Ecology

Bacterial strains isolation and identificationFifty six bacterial cultures were isolated from both management systems (G1 and G2) of mango orchards (rhizosphere) at CISH, Lucknow, India. Isolation of microorganisms using spread plate methods revealed that the Nutrient agar medium had the highest number of colony appearances compared to the Rose Bengal Agar medium. Microbial enumeration showed organic system enriched with higher bacterial and fungal population than conventional system (Fig. 1). From organic system, thirty seven bacteria were isolated out of which, twenty-three isolates were (G+), and fourteen were (G−). While, in the conventional system, nineteen bacteria were isolated, out of which fifteen were (G+) and four were (G−) isolates.Figure 1Comparative microbial enumeration of organic and conventional treated mango rhizosphere soil the CFU mL−1 of selected samples showing growth of fungus and bacterial populations under two different treatments i.e. organic and conventional. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imagePlant growth promotion propertiesFor plant growth promotory properties out of fifty-six bacterial isolates total, ten bacterial cultures (2, 3, 4, 8, 15, 23 and 31) from the organic system showed positive results for phosphate solubilization. In contrast, three bacterial cultures (I1, I8 and I9) from the inorganic system (conventional system) showed positive phosphate solubilization in Pikovaskya’s agar medium. For siderophore production, bacterial cultures (2, 3, 4, 8, 12 and 26) from the organic system showed positive results, while four bacterial cultures (I1, I6, I8 and I9) inorganic system showed positive results. Bacterial cultures (2, 3, 4 and 8) from the organic system showed positive results for K-solubilization, while five bacterial cultures (I1, I2, I7, I8 and I9) from the inorganic system showed positive K-solubilization. A total of ten isolates (7 from organic and 3 from the inorganic system) possessed Zn-solubilizing activity. The test isolated from the organic system showed better Zn (ZnO), Zn3 (PO4)2, and (ZnCO3) solubilization as compared to test culture isolated from the inorganic system (Supplementary S1.8).Acetylene reduction assay (ARA)Results from acetylene reduction assay showed in aerophilic condition, bacterial isolates 1, 3, 4 (from organic treated soil) and I1, I8 and I9 (conventional system) showed 134.8, 37.70, 36.73, 13.15, 16.70 and 12.87 ppm of ethylene tube−1 h−1, respectively. In case of microaerophilic condition, bacterial isolates 4, 9, I9 showed 24.17, 19.14, and 12.71 ppm ethylene, respectively. Results indicate possible use of these bacterial isolates as a bioinoculant agent for horticultural crops, especially mango and other subtropical climate fruit crops.Soil enzymatic studyThe soil enzymatic activity in the organic system (G1) showed better dehydrogenase activity than the conventional system (G2). For both methods, alkaline phosphatase almost showed similar activity (at pH 11), while in the case of acid phosphatase showed better activity in the inorganic system (G2) as compared to the organic system (G1) at pH level 6.5 (Fig. 2). The dehydrogenase enzyme oxidizes the organic matter, and it belongs to the oxidoreductase type of enzyme. In the process of respiration of soil microorganisms, the dehydrogenase enzyme facilitates the transfer of protons and electrons from the substrate to the acceptor. It was significant to observe that the dehydrogenase activity was higher in organic treated soils (0.784 µg TPF g−1 h−1) than in conventional system (0.053 µg TPF g−1 h−1).Figure 2Comparative soil enzymes activities of conventional and organic treated mango rhizosphere soil the dehydrogenase, acid phosphatase and alkaline phosphatase activities were showing in µg TPF formed g−1 of soil h−1 and µg PNP g−1 soil h−1 respectively. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imageAlpha biodiversity with samples and rarefaction curvesIn this segment, by measuring Shannon, Chao1, and observed species metrics, we analyze the microbial diversity within the samples. The chao1 metric measures the richness of the ecosystem, while the Shannon metric is the formula for calculating reported OTU abundances and accounts for both prosperity and equality. The rarefaction curve is provided in Fig. 3 for each metric. Using QIIME software, the metric measurement was done. The impact of both treatments on the microbial complexity and abundance in the sample was also revealed using the Shannon diversity Index (depicting richness and evenness) and Chao 1 representing only richness. Shannon’s diversity index of the bacterial community in the treatment (G1 and G2) was 8.06 and 8.12. The Simpson index in ecology is used to quantify biological diversity in a region, which was also nearly similar in both the treatments. Chao 1 richness estimator showed an increase in species richness. Rarefaction analysis conducted to confirm species richness revealed a difference in the number of reads and OTUs between the samples. The Rare fraction curve had