. .<strong>Rapoport, E.H. (1982).</strong> Areogrphy: geographical strategies of species. Trans. B. Drausal, Vol.1. Pergamon, New York. Rapoport, E.H. (1975). Areografia: estrategias geograficas des las species. <em>Fondo de Cultura Economica</em>, Mexico City. .Reddy, C.S. and Ugle, P. (2008).Tree species diversity and distribution pattern in tropical forest of Eastern Ghats, India: a case study. <em>Life science Journal</em>. 5(4):87-93. .Rennolls, K. and Laumonier, Y. (2000). Species diversity structure analysis at two sites in the tropical rainforest of Sumatra. <em>Journal of Tropical Ecology</em>.16:253-270. Richards, P.W. (1996). The tropical Rain Forest: an Ecological study.<em>2^nd edition,</em> <em>Cambridge University Press</em>, London. Ruijven, J.V. and Berendse, F. (2007).&nbsp; Contrasting effects of diversity on the temporal stability of plant populations. <em>Oikos</em>.116:1323-1330. . Saxena, H.O. and Brahmam, M. (1996). <em>The flora of Orissa. Vol. I-IV, </em>Orissa&nbsp;&nbsp; Forest&nbsp; Development Corporation, Bhubaneswar, Orissa. Singh, J.S and Yadav P.S. (1974). Seasonal variation in composition, plant biomass and net primary productivity of tropical grassland of Kurukshetra, India. <em>Ecology Monograph</em>. 44:351-375. . Stevens, G.C. (1989).The latitudinal gradient in geographical range: How so many species coexist in the tropics? <em>American&nbsp; Naturalist.</em> 1333:240-256. Stevens, G.C. (1992). The elevational gradient in altitudinal range: An extension of Rapoport&rsquo;s latitudinal rule to altitude. <em>American Naturalist</em>. 140:893-911. Terborgh, J. (1977). Bird species diversity on an Andean elevational gradient. <em>Ecology</em>. 58:1007-1019. . Whittaker, R.J., Willis, K.J. and Field, R. (2001). Scale and species richness; towards a general, hierarchical theory of species diversity. <em>Journal of Biogeography</em>. 28:453-470. &nbsp;

README Elvire Bestion 2024-05-28 1. General Information 1.1. Title of Dataset <strong>Data and code for: Species interactions affect dispersal: a meta-analysis, Bestion et al 2024, Philosophical Transactions B, doi: </strong><strong>10.5281/zenodo.10940162</strong> 1.2. Author information <strong>First author information</strong> &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Name: Elvire Bestion &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Orcid: 0000-0001-5622-7907 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Institution: Station d&rsquo;Ecologie Th&eacute;orique et Exp&eacute;rimentale, CNRS, UAR 2029 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Address: 2 route du CNRS, 09200 Moulis, France &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Email: elvire.bestion@sete.cnrs.fr <strong>Last author information</strong> &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Name: Julien Cote &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Orcid: 0000-0002-4453-5969 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Institution: Centre de Recherche sur la Biodiversit&eacute; et l&rsquo;Environnement (CRBE), UMR 5300 CNRS-IRD-TINP-UT3 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Address: Universit&eacute; Toulouse III &ndash; Paul Sabatier, B&acirc;t 4R1, 118 route de Narbonne, 31062 Toulouse, France &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Email: julien.cote@univ-tlse3.fr 1.3. Abstract Context-dependent dispersal allows organisms to seek and settle in habitats improving their fitness. Despite the importance of species interactions in determining fitness, a quantitative synthesis on how they affect dispersal is lacking. We present a meta-analysis asking (1) whether the interaction experienced and/or perceived by a focal species (detrimental interaction with predators, competitors, parasites, or beneficial interaction with resources, hosts, mutualists) affects its dispersal, (2) how the species&rsquo; ecological and biological background affects the direction and strength of this interaction-dependent dispersal. After a systematic search focusing on actively dispersing species, we extracted 397 effect sizes from 118 empirical studies encompassing 221 species pairs; arthropods were best represented, followed by vertebrates, protists and others. Detrimental species interactions increased the focal species&rsquo; dispersal (adjusted effect: 0.33 [0.06,0.60]), while beneficial interactions decreased it (-0.55 [-0.92,-0.17]). The effect depended on the dispersal phase, with detrimental interactors having opposite impacts on emigration and transience. Interaction-dependent dispersal was negatively related to species&rsquo; interaction strength, and depended on the global community composition, with cues of presence having stronger effects than presence of the interactor, and the ecological complexity of the community. Our work demonstrates the importance of interspecific interactions on dispersal plasticity with consequences for metacommunity dynamics. 1.4. Keywords context-dependent dispersal, metacommunity dynamics, predator-prey interactions, competition, host-parasite interactions, biotic interactions 1.5. Date of data collection A search on Web of Science was conducted on the 20th of October 2021, yielding 21499 results, which were subsequently filtered to 118 studies and 397 effect sizes. 