Exploring freshwater stream bacterial communities as indicators of land use intensity.

BACKGROUND: Stream ecosystems comprise complex interactions among biological communities and their physicochemical surroundings, contributing to their overall ecological health. Despite this, many monitoring programs ignore changes in the bacterial communities that are the base of food webs in streams, often focusing on stream physicochemical assessments or macroinvertebrate community diversity instead. We used 16S rRNA gene sequencing to assess bacterial community compositions within 600 New Zealand stream biofilm samples from 204 sites within a 6-week period (February-March 2010). Sites were either dominated by indigenous forests, exotic plantation forests, horticulture, or pastoral grasslands in the upstream catchment. We sought to predict each site's catchment land use and environmental conditions based on the composition of the stream bacterial communities. RESULTS: Random forest modelling allowed us to use bacterial community composition to predict upstream catchment land use with 65% accuracy; urban sites were correctly assigned 90% of the time. Despite the variation inherent when sampling across a ~ 1000-km distance, bacterial community data could correctly differentiate undisturbed sites, grouped by their dominant environmental properties, with 75% accuracy. The positive correlations between actual values and those predicted by the models built using the stream biofilm bacterial data ranged from weak (average log N concentration in the stream water, R2 = 0.02) to strong (annual mean air temperature, R2 = 0.69). CONCLUSIONS: Freshwater bacterial community data provide useful insights into land use impacts on stream ecosystems; they may be used as an additional measure to screen stream catchment attributes.

Soil bacterial community composition is more stable in kiwifruit orchards relative to phyllosphere communities over time.

BACKGROUND: Soil and phyllosphere (leaves and fruit) microbes play critical roles in the productivity and health of crops. However, microbial community dynamics are currently understudied in orchards, with a limited number incorporating temporal monitoring. We used 16S rRNA gene amplicon sequencing to investigate bacterial community temporal dynamics and community assembly processes on the leaves and fruit, and in the soil of 12 kiwifruit orchards across a cropping season in New Zealand. RESULTS: Community composition significantly differed (P < 0.001) among the three sample types. However, the communities in the phyllosphere substrates more closely resembled each other, relative to the communities in the soil. There was more temporal stability in the soil bacterial community composition, relative to the communities residing on the leaves and fruit, and low similarity between the belowground and aboveground communities. Bacteria in the soil were more influenced by deterministic processes, while stochastic processes were more important for community assembly in the phyllosphere. CONCLUSIONS: The higher temporal variability and the stochastic nature of the community assembly processes observed in the phyllosphere communities highlights why predicting the responsiveness of phyllosphere communities to environmental change, or the likelihood of pathogen invasion, can be challenging. The relative temporal stability and the influence of deterministic selection on soil microbial communities suggests a greater potential for their prediction and reliable manipulation.

The soil microbiome: An essential, but neglected, component of regenerative agroecosystems.

Regenerative agriculture (RA) is gaining traction globally as an approach for meeting growing food demands while avoiding, or even remediating, the detrimental environmental consequences associated with conventional farming. Momentum is building for science to provide evidence for, or against, the putative ecosystem benefits of RA practices relative to conventional farming. In this perspective article, we advance the argument that consideration of the soil microbiome in RA research is crucial for disentangling the varied and complex relationships RA practices have with the biotic and abiotic environment, outline the expected changes in soil microbiomes under RA, and make recommendations for designing research that will answer the outstanding questions on the soil microbiome under RA. Ultimately, deeper insights into the role of microbial communities in RA soils will allow the development of biologically relevant monitoring tools which will support land managers in addressing the key environmental issues associated with agriculture.

Land-use history impacts spatial patterns and composition of woody plant species across a 35-hectare temperate forest plot.