a similar pattern for both samples and showed an impact on the bacterial population in the experiment (Fig. 3a–c).Figure 3Shanon (a), Chao1 (b) curves and observed species (c) obtained for the samples (G1 and G2).Full size imageBacterial diversity analysis at phyla levelTaxonomic study of the 16S rRNA gene amplicon reads yielded seven classifiable bacterial phyla. Six phyla, namely Acidobacteria, Actinobacteria, Bacteroides, Proteobacteria, Firmicutes, and Chloroflexi were dominant in both the systems. The Organically treated soil (G1) sample harbored a higher percentage of Bacteroidetes (14.55%), Actinobacteria (7.45%), and Proteobacteria (10.82%) as compared to conventional treatment (G2) 8.98%, 5.71%, and 6.64%, respectively. However, phylum Acidobacteria(13.6%), Firmicutes(4.84%), and Chloroflexi (2.56) were higher abundance in conventional treatment as compared to the organic treatment, which showed the same phyla with lesser quantity, i.e., 5.63%, 0.91%, and 0.79% respectively (Fig. 4a).Figure 4Comparative microbiome (a-phylum and b-order) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageDistribution of bacterial community at order levelThe bacterial orders in both systems were diversified. The most abundant orders in organic and conventional systems were Chitinophagales (Organic-11.32%, Conventional-43%), Elev-16S-573 (Organic-3.09%, Conventional-8.69%), Pedosphaerales (Organic-1.56%, Conventional-3.55%), Opitutales (Organic-2.46%, Conventional-0.27%), Chthoniobacterales (Organic-1.35%, Conventional-2.84%), Bacillales (Organic-0.91%, Conventional-4.84%) and Solibacterales (Organic-1.39%, Conventional-2.26%) (Fig. 4b).Bacterial community distribution at family levelBacterial family members were identified and enriched including Pedosphaeraceae (O-1.56%, C-3.55%), Opitutaceae (O-2.46%, C-0.27%), Chthoniobacteraceae (O-1.03%, C-2.68%), Steroidobacteraceae (O-2.05%, C-0.73%), Bacillaceae (O-0.77%, C-4.55%), Chitinophagaceae (O-10.99%, C-5.06%), and Xanthomonadaceae (O-1.39%, C-0.06%) and other families (Fig. 5a).Figure 5Comparative microbiome (a-family and b-genus) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageBacterial community distribution at the genus levelComparative abundance of unidentified genus in organic system were uncultured soil bacterium, Glycomyces, Chitinophaga, Lysobacter, Udaeobacter, Bacillus (not detected, 1.85%, 4.77%, 1.19%,1.03% and 0.75% respectively) whereas same genus-group were observed in conventional system with different percentage i.e., 0.11%, not detected, 0.56%, 0.04%, 2.67%, 4.54% respectively (Fig. 5b).Bacterial communities at species levelBecause most of the species were unidentified and uncultured bacterium based on relative abundance, they could not be assigned a species name in either sample. Few species are identified in both systems, like Sphingomonas sp. (O-1.57%, C-1.05%), Bacillus drentensis (O-0.25%, C-2.65%), and Chitinophaga sp. (O-4.64%, C-0.11%) (Fig. 6).Figure 6Comparative microbiome (Species) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 regions) approach.Full size imageHeat map and PCA analysisUnder long-term exposure of organic and conventional treatments, a microbial shift was observed in the rhizosphere microbiome of mango orchards. Based on percent abundance, nine different microbial genera Acidobacteria, Actinobacteria, Bacteroidetes and Proteobacteria formed Cluster I. While, Firmicutes, Chloroflexi and Opitutales were abundances in cluster II. Cluster III includes Chitinobacterales, Bacillales, Chitinophagarales and Otherales genera. Whereas cluster IV (Elev7-16S-573, Otherales, Solibacterales and Pedobacteriaceae), cluster V (Opitutaceae, Chitnobacteraceae, Bacillaceae, Chitinophagaceae and Otherales), cluster VI (Xanthomonadaceae, Uncultured soil bacterium, Candidatus-Udaeobacter, Lysobacter and Bacillus), cluster VII (Chitinophaga, Glycomyces and Other), cluster VIII (Uncultured bacterium and Others) and cluster IX (Bacillus drentensis and Others) (Fig. 7). The cluster I observed with the highest abundance was closely related to clusters II and III. Cluster IV to IX created large groups and is distantly related to cluster I to III of the microbial groups in organic and conventional systems (Fig. 7). In the organic system (G1), microbial groups like Proteobacteria, Actinobacteria, Bacteroidetes, and Opitutaceae were largely dominated and provided benefits to the mango rhizosphere in terms of nutrient availability, plant growth promotion, and protection against biotic and abiotic stress. Phylum Proteobacteria and Actinobacteria are closely linked with the rhizosphere and identified as potential PGPR. Acidobacteria and firmicutes, on the other hand, were dominated primarily by conventional systems and serve as a bio-indicator of anthropogenic stress caused by excessive chemical fertilizer application. Undefined Acidobacteria is oligotrophic in nature and considered as an indicator of low organic carbon and acidic environment. To desire higher productivity, the indiscriminate use of chemical fertilizers or pesticides in conventional systems leads to low nutrient availability, microbial shift, less PGPR, and developing the environment for Acidobacteria, Firmicutes and Chloroflexia group of microorganisms. Principal component analysis (PCA) was performed for both systems (organic-component 1; conventional-component 2). The total variables of principal component analysis were the percentage of different parameters such as alkaline phosphatase, acid phosphatase, DHA, Acetylene reduction assay (ARA1, ARA2, ARA3), and CFU mL−1 (bacteria and fungi). The results of PCA yielded two components that explained 100% of the total variance in the data and had an Eigen value of 6.1 for component 1. In contrast, 1.8 for component 2 and together they described 100% of the total variance in the data (Fig. 8). In the organic system, the loading factor with score plot indicates that component-1 is positively associated with DHA, ARA1, ARA2, alkaline phosphatase, acid phosphatase while negatively correlated with CFU ARA3 activity. Component-1 explains the 76.42% variance of the experimental data, while component-2 explains 23.58%. The second component (PC2) represents the positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU while negatively correlated with alkaline phosphatase and acid phosphatase. In the conventional system, the loading factor with score plot indicates that component-1 is positively associated with single variable acid phosphatise while negatively correlated with DHA, ARA1, ARA2, ARA3, CFU, and alkaline phosphatase activity. The second component (PC2) of the conventional system showed positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU, while the negative association with alkaline phosphatase and acid phosphatase.Figure 7Comparative (G1 organic and G2 conventional) heat map of dominant microbial diversity and their clusters in terms of T1 (phylum), T2 (order), T3 (family), T4 (Genus) and T5 (Species).Full size imageFigure 8PCA analysis of different parameters for organic and conventional systems.Full size image More

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Influence of soil organic matter on zeta potentialIn this study, the zeta potential of a clayey red soil was compared among 4 types of long-term treatments including manure, NPK + straw, NPK and CK in a subtropical climate. Generally, the manure treatment which also had the greatest concentration of SOC resulted in the highest clay zeta potential (less intense charge imbalance), while NPK + straw did not result in the second highest zeta potential as expected compared to the NPK and CK treatments. Variation in clay zeta potential among types of fertilization might be related with their different SOM content, because SOM had an influence on the zeta potentials via affecting the negative charges of soils19. The zeta potential of manure and NPK + straw treatments having high SOC agreed with earlier studies in Marchuk et al.9 that decreases of SOC via NaOH treatments decreased the negative zeta potential value9, where Claremont soil originally having high SOC (2.2%) displayed a greater degree of decline in negative zeta potential (from − 29 to − 34.9 mV) than Urrbrae having lower SOC (1.4%) (− 66.3 to − 68 mV). However, zeta potential in water dispersible clay responded to SOC contrastly in the study of Melo et al.12 , where Londrina soil with high SOC (5–20 g kg−1) displayed lower negative zeta potential values in water dispersible clay than that in Rondon soil (SOC 5 to 12 g kg−1) in subtropical Brazil.Differences of SOC effect on zeta potential in our study and other studies were probably because ionic strength in bulk solution also affected the intensity of soil charge imbalance. Generally, in tropical and subtropical Ferralsols, high amounts of SOM that was released following the breakdown of macroaggregate provided an excess of negative charges and intensified the imbalance in charge, resulting in more negative in zeta potential of clay12. In contrast to Ferralsols in Brazil, red soil (highly-weathered) in our study showed higher negative zeta potential in manure soils with higher SOM. This was because high ionic strength in bulk solution might counterbalance the negative charges from SOM, and attenuated the imbalance in charges. Hence, manure treatment which provided greater EC and Ca2+, Mg2+ concentration and possibly higher ionic strength was reasonable to allow for more charge balance and greater negative zeta potential values than other treatment.In this study, NPK + straw treatment exhibited similar negative zeta potential values as that in NPK but slightly lower than manure, probably due to the effect of SOM functional group from straw and soil solution concentration. Straw can increase the humin content as reported in the study of Sheng et al.11, and then a decrease of negative zeta potential can be induced as addition of humic acid on a Luvisol20. But the negative humic effect from straw on zeta potential was probably stronger than the positive effect from the increased bulk soil solution concentration in NPK + straw relative to NPK in Fig. 3 where increase of bulk solution concentration was found to increase the negative charge numbers and the negative zeta potential in Ultisol and Oxisol15. Therefore, our hypothesis that organic treatments decreased negative zeta potential value of soil was not supported for manure treatment, but was for NPK + straw treatment.NPK + straw’s similar effect on negative zeta potential as NPK treatment was probably also related with their similar pH values. The effect of pH on the potential of clay surfaces can be related to the amount of variable charge on the external surface of the clay particles. Negative zeta potential decreased with rising pH of the solution due to deprotonation of the functional groups on the surface of the organic matter and Fe/Aloxides in NPK + straw treated soils. An increase of soil pH (from 3.5 to 7.5) influenced zeta potential through production of more negative net surface charges on soils in subtropical Australia21,22. Therefore, the pH in our study after KCl adjustment that showed a first increase and then decrease pattern with the increase of concentration, can help to explain the bell shape pattern of negative zeta potential (first decrease and then increase). However, in our study, the pH pattern with increment of KCl concentration was different from the results in study of Yu et al.8 where a continuous decline pattern in pH of two soils (Vertisol and Ultisol) was reported when the KCl concentration increased from 10–5 to 10–1 mol L−1. This is probably because the Ultisol possessed high amount of variable charges from Fe or Al oxides, which resulted in the diffusion layer attracted more positive charged cations (i.e. K+) from bulk solution to balance the increased negative charge on the surface of colloidal particles in order to maintain the electrical neutrality of the system15. This indicated that when KCl concentration was low, between 0 and 10–2 mol L−1, part of K+ was attracted to the diffuse double layer and the remaining K+ hydration allowed for raising in soil pH. When KCl concentration was beyond 10–2 mol L−1, many Al3+ions on soil exchange site were released into solution (0.03 to 0.12 mg L−1) through K+ exchange and probably dropped soil pH (data not shown).Studies also found that the effect of SOM on zeta potential of clay also varied for soils in different climate. Yu et al.8 compared rice straw incorporation effect on two soils (Ultisol and Vertisol) and found that similar SOC content resulted in contrasting effects on surface potential of two types of soils, where surface potential of Ultisol continuously increased while firstly increased and became stable for Vertisol with increase of treated solution concentration. Different SOM effect on soil potential properties of two soils were probably associated with presence of soil variable charges in Ultisol23. SOM and Fe/Al (hydro)oxides in Ultisol carried a larger number of variable surface charges, and resulted in a strong overlapping of oppositely charged electric double layers (EDLs) between SOM and Fe/Al (hydro)oxides at low concentration8. The overlapping of oppositely charged EDLs between SOM and Fe/Al probably yielded in an increase in negative surface charge for Ultisols compared to Vertisol.Effect of SOM and zeta potential on soil aggregationIncrement in content of SOM after additions of straw or other organic treatments can improve aggregate stability6,24,25. The hydrophobic organic compounds that coated around soil particle can act as nucleus of aggregate formation and reduce the destruction effect from water infiltration26,27. The hydrophobic-C/hydrophilic-C increased from 1.04 to 1.07, from 1.22 to 1.27 for chicken manure and maize residues treatments, respectively, when soil water conditions changed from water deficiency to natural rainfall treatment28. This indicated that a small change of hydrophobic-C/hydrophilic-C might result in substantial change in soil water, which was a critical factor of aggregate development28. Xue et al.24 also reported that a small difference of aromatic percentage between tillage + straw and no tillage + straw treatments resulted in significant differences for aggregate ( > 0.25 mm). Hence, small variation in soil hydrophobic-C groups can yield in soil aggregate variation. In our study, the manure treatment, which had higher SOM and hydrophobic-C (aromatic C) while lower hydrophilic-C than other treatments, was probably reasonable to yield in its higher stability than others. In these previous studies, the positive effect of SOM on soil aggregate development was attributed to the increment in van der Waals force between soil particles. However, different from our study, Melo et al.12 reported that Londrina soil with high SOC released greater water dispersible clay (60–80%) than that in Rondon with low SOC (50–70%) after mechanical breakdown of macroaggregate. This was probably due to the repulsive force prevailing attractive force between soil particles as affected by more negative zeta potential or surface potential8.Clay zeta potential influenced the powerful electrostatic fields, soil internal forces and aggregate stability9. Decrease in negative clay zeta potential mainly yielded an increase in the soil microaggregate portion ( More

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Altogether, we found that, while traffic volume declined by  > 7% during the pandemic year (with a maximum monthly decline of nearly 40%), the absolute number of annual WVCs was largely unchanged. This resulted in significant increases of  > 8% in collision rates between vehicles and wildlife during the pandemic year, peaking at a  > 27% nationwide increase in April 2020. Other studies from the first several months of the pandemic documented similar transient declines in the number of WVCs when the pandemic began which then reversed in many jurisdictions as the pandemic progressed and traffic rebounded26,27. We observed a similar pattern over the first five months of the pandemic at the national scale (Fig. 2): WVCs initially declined during the pandemic in step with declines in traffic volume, but then started to increase to baseline levels at a faster rate than traffic, possibly due to behavioral lags by wildlife following traffic-mediated increases in wildlife road use. Though based on coarse-scale data, our research aligns with assertions from studies during27 and prior to the pandemic3,15,16,28,29 that the relationship between traffic volume and WVCs is non-linear.We postulate that the observed non-linear relationship between traffic volume and WVCs is the result of greater use of roads and roadsides by certain wildlife species, namely large mammals (Table S1), in response to decreasing traffic volume, as prior research has suggested3,14,15,16. This explanation is consistent with accounts of various wildlife species making increased use of human spaces during the pandemic17,20,21: with less cars on the roads, wildlife might be less deterred from roads by the noise and light pollution that accompany high traffic volumes9,10,11,20 and perceive roads as less risky, thereby increasing their willingness to attempt road crossings3,8,15,16. Beyond incidentally crossing roads while moving about the landscape8,9, wildlife might be attracted to roads for travel, mates, or other resources8,10,11. Many animals are shown to utilize roads to move efficiently across the landscape11,12, and roads and the surrounding areas are comparatively open, such that wildlife might select roads and roadsides for enhanced visibility to find mates, detect predators, or locate prey10,13. Roadsides also can provide foraging opportunities and essential nutrients for wildlife via abundant, high-quality early successional vegetation and high salt concentrations10,11. As such, decreased road traffic during the pandemic might have caused certain wildlife species to tolerate the risks associated with roads in order to access the benefits of roads and roadsides.An alternative explanation for the observed increases in collision rates is that human driving behavior, rather than animal behavior, changed during the pandemic. With fewer cars on the road, people might drive faster35, rendering it more difficult for both humans and wildlife to avoid collisions3. Preliminary studies from throughout the United States have indeed suggested changes to human driving behavior during the pandemic, with several jurisdictions reporting increased vehicle speeds35,36. Despite reported increases in vehicle speeds, however, the total number of vehicle collisions (the sum of both wildlife and non-wildlife collisions) mirrored trends in traffic volume and declined considerably during the pandemic37,38. Thus, because changes to human behavior appear to have had a minimal effect on vehicle collisions overall, it is unlikely that the observed changes in collision rates are due to increased vehicle speeds alone. Still, we cannot discount the possibility that changes to human driving behavior contributed to the patterns documented here, and future work should more explicitly test the relative effects of changes in traffic volume on both human driving behavior and wildlife space-use, as well as the resultant impacts on WVCs.A greater understanding of human driving behavior would also help explain our findings regarding changes in traffic patterns during the pandemic. Nationwide, the severity of COVID-19 restrictions accounted for a large amount of the variation in changes in monthly traffic volume (R2 = 0.968), but the severity of restrictions was less influential on changes in yearly traffic across states (Tables S3 and S4). Restrictions implemented throughout the pandemic were largely enacted for the purpose of minimizing travel, and other research has demonstrated that these restrictions were effective at reducing human mobility18,21. Our state-level findings, however, imply that it was not only the restrictions themselves that reduced travel, but possibly also the associated anxiety regarding the risk of contracting the SARS-CoV-2 virus, as has been suggested in other studies21,22,23,24; although we observed the greatest declines in traffic volume early in the pandemic (Fig. 2A) when restrictions were most stringent (Fig. S2)21, there was widespread anxiety about the risks posed by SARS-CoV-2 during this time22,23, which likely motivated people to stay home independent of restrictions24. Indeed, anxiety and risk perception might explain the relationship between traffic volume and the other covariates in our top models (Table S4). Declines in traffic were greatest in the most densely populated states (Fig. 4A) and in states that had the highest and the lowest disease burdens (Fig. 4B). The risk of SARS-CoV-2 transmission is greater in more densely populated states due to the close proximity of and frequent interactions amongst people21. As such, people may have altered their road use more in densely populated states as compared to sparsely populated ones due to differing perceptions of disease transmission risk23—though differences in infrastructure in relation to population density likely contributed to this pattern as well39. Similarly, declines in traffic volume in states with larger outbreaks of SARS-CoV-2 might have been driven by increases in the perceived risk of contracting the virus21,23. Alternatively, traffic reductions in states with low disease burdens might reflect increased compliance with stay-at-home orders, and therefore less opportunity for disease spread40,41; essentially, reductions in traffic volume might be the cause of locally low disease burdens therein, rather than a consequence. Altogether, we posit that the observed heterogeneity in traffic volume between states is, at least in part, attributed to differences in the perceived risk posed by the SARS-CoV-2 virus.Regardless of the mechanisms underlying changes in traffic volume and WVCs, our observation that the annual number of WVCs was largely unchanged despite substantive declines in traffic volume has implications for mitigating WVCs going forward. Most directly, the lack of a directional change in WVCs suggests that road traffic levels in the United States are currently such that even large decreases in traffic volume would have minimal long-term effects on the absolute number of WVCs. As such, decreasing collisions by reducing traffic volume would require even larger and longer-lasting changes in traffic than those observed during the pandemic. Since such massive and sustained reductions in traffic are unlikely4,5,6, WVCs in the United States essentially represent a fixed cost as of now, both for human society and wildlife populations. As such, these transient decreases in traffic likely provided minimal reprieve to large mammals from collision-induced mortality, in contrast to speculation that changes in human mobility during the COVID-19 pandemic had substantial positive effects for wildlife populations by freeing wildlife from the pervasive direct and indirect effects of humans17,18,19,20,26,27,42.Indeed, it is possible that short-term decreases in traffic volume might ultimately be harmful to those wildlife species that increased their road use. Although the increases in collision rates we observed at the beginning of the pandemic were rapid and corresponded to nationwide declines in traffic volume (see also26,27), collision rates remained elevated even as traffic approached baseline levels in July (Fig. 2B). If wildlife responses to changes in traffic are asymmetric (i.e., increases in wildlife road use following declines in traffic occur more rapidly than decreases in wildlife road use in response to increased traffic), then short-term declines in traffic volume might lead to net increases in the number WVCs over longer timeframes, ultimately proving detrimental to certain wildlife populations1,3. Future work should evaluate the long-term effects of the pandemic on wildlife populations, specifically with regards to collision-induced mortality17,20,26,27,42.Although the COVID-19 pandemic provided an opportunity to examine the short-term effects of transient decreases in traffic volume on WVCs, the longer-term effects of expanding human populations, greater road densities, and altogether higher traffic volumes on WVCs are less clear. Similar to the increases in wildlife road use in response to decreases in traffic volume theorized here, steady increases in traffic might reduce wildlife road use long-term3,14,15,16; since road traffic is indeed increasing through time4,5,6, we might therefore see declines in WVCs as roads become more effective at repelling wildlife1,3,14. Although these reductions in vehicle-induced wildlife mortality are welcome, this would see roads increasingly serve as barriers to animal movement and gene flow43, further fragmenting already disconnected wildlife populations8. Thus, policy makers and urban planners should invest in infrastructure such as overpasses, underpasses, and fencing that enables wildlife to cross high-traffic roads safely or directs wildlife towards low-risk areas8,9. Even substantive short-term declines in road traffic are not sufficient to mitigate wildlife-vehicle conflict on their own. More