1.6. Taxon or species from which data was collected The search was done across species, with the only species criterion being that focal species should be active dispersers. The final dataset contains data for 144 focal taxa and 165 interacting taxa. 2. Sharing and access information 2.1. Licenses and restrictions placed on the data The data is usable under Creative Commons Attribution 4.0 International licence but we would appreciate if we were contacted prior to meaningful use, and this dataset and the matching paper cited if appropriate. 2.2. Links to publications that cite or use the data This dataset corresponds to the data used in: <strong>Bestion E, Legrand D, Baines CB, Bonte D, Coulon A, Dahirel M, Delgado M, Deshpande JN, Duncan AB, Fronhofer EA, Gounand I, Jacob S, Kaltz O, Massol F, Matthysen E, Parmentier T, Saade C, Schtickzelle N, Zilio G, Cote J. 2024. Species interactions affect dispersal: a meta-analysis. Philosophical Transactions B, 379:20230127, doi: </strong><strong>10.1098/rstb.2023.0127</strong> 2.3. Links to other publicly accessible locations of the data The data can be found on zenodo at doi: 10.5281/zenodo.10940162 2.4. Was data derived from another source? Yes. The data used in this meta-analysis was sourced from 118 primary research papers, see output/Table_bibliography.docx for a list of the primary research articles. 2.5. Recommended citation for this dataset <strong>Bestion et al, 2024. Data and code for: Species interactions affect dispersal: a meta-analysis, Philosophical Transactions B, Zenodo, doi: </strong><strong>10.5281/zenodo.10940162</strong> 3. Data and file overview 3.1. Directory structure .|- data | |- Database_Bestion.csv | |- Phylogeny_focal.csv | |- Phylogeny_interactor.csv&nbsp; | |- catalogue_life_taxonomy.csv |- interm| |- R_session_info_2024-04-02.txt | |- dredge_mod.rds | |- dredge_mod_detrimental.rds | |- dredge_mod_beneficial.rds | |- list_best_mod.rds | |- list_best_mod_detrimental.rds | |- list_best_mod_beneficial.rds | |- av_mod.rds | |- av_mod_detrimental.rds| |- av_mod_beneficial.rds | |- FigureXX&nbsp; | |- Plot_relationship_variance_sample_size.png|- output | |- Figure_2_type_and_nature_interactor.png&nbsp; | |- Figure_3_dispersal_phase.png | |- Figure_4_biotic_context.png | |- Figure_5_generalism.png | |- Figure_6_interaction_strength.png | |- Figure_S1_prisma_plot.png | |- Figure_S2_focal_phylogenetic_tree.png | |- Figure_S3_Sankey_plots_by_phylum_class.png | |- Figure_S4_Sankey_general_plot.png | |- Figure_S5_Funnel_full_mod.png | |- Figure_S6_Funnel_detrimental_beneficial.png | |- Figure_S7_duration_generation_type_study.png | |- Tables_main_text_and_supplementary.docx | |- Tables_information_in_the_main_text_and_supplementary.docx | |- Table_bibliography.docx |- raw_data| |- Raw_database_Bestion.csv | |- Phylogeny_focal.csv&nbsp; | |- Phylogeny_interactor.csv&nbsp; |- 00_Analysis.Rproj&nbsp; |- 0_functions.R&nbsp; |- 1_data_preparation.R |- 2_analysis.R |- README.docx |- README.html |- README.md|- README.Rmd |- renv.lock 3.2. Description of directories Name Description data Directory for cleaned datasets used in this study after the cleaning step interm Directory for saving intermediate calculation steps from R script output Directory for saving figures and tables resulting from the analysis found in the main article and supplementary data raw_data Directory for the raw datasets used in this study 3.3. Description of primary data files The raw data files are in the /raw_data folder and the cleaned data files in the /data folder <strong>Data in the /raw_data folder</strong> Name Description Raw_database_Bestion.csv the database for the 119 papers selected for the meta-analysis, with information to calculate effect sizes. This dataset will be cleaned, effect sizes will be calculated, and it will be transformed to data/Database_Bestion.csv through the 0_data_preparation.R script. Note that this dataset contains more rows than the Database_Bestion finally used in the meta-analysis, as 6 rows with very high variance of the effect size were excluded for model stability, leading to exclude one study Phylogeny_focal.csv a dataset containing the names of each focal taxa as well as their taxonomic identity derived from the taxize R package with all of the species in the raw database Phylogeny_interactor.csv a dataset containing the names of each interactor taxa as well as their taxonomic identity derived from the taxize R package with all of the species in the raw database <strong>Data in the /data folder</strong> Name Description Database_Bestion.csv the database for the 118 papers finally used in the meta-analysis with the calculated effect sizes and the cleaned variables needed for the meta-analysis Phylogeny_focal.csv a dataset containing the names of each focal taxa as well as their taxonomic identity derived from the taxize R package, filtered to contain taxonomic info for the 118 studies instead of 119 Phylogeny_interactor.csv a dataset containing the names of each interactor taxa as well as their taxonomic identity derived from the taxize R package, filtered to contain taxonomic info for the 118 studies instead of 119 catalogue_life_taxonomy.csv a dataset containing information about the number of species for taxons at different levels of resolution gathered from the catalogue of life 3.4. Description of files in the interm and output folders derived using R The project leads to the creation of intermediate calculation steps from the R script that are stored in the /interm folder and of figures and tables resulting from the analysis found in the main article and supplementary data that are stored in the /output folder. <strong>Objects in the /interm folder</strong> Name Description R_session_info_2024-04-02.txt the session information about the R version and R packages used when running the R code dredge_mod.rds the R object resulting from the dredge of the main model. Note that the dredge is very time intensive, thus we save the R object to the intern folders. Running the code will overwrite this object dredge_mod_detrimental.rds the R object resulting from the dredge of the main model for the detrimental interaction subset dredge_mod_beneficial.rds the R object resulting from the dredge of the main model for the beneficial interaction subset list_best_mod.rds the R object resulting from the list of best models (with deltaic&lt;2) from the dredge of the main model list_best_mod_detrimental.rds the R object resulting from the list of best models (with deltaic&lt;2) from the dredge of the main model for the detrimental interaction subset list_best_mod_beneficial.rds the R object resulting from the list of best models (with deltaic&lt;2) from the dredge of the main model for the beneficial interaction subset av_mod.rds the R object resulting from the model averaging of the best models (with deltaic&lt;2) from the dredge of the main model av_mod_detrimental.rds the R object resulting from the model averaging of the best models (with deltaic&lt;2) from the dredge of the main model for the detrimental interaction subset av_mod_beneficial.rds the R object resulting from the model averaging of the best models (with deltaic&lt;2) from the dredge of the main model for the beneficial interaction subset FigureXX the figures corresponding to the parts of the main figures (e.g.&nbsp;Figure_type_interactor and Figure_nature_interactor correspond to the Fig 2a and 2b separated), with each effect of a moderator on dispersal (e.g.&nbsp;Figure_dispersal_interactor), and with information about the database (Figure_sankey_phylums). There are 13 figures Plot_relationship_variance_sample_size.png preliminary plot looking at the relationship between variance and sample size for imputation of the variance for the papers for which variance was not found <strong>Objects in the /output folder</strong> Name Description Figure_2_type_and_nature_interactor.png the figure 2 (also available as pdf) Figure_3_dispersal_phase.png the figure 3 (also available as pdf) Figure_4_biotic_context.png the figure 4 (also available as pdf) Figure_5_generalism.png the figure 5 (also available as pdf) Figure_6_interaction_strength.png the figure 6 (also available as pdf) Figure_S1_prisma_plot.png the figure S1 Figure_S2_focal_phylogenetic_tree.png the figure S2 Figure_S3_Sankey_plots_by_phylum_class.png the figure S3 Figure_S4_Sankey_general_plot.png the figure S4 Figure_S5_Funnel_full_mod.png the figure S5 Figure_S6_Funnel_detrimental_beneficial.png the figure S6 Figure_S7_duration_generation_type_study.png the figure S7 Tables_main_text_and_supplementary.docx word document containing the formatted tables present in the main text (Table 1 and 2) and in the supplementary material (Table S3 to S7). Note it is also available as an html document Tables_information_in_the_main_text_and_supplementary.docx word document containing tables corresponding to information in the main text and supplementary material (e.g.&nbsp;I2 values for the main model and model with subsetted data, information about levels in each moderator). Note it is also available as an html document Table_bibliography.docx table of the names of the 118 articles used in the final data. Note it is also available as an html document 3.5. Description of R scripts Name Description 0_functions.R functions used in the main analysis script 1_data_preparation.R The data preparation step of the analysis, that cleans the data and calculates effect sizes. This script uses the raw data in the /raw_data folder, and outputs cleaned data to the /data folder for the next step of the analysis 2_analysis.R The main analysis script. This script calls to the 0_functions.R for functions, uses the data in the /data, and outputs results to the /interm and /output folder. This script does the main analysis for the meta-analysis 3.6. Description of other files Name Description 00_Analysis.Rproj The R project file README.docx the present readme as a word file README.html the present readme as an html file README.md the present readme as a markdown file README.rmd the present readme as a Rmarkdown file renv.lock file with information about the R packages used to do the analysis. Users that want to re-create the R environment need to install R version 4.3.1 and the renv package version 1.0.2, and to call for the renv::restore() function in the R project to install the exact version of the packages used in the analysis 4. Methodological information 4.1. Description of methods used for collection of data A detailed explanation of the methods is provided in the related Bestion et al 2024 Philosophical Transactions B article, please refer to it for a full understanding of the methods and results. Briefly: Our aim was to study through a meta-analysis whether (1) whether different types of interactors (predators, parasites, competitors negatively affecting fitness or resources, hosts and mutualists with a positive impact) have different impacts on the focal species&rsquo; dispersal, and (2) which species ecological and biological attributes modulate the dependency of dispersal on species interactions. We conducted the literature search on Web of Knowledge in October 2021 for keywords related to dispersal, and species interactions for a list of keywords used in Web of Knowledge. The search yielded 21, 496 articles. We examined each abstract to determine whether articles met the criteria for inclusion. Criteria for inclusion comprised (a) the presence, abundance or density of an interacting species was quantified and experimentally varied or not, (b) the rate of emigration or the dispersal distance or another dispersal metric of a focal species that was quantified in different contexts of species interactions, (c) the effect of a single interacting species on a focal species can be isolated when more than two species were studied and (d) the two species were interacting species in natural environments, excluding artificial biotic elements (e.g., unnatural resource or predator species). The filtering process led to a selection of 1,539 studies from the original search that fitted the scope of this overview. These articles were then reviewed in full to determine whether they fitted our inclusion criteria, contained relevant data and whether the results were presented with sufficient clarity to extract effect sizes. During this step, we refined our inclusion criteria to exclude (a) studies in which the focal species dispersed passively and in which there was no active choice to emigrate, (b) studies which studied the colonization process only, keeping studies on emigration, transience or the ones monitoring the full dispersal process, (c) studies in which the second species did not interact with the focal species, (d) studies in which the effect of an interacting species on the dispersal of the focal species was compared to a control with a second interacting species instead of no interacting species or different abundances of interacting species. This step led to the further exclusion of 1,420 articles from the first search. In the final set of 118 studies, we extracted 397 effect sizes and collected information on several moderators to investigate our questions of interest. Data to calculate effect sizes were extracted preferentially from raw data when available, from figures using the juicr v 0.1 R package, or directly from the paper (tables or main text). We calculated Hedges&rsquo; d, and extracted several aspects of the experimental design (see 1_data_preparation.R script). We then used meta-analytic models to study our question of interest, with a model selection approach (see 2_analysis.R script). 4.2. Instrument- or software-specific information needed to interpret the data To reproduce the analyses, the data should be read in R with the provided R scripts. The R version and the version of the packages used to reproduce the data are found in the renv.lock file, as well as for a more human readable file, in the interm/R_session_info_2024-04-02.txt file 4.3. People involved with sample collection, processing, analysis and/or submission The idea of the article originally emerged from a dispersal workshop organised by Emanuel A Fronhofer, in which Elvire Bestion and Julien Cote proposed to lead the article. Elvire Bestion, Julien Cote, Delphine Legrand, Dries Bonte, Jhelam N Deshpande, Alison B Duncan, Emanuel A Fronhofer, Oliver Kaltz, Fran&ccedil;ois Massol, Thomas Parmentier, Camille Saade, Nicolas Schtickzelle, Giacomo Zilio discussed the original search terms, and Elvire Bestion, Julien Cote and Delphine Legrand refined the search. Elvire Bestion and Julien Cote defined the abstract screening strategy, with the help of Delphine Legrand, and all authors participated in abstract screening. Elvire Bestion, Julien Cote and Delphine Legrand defined the full text screening strategy and the type of data to be extracted. Elvire Bestion screened each article&rsquo;s full text with the help of Julien Cote. Julien Cote and Delphine Legrand extracted complementary data from the literature. Elvire Bestion extracted effect sizes with the help of Julien Cote. Elvire Bestion ran the analyses. Evire Bestion and Julien Cote wrote the first draft of the manuscript, Delphine Legrand contributed to early draft revisions, and all authors contributed to revisions. 5. Data-specific information 5.1. Codebook for the Database_Bestion dataset <strong>Number of variables:</strong> 37 <strong>Number of rows:</strong> 397 <strong>Variable list:</strong> Name Description rownumber the row name (identification) STUDY_ID the study id (identification) AU the authors&rsquo; names TI the title of the study SO The name of the journal PY the year of publication (numeric) VL the volume number (numeric) BP the page number beginning (numeric) EP the page number end (numeric) DI the doi of the article focal_tax the focal taxon ID (identification). The ID can be either a species name, a genus name or a family name depending on the taxonomic precision of the primary research article interactor_tax the interacting taxon ID (identification). The ID can be either a species name, a genus name or another taxonomic level or group name depending on the taxonomic precision of the primary research article pair_species concatenation of the focal and interactor taxa (identification) type_interactor the type of interactor relative to the focal taxa, i.e.&nbsp;is the interactor a beneficial interactor (mutualist, host, resource) or a detrimental interactor (competitor, predator, parasite) nature_interactor the nature of the interactor, i.e.&nbsp;is the interactor a competitor, a predator, a parasite, a mutualist, a host, or a resource type_study is the study an experiment or observation type_experimental_system is the study a laboratory study, a semi-natural study or in natura type_manipulation_interactor How the presence of the interacting taxon is manipulated (or in the case of observational studies, is recorded. Either presence_interaction, abundance or presence_cues type_community the level of complexity of the community (either 1_pair = pair of species, 2_simp = simple community or 3_complex = complex community) possibility_dispersal_interactor whether the interacting taxon can disperse during the experiment (yes or no) similarity_generation_interaction_dispersal whether the interaction and dispersal take place on the same generation or a different generation (e.g.&nbsp;nest predation but dispersal of adult birds) log_generation_time_focal the log generation time of the focal species in days (numeric) log_duration_experiment the log duration of the experiment in days (numeric) specialisation_focal the level of specialisation of the focal species. We rated focal and interacting species from on whether they eat only one species (mono), from the same genus or family (oligo), from the same order (multi), or from different orders (poly) specialisation_interactor the level of specialisation of the interacting species specialisation_focal_num numeric, the level of specialisation of the focal species transformed to numeric (1 = mono, 2 = oligo, 3 = multi, 4 = poly) specialisation_interactor_num numeric, the level of specialisation of the interacting species transformed as a numeric home_range_focal the home range of the focal species in Km2 home_range_interactor the home range of the interacting species in Km2 interaction_strength the interaction strength between pair of species, either from the primary source or derived from secondary literature interaction_same_study is the interaction data extracted from the same study as the dispersal data dispersal_phase the dispersal phase, either emigration, transience or full dispersal es the effect size as Hedges&rsquo;d var the variance of the effect size weight the weight associated with the effect size sample.size the sample size <strong>Missing data codes:</strong> NA 5.2. Codebook for the Phylogeny_focal dataset <strong>Number of variables:</strong> 8 <strong>Number of rows:</strong> 144 in the /data folder (145 in the raw_data folder, exclusion of one focal species during data preparation) <strong>Variable list:</strong> Name Description focal_tax the name of the focal taxon (factor, either a species name, a genus name or another taxonomic level depending on the taxonomic precision of each primary article) focal_kindgom the kingdom of the focal taxon (factor) focal_phylum the phylum of the focal taxon (factor) focal_class the class of the focal taxon (factor) focal_order the order of the focal taxon (factor) focal_family the family of the focal taxon (factor) focal_genus the genus of the focal taxon (factor) focal_species the species of the focal taxon (factor) <strong>Missing data codes:</strong> NA (for genus and/or species names of taxa identified to either the family or the genus level in the primary research study). 5.3. Codebook for the Phylogeny_interactor dataset <strong>Number of variables:</strong> 15 (8 in the raw_data folder, the other columns are created by the data preparation step) <strong>Number of rows:</strong> 165 (167 in the raw_data folder, exclusion of 2 lines in the data preparation step) <strong>Variable List</strong> Name Description interactor_tax the name of the interacting taxon (factor, either a species name, a genus name or another taxonomic level or group name depending on the taxonomic precision of each primary article) interactor_kindgom the kingdom of the interacting taxon (factor) interactor_phylum the phylum of the interacting taxon (factor) interactor_class the class of the interacting taxon (factor) interactor_order the order of the interacting taxon (factor) interactor_family the family of the interacting taxon (factor) interactor_genus the genus of the interacting taxon (factor) interactor_species the species of the interacting taxon (factor) interactor_kindgom2 the kingdom of the interacting taxon with NAs replaced by &laquo;undetermined&raquo; (factor) interactor_phylum2 the phylum of the interacting taxon with NAs replaced by &laquo;undetermined&raquo; (factor) interactor_class2 the class of the interacting taxon with NAs replaced by &laquo;undetermined&raquo; (factor) interactor_order2 the order of the interacting taxon with NAs replaced by &laquo;undetermined&raquo; (factor) interactor_family2 the family of the interacting taxon with NAs replaced by &laquo;undetermined&raquo; (factor) interactor_genus2 the genus of the interacting taxon with NAs replaced by &laquo;undetermined&raquo; (factor) interactor_species2 the species of the interacting taxon with NAs replaced by &laquo;undetermined&raquo; (factor) <strong>Missing data codes:</strong> NA (for lower taxonomic levels for taxa identified only to upper taxonomic levels in the primary research study). 5.4. Codebook for the catalogue_life_taxonomy dataset <strong>Number of variables:</strong> 5 <strong>Number of rows:</strong> 21 <strong>Variable List</strong> Name Description biological_level the level of biological resolution (either total, kingdom, phylum) level the name of the taxonomic level (e.g.&nbsp;for kingdom, Animalia or Chromista) is_animalia does the taxon pertain to the animalia number_species the number of species for this taxon gathered from the catalogue of life remark remarks <strong>Missing data codes:</strong> NA 5.5. Codebook for the Raw_database_bestion dataset <strong>Number of variables:</strong> 90 <strong>Number of rows:</strong> 403 (note, 6 lines excluded in the data preparation step) <strong>Variable List</strong> Name Description rownumber the row name (identification) STUDY_ID the study id (identification) AU the authors&rsquo; names TI the title of the study SO The name of the journal PY the year of publication (numeric) VL the volume number (numeric) BP the page number beginning (numeric) EP the page number end (numeric) DI the doi of the article Abstract_reviewer the name of the person who reviewed the abstract Text_reviewer the name of the person who reviewed the full text type_interaction the type of interaction, either consumption, parasitism, competition or mutualism focal_tax the focal taxon ID (identification). The ID can be either a species name, a genus name or a family name depending on the taxonomic precision of the primary research article interactor_tax the interacting taxon ID (identification). The ID can be either a species name, a genus name or another taxonomic level or group name depending on the taxonomic precision of the primary research article pair_species concatenation of the focal and interactor taxa type_interactor the type of interactor relative to the focal taxa, i.e.&nbsp;is the interactor a beneficial interactor (mutualist, host, resource) or a detrimental interactor (competitor, predator, parasite) nature_interactor the nature of the interactor, i.e.&nbsp;is the interactor a competitor, a predator, a parasite, a mutualist, a host, or a resource type_study is the study an experiment or observation type_experimental_system is the study a laboratory study, a semi-natural study or in natura experimental_device a text description of the experimental device type_ecosystem either aquatic or terrestrial manipulation_interactor is the interactor presence/abundance manipulated? yes/no type_community the level of complexity of the community (either 1_pair = pair of species, 2_simp = simple community or 3_complex = complex community) presence_abundance is the interactor presence or the interactor abundance recorded/manipulated interaction_or_not are the interactors actually in interaction (interaction), or are cues of presence used (cues) individual_populational_interaction is the interaction at the individual level (e.g.&nbsp;parasite infection) or at the population level (e.g.&nbsp;parasite prevalence in the population) type_of_cues a description of the type of cues used possibility_dispersal_interactor whether the interacting taxon can disperse during the experiment (yes or no) similarity_generation_interaction_dispersal whether the interaction and dispersal take place on the same generation or a different generation (e.g.&nbsp;nest predation but dispersal of adult birds) duration_experiment the duration of the experiment in days (numeric) log_duration_experiment the log duration of the experiment in days (numeric) acclimation_before_dispersal is there an acclimation phase to the experimental setting before measuring dispersal, and if yes, of what length? generation_time_focal the log generation time of the focal species in days (numeric) article_generation_time the article from which the generation time info was extracted age_class_focal information on the age class of the focal individual sex_focal information on the sex of the focal individual specialisation_focal the level of specialisation of the focal species. We rated focal and interacting species from on whether they eat only one species (mono), from the same genus or family (oligo), from the same order (multi), or from different orders (poly) specialisation_interactor the level of specialisation of the interacting species choice_density_focal information about the choice for the density of the focal species choice_density_interactor information about the choice for the density of the interactor density_focal_sp_treatment density of the focal species for the treatment level density_focal_sp_control density of the focal species for the control level density_interactor_treatment density of the interactor for the treatment level density_interactor_control density of the interactor for the control level density_unit unit in which the density is measured second_treatment description of a second treatment if any second_treatment_level level of the second treatment if any additional_factors additional information on the study design latitude the latitude longitude the longitude interaction_strength the interaction strength between pair of species, either from the primary source or derived from secondary literature interaction_strength_metric the type of data from which interaction strength was measured (e.g.&nbsp;survival, reproduction, abundance) interaction_same_study is the interaction data extracted from the same study as the dispersal data (yes/no) article_interaction_strength the study from which the interaction strength data was extracted home_range_focal the home range of the focal species in Km2 home_range_interactor the home range of the interacting species in Km2 dispersal_phase the dispersal phase, either emigration, transience or full dispersal dispersal_metric the dispersal metric used by the study dispersal_units the units of the dispersal metric treatment the description of the treatment level (e.g.&nbsp;cues of sp2 added) control the description of the control level (e.g.&nbsp;sp2 absent) type_effect_size the type of effect size (e.g.&nbsp;pairs of means, contingency table, log odds ratio) need_to_reverse_effect_size does the effect size need to be reversed (e.g.&nbsp;study on remaining rate instead of dispersal rate)? YES/NO N_replicates_total the total number of replicates N_individuals_total the total number of individuals in all of the treatments N_treatment_pairs if data extracted is pairs_of_means, the number of replicates for the treatment mean_treatment if data extracted is pairs_of_means, the mean dispersal in the treatment SD_treatment if data extracted is pairs_of_means, the SD of dispersal in the treatment N_control_pairs if data extracted is pairs_of_means, the number of replicates for the control mean_control if data extracted is pairs_of_means, the mean dispersal in the control SD_control if data extracted is pairs_of_means, the SD of dispersal in the control pearson_correlation if data extracted is correlation, the pearson correlation coefficient N_correlation if data extracted is correlation, the N for the correlation Ndisp_treatment if data extracted is contingency table, the number of dispersers in the treatment N_treatment_contin if data extracted is contingency table, the total N individuals in the treatment Ndisp_control if data extracted is contingency table, the number of dispersers in the control N_control_contin if data extracted is contingency table, the total N individuals in the control F_value if data extracted is from F_test, with two groups, the F_value t_value if data extracted from t_test, the t value Mann_whitney_z_value if data extracted from a mann whitney non parametric test, the z value chisquare_value if data extracted from 2-by-2 chisquare test, the chisquare value Log_odds_ratio if data extracted as log_odds_ratio, the log_odds_ ratio Var_log_odds_ratio if data extracted as log_odds_ratio, the variance of the log_odds_ ratio Eta2 if data extracted as eta2, the eta2 Hedges_d if data extracted as any other way, the already calculated Hedges&rsquo; d Hedges_d_variance if data extracted as any other way, the already calculated Hedges&rsquo; d variance data_extraction_method the method from data extraction (either from the raw_data, a figure, a table, the text&hellip;) <strong>Missing data codes:</strong> NA

The edible freshwater bivalves identified in Cagayan River were: Batissa childreni, Corbicula manilensis and Psammotaea virescens. Corbicula manilensis was the most abundant species collected. High density and bigger sized bivalves were collected in the months of May, which was the peak season. A high density of Batissa childreni was noted as the temperature of water was low. Total hardness and organic content significantly influenced the density of Corbicula manilensis and Psammotaea virescens. Lesser density of Corbicula manilensis resulted as the total hardness decreased while high density of Psammotaea virescens was noted as the organic content of the substrate increased. Relative abundance was greatly affected by the total water hardness. A positive correlation existed between relative abundance of Corbicula manilensis and total hardness. Shell sized of Corbicula manilensiswas bigger as the total hardness increased and bigger sized Psammotaea virescens was noted as the organic content of the substrate increased. No predictor variables were significantly correlated with relative abundance of Psammotaea virescensand the shell sized of Batissachildreni.

This paper presents the results of estimating the population density and abundance of Ursus arctos (hereinafter – brown bear) in the Southern Forestry of the Central Forest State Nature Biosphere Reserve (CFNR), West of European Russia, in 2021 based on the Random Encounter Model (REM) based upon data obtained from camera traps. Methods for obtaining parameters necessary for building a model are demonstrated. A total of 7970 camera trap nights were worked out at 46 stations, and 502 independent trap events were obtained. The average relative abundance index (RAI) was 6.28 ± 1.59. The total average brown bear population density was 0.086 ± 0.034 individuals per 1 km2. The approximate estimated abundance was 18.98 ± 7.54 individuals. The coefficient of variation was 38%. Population density estimates had a pronounced seasonal dynamics. The minimum value was recorded for the period from 24 June to 23 July (individuals feeding on meadows and ants outside the CFNR core area), and the maximum for the period from 24 July to 22 August (brown bears feeding by berries in the CFNR core area). We found a strong significant correlation between brown bear population density and its relative abundance index (r = 0.81, p &lt; 0.05). It was found that with an increase in the sampling period duration, the estimate of the population density noticeably decreases (r = -0.53, p &lt; 0.05). Parameters of the average travel speed and activity level are a subject to the greatest variability, which determines the significant variability of the day range. In general, the method of population density estimation using REM is highly promising to carry out the brown bear population size estimation in forests and mountain forests, where visual estimations are difficult or impossible.

Passive acoustic monitoring (PAM) with autonomous bottom-moored recorders is widely used to study cetacean occurrence, distribution and behaviors, as it is less affected by factors that limit other observation methods (e.g., vessel, land and aerial-based surveys) such as inclement weather, sighting conditions, or remoteness of study sites. During the winter months in Hawai‘i, humpback whale male song chorusing becomes the predominant contributor to the local soundscape and previous studies showed a strong seasonal pattern, suggesting a correlation with relative whale abundance. However, the relationship between chorusing levels and abundance, including non-singing whales, is still poorly understood. To investigate how accurately acoustic monitoring of singing humpback whales tracks their abundance, and therefore is a viable tool for studying whale ecology and population trends, we collected long-term PAM data from three bottom-moored Ecological Acoustic Recorders off west Maui, Hawaii during the winter and spring months of 2016–2021. We calculated daily medians of root-mean-square sound pressure levels (RMS SPL) of the low frequency acoustic energy (0–1.5 kHz) as a measure of cumulative chorusing intensity. In addition, between December and April we conducted a total of 26 vessel-based line-transect surveys during the 2018/19 through 2020/21 seasons and weekly visual surveys (n = 74) from a land-based station between 2016 and 2020, in which the location of sighted whale pods was determined with a theodolite. Combining the visual and acoustic data, we found a strong positive second-order polynomial correlation between SPLs and abundance (land: 0.72 ≤ R2 ≤ 0.75, vessel: 0.81 ≤ R2 ≤ 0.85 for three different PAM locations; Generalized Linear Model: pland ≪ 0.001, pvessel ≪ 0.001) that was independent from recording location (pland = 0.23, pvessel = 0.9880). Our findings demonstrate that PAM is a relatively low-cost, robust complement and alternative for studying and monitoring humpback whales in their breeding grounds that is able to capture small-scale fluctuations during the season and can inform managers about population trends in a timely manner. It also has the potential to be adapted for use in other regions that have previously presented challenges due to their remoteness or other limitations for conducting traditional surveys.

Accurate assessments of the patterns and drivers of livestock depredation by wild carnivores are vital for designing effective mitigation strategies to reduce human-wildlife conflict. Snow leopard’s (Panthera uncia) range extensively overlaps pastoralist land-use and livestock predation there is widely reported, but the ecological determinants of livestock consumption by snow leopards remain obscure. We investigated snow leopard dietary habits at seven sites across the Sanjiangyuan region of the Qinghai–Tibetan Plateau (QTP), an area central to the species’ global range. Snow leopard abundance, wild prey composition, and livestock density varied among those sites, thus allowing us to test the effects of various factors on snow leopard diet and livestock predation. Using DNA metabarcoding, we obtained highly resolved dietary data from 351 genetically verified snow leopard fecal samples. We then analyzed the prey preferences of snow leopards and examined ecological factors related to their livestock consumption. Across the sites, snow leopard prey was composed mainly of wild ungulates (mean = 81.5% of dietary sequences), particularly bharal (Pseudois nayaur), and supplemented with livestock (7.62%) and smaller mammals (marmots, pikas, mice; 10.7%). Snow leopards showed a strong preference for bharal, relative to livestock, based on their densities. Interestingly, both proportional and total livestock consumption by snow leopards increased linearly with local livestock biomass, but not with livestock density. That, together with a slight negative relationship with bharal density, supports apparent facilitation between wild and domestic prey. We also found a significant positive correlation between population densities of snow leopard and bharal, yet those densities showed slight negative relationships with livestock density. Our results highlight the importance of sufficient wild ungulate abundance to the conservation of viable snow leopard populations. Additionally, livestock protection is critically needed to reduce losses to snow leopard depredation, especially where local livestock abundances are high.

Abstract To determine the species composition, relative abundance and seasonal variation of different mosquitoes Genera (Aedes, Anopheles, Armigeres, Culex, and Culiseta) in different habitats the present research work was carried out in Entomology Research Laboratory of The University of Peshawar. Sampling performed from variety of permanent and temporary breeding habitats was carried out on monthly basis from targeted breeding sites for two consecutive years through dipping method. Species diversity in the survey sites was noted. Collection from these seventeen various types of potential larval habitats, yielded a total of 42,430 immature constituting 41,556 larvae and 874 pupae. Among these only 19,651 adult mosquitoes emerged comprising 11,512 female and 8,139 male mosquitoes. 78% (n= 15333) of mosquito larvae were from permanent and 22% (n=4318) were from temporary breeding sites. This study showed that Peshawar valley harbours 15 species from the genera Aedes, Anopheles, Armigeres, Culex and Culiseta. When the density of each species was examined, Culex quinquifasciatus was found to be dominant (79%) and constant in distribution. Among the temporary habitats Aedes albopictus was found as the most prevalent species particularly from tree holes and water cisterns. The highest intensity of mosquitoes was in June (2243 emerged adults) and November (2667 emerged adults) while the lowest was in January (203 emerged adults). A perfect positive correlation (r = +0.8) was found between temperature and population of mosquitoes (df 10 and α 0.05). The species diversity index for mosquitoes remained between 0.12 and 1.76. The Margalef’s richness components was noticeably low for bamboo traps (0.2) and fairly high for rice fields, Percolating water and Animal tracks (1.3) which shows the abundance of mosquito species in these habitats. Similarly Pielou’s Evenness was highest for bamboo traps (E=1) showing species uniform distribution. Animal tracks were presumed not only the diverse habitat rather also possessed high value for species richness and species evenness. Temperature, rainfall, humidity and other related attributes responsible for species variation and abundance need to be analysed further to pave way for controlling vector species in their oviposition targeted sites.