Land-use history is the template upon which contemporary plant and tree populations establish and interact with one another and exerts a legacy on the structure and dynamics of species assemblages and ecosystems. We use the first census (2010-2014) of a 35-ha forest-dynamics plot at the Harvard Forest in central Massachusetts to describe the composition and structure of the woody plants in this plot, assess their spatial associations within and among the dominant species using univariate and bivariate spatial point-pattern analysis, and examine the interactions between land-use history and ecological processes. The plot includes 108,632 live stems ≥ 1 cm in diameter (2,215 individuals/ha) and 7,595 standing dead stems ≥ 5 cm in diameter. Live tree basal area averaged 42.25 m2/ha, of which 84% was represented by Tsuga canadensis (14.0 m2/ ha), Quercus rubra (northern red oak; 9.6 m2/ ha), Acer rubrum (7.2 m2/ ha) and Pinus strobus (eastern white pine; 4.4 m2/ ha). These same four species also comprised 78% of the live aboveground biomass, which averaged 245.2 Mg/ ha. Across all species and size classes, the forest contains a preponderance (> 80,000) of small stems (<10-cm diameter) that exhibit a reverse-J size distribution. Significant spatial clustering of abundant overstory species was observed at all spatial scales examined. Spatial distributions of A. rubrum and Q. rubra showed negative intraspecific correlations in diameters up to at least a 150-m spatial lag, likely indicative of crowding effects in dense forest patches following intensive past land use. Bivariate marked point-pattern analysis, showed that T. canadensis and Q. rubra diameters were negatively associated with one another, indicating resource competition for light. Distribution and abundance of the common overstory species are predicted best by soil type, tree neighborhood effects, and two aspects of land-use history: when fields were abandoned in the late 19th century and the succeeding forest types recorded in 1908. In contrast, a history of intensive logging prior to 1950 and a damaging hurricane in 1938 appear to have had little effect on the distribution and abundance of present-day tree species. Our findings suggest that current day composition and structure are still being influenced by anthropogenic disturbances that occurred over a century ago.

High light-induced photoinhibition is not limiting seedling establishment at abrupt treeline ecotones in New Zealand.

Seedlings of New Zealand's treeline-forming Fuscospora cliffortioides (Hook.f.) perform poorly beyond the established canopy, limiting treeline advance. To test the long-standing assumption that photoinhibition impairs regeneration in the subalpine belt of New Zealand's Southern Alps, we assessed photosystem II (PSII) performance of seedling-sized individuals and microclimate variation. We performed diurnal, non-invasive chlorophyll-a-fluorescence measurements on exposed and canopy-sheltered individuals at two sites in New Zealand's Southern Alps during summer and winter. Diurnal recordings of the effective (ΦPSII) and optimal (Fv/Fm) photosynthetic quantum yield were supplemented with light response curves and micro-temperature recordings. ΦPSII returned to near-optimal values around 0.8 after 30 min of shading, which rules out accumulative or long-term photoinhibition effects. The maximum electron transport rate derived from rapid light curves was significantly higher (+12%) in exposed compared with canopy-shaded individuals. Summer temperature fluctuated widely on the scree (-0.5 to 60.5 °C) and near seedlings (-2 to 26.5 °C). Our results revealed a remarkable level of light adaptation and contradict previous studies hinting at high light-induced photoinhibition as a treeline-limiting factor in the Southern Alps. By linking low ΦPSII on winter mornings, and large, sudden temperature drops in summer, we suspect that cold-induced photoinhibition might occur but the rapid recovery of ΦPSII seen across a wide temperature range makes lethal photo-oxidative damage rather unlikely. Given the demonstrably low summer frost tolerance of F. cliffortioides, cold-related damage resulting from frost events during the growing season or embolism induced by frost drought may offer more plausible explanations for the poor seedling establishment. Duration and frequency of these events could diminish with global warming, which may promote treeline advance.

Measuring change in biological communities: multivariate analysis approaches for temporal datasets with low sample size.

Effective and robust ways to describe, quantify, analyse, and test for change in the structure of biological communities over time are essential if ecological research is to contribute substantively towards understanding and managing responses to ongoing environmental changes. Structural changes reflect population dynamics, changes in biomass and relative abundances of taxa, and colonisation and extinction events observed in samples collected through time. Most previous studies of temporal changes in the multivariate datasets that characterise biological communities are based on short time series that are not amenable to data-hungry methods such as multivariate generalised linear models. Here, we present a roadmap for the analysis of temporal change in short-time-series, multivariate, ecological datasets. We discuss appropriate methods and important considerations for using them such as sample size, assumptions, and statistical power. We illustrate these methods with four case-studies analysed using the R data analysis environment.

Changes in the analysis of temporal community dynamics data: a 29-year literature review.

BACKGROUND: Understanding how biological communities change over time is of increasing importance as Earth moves into the Anthropocene. A wide variety of methods are used for multivariate community analysis and are variously applied to research that aims to characterise temporal dynamics in community composition. Understanding these methods and how they are applied is useful for determining best practice in community ecology. METHODOLOGY: We reviewed the ecological literature from 1990 to 2018 that used multivariate methods to address questions of temporal community dynamics. For each paper that fulfilled our search criteria, we recorded the types of multivariate analysis used to characterise temporal community dynamics in addition to the research aim, habitat type, location, taxon and the experimental design. RESULTS: Most studies had relatively few temporal replicates; the median number was seven time points. Nearly 70% of studies applied more than one analysis method; descriptive methods such as bar graphs and ordination were the most commonly applied methods. Surprisingly, the types of analyses used were only related to the number of temporal replicates, but not to research aim or any other aspects of experimental design such as taxon, or habitat or year of study. CONCLUSIONS: This review reveals that most studies interested in understanding community dynamics use relatively short time series meaning that several, more sophisticated, temporal analyses are not widely applicable. However, newer methods using multivariate dissimilarities are growing in popularity and many can be applied to time series of any length.

Using soil bacterial communities to predict physico-chemical variables and soil quality.

BACKGROUND: Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial to ensure the sustainability of land management practices. We applied 16S rRNA gene sequencing to determine the bacterial community composition of over 3000 soil samples from 606 sites in New Zealand. Sites were classified as indigenous forests, exotic forest plantations, horticulture, or pastoral grasslands; soil physico-chemical variables related to soil quality were also collected. The composition of soil bacterial communities was then used to predict the land use and soil physico-chemical variables of each site. RESULTS: Soil bacterial community composition was strongly linked to land use, to the extent where it could correctly determine the type of land use with 85% accuracy. Despite the inherent variation introduced by sampling across ~ 1300 km distance gradient, the bacterial communities could also be used to differentiate sites grouped by key physico-chemical properties with up to 83% accuracy. Further, individual soil variables such as soil pH, nutrient concentrations and bulk density could be predicted; the correlations between predicted and true values ranged from weak (R2 value = 0.35) to strong (R2 value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50-95% accuracy. CONCLUSIONS: The inclusion of biological information when monitoring soil quality is crucial if we wish to gain a better, more accurate understanding of how land management impacts the soil ecosystem. We have shown that soil bacterial communities can provide biologically relevant insights on the impacts of land use on soil ecosystems. Furthermore, their ability to indicate changes in individual soil parameters shows that analysing bacterial DNA data can be used to screen soil quality. Video Abstract.

Connecting through space and time: catchment-scale distributions of bacteria in soil, stream water and sediment.

Terrestrial and aquatic environments are linked through hydrological networks that transport abiotic components from upslope environments into aquatic ecosystems. However, our understanding of how bacteria are transported through these same networks is limited. Here, we applied 16S rRNA gene sequencing to over 500 soil, stream water and stream sediment samples collected within a native forest catchment to determine the extent to which bacterial communities in these habitats are connected. We provide evidence that while the bacterial communities in each habitat were significantly distinct from one another (PERMANOVA pairwise P < 0.001), the bacterial communities in soil and stream samples were weakly connected to each other when stream sediment sample locations were downhill of surface runoff flow paths. This pattern decreased with increasing distance between the soil and sediment samples. The connectivity between soil and stream water samples was less apparent and extremely transient; the greatest similarity between bacterial communities in soil and stream water overall was when comparing stream samples collected 1 week post soil sampling. This study shows how bacterial communities in soil, stream water and stream sediments are connected at small spatial scales and provides rare insights into the temporal dynamics of terrestrial and aquatic bacterial community connectivity.

Following Rapoport's Rule: the geographic range and genome size of bacterial taxa decline at warmer latitudes.

We sought to test whether stream bacterial communities conform to Rapoport's Rule, a pattern commonly observed for plants and animals whereby taxa exhibit decreased latitudinal range sizes closer to the equator. Using a DNA sequencing approach, we explored the biogeography of biofilm bacterial communities in 204 streams across a ∼1000 km latitudinal gradient. The range sizes of bacterial taxa were strongly correlated with latitude, decreasing closer to the equator, which coincided with a greater than fivefold increase in bacterial taxonomic richness. The relative richness and range size of bacteria were associated with spatially correlated variation in temperature and rainfall. These patterns were observed despite enormous variability in catchment environmental characteristics. Similar results were obtained when restricting the same analyses to native forest catchments, thereby controlling for spatial biases in land use. We analysed genomic data from ∼500 taxa detected in this study, for which data were available and found that bacterial communities at cooler latitudes also tended to possess greater potential metabolic potential. Collectively, these data provide the first evidence of latitudinal variation in the range size distributions of freshwater bacteria, a trend which may be determined, in part, by a trade-off between bacterial genome size and local variation in climatic conditions.

Bacteria as Emerging Indicators of Soil Condition.

Bacterial communities are important for the health and productivity of soil ecosystems and have great potential as novel indicators of environmental perturbations. To assess how they are affected by anthropogenic activity and to determine their ability to provide alternative metrics of environmental health, we sought to define which soil variables bacteria respond to across multiple soil types and land uses. We determined, through 16S rRNA gene amplicon sequencing, the composition of bacterial communities in soil samples from 110 natural or human-impacted sites, located up to 300 km apart. Overall, soil bacterial communities varied more in response to changing soil environments than in response to changes in climate or increasing geographic distance. We identified strong correlations between the relative abundances of members of Pirellulaceae and soil pH, members of Gaiellaceae and carbon-to-nitrogen ratios, members of Bradyrhizobium and the levels of Olsen P (a measure of plant available phosphorus), and members of Chitinophagaceae and aluminum concentrations. These relationships between specific soil attributes and individual soil taxa not only highlight ecological characteristics of these organisms but also demonstrate the ability of key bacterial taxonomic groups to reflect the impact of specific anthropogenic activities, even in comparisons of samples across large geographic areas and diverse soil types. Overall, we provide strong evidence that there is scope to use relative taxon abundances as biological indicators of soil condition. IMPORTANCE: The impact of land use change and management on soil microbial community composition remains poorly understood. Therefore, we explored the relationship between a wide range of soil factors and soil bacterial community composition. We included variables related to anthropogenic activity and collected samples across a large spatial scale to interrogate the complex relationships between various bacterial community attributes and soil condition. We provide evidence of strong relationships between individual taxa and specific soil attributes even across large spatial scales and soil and land use types. Collectively, we were able to demonstrate the largely untapped potential of microorganisms to indicate the condition of soil and thereby influence the way that we monitor the effects of anthropogenic activity on soil ecosystems into the future.

Increased stem density and competition may diminish the positive effects of warming at alpine treeline.

The most widespread response to global warming among alpine treeline ecotones is not an upward shift, but an increase in tree density. However, the impact of increasing density on interactions among trees at treeline is not well understood. Here, we test if treeline densification induced by climatic warming leads to increasing intraspecific competition. We mapped and measured the size and age of Smith fir trees growing in two treelines located in the southeastern Tibetan Plateau. We used spatial point-pattern and codispersion analyses to describe the spatial association and covariation among seedlings, juveniles, and adults grouped in 30-yr age classes from the 1860s to the present. Effects of competition on tree height and regeneration were inferred from bivariate mark-correlations. Since the 1950s, a rapid densification occurred at both sites in response to climatic warming. Competition between adults and juveniles or seedlings at small scales intensified as density increased. Encroachment negatively affected height growth and further reduced recruitment around mature trees. We infer that tree recruitment at the studied treelines was more cold-limited prior to 1950 and shifted to a less temperature-constrained regime in response to climatic warming. Therefore, the ongoing densification and encroachment of alpine treelines could alter the way climate drives their transitions toward subalpine forests.

Using codispersion analysis to quantify and understand spatial patterns in species-environment relationships.

The analysis of spatial patterns in species-environment relationships can provide new insights into the niche requirements and potential co-occurrence of species, but species abundance and environmental data are routinely collected at different spatial scales. Here, we investigate the use of codispersion analysis to measure and assess the scale, directionality and significance of complex relationships between plants and their environment in large forest plots. We applied codispersion analysis to both simulated and field data on spatially located tree species basal area and environmental variables. The significance of the observed bivariate spatial associations between the basal area of key species and underlying environmental variables was tested using three null models. Codispersion analysis reliably detected directionality (anisotropy) in bivariate species-environment relationships and identified relevant scales of effects. Null model-based significance tests applied to codispersion analyses of forest plot data enabled us to infer the extent to which environmental conditions, tree sizes and/or tree spatial positions underpinned the observed basal area-environment relationships, or whether relationships were a result of other unmeasured factors. Codispersion analysis, combined with appropriate null models, can be used to infer hypothesized ecological processes from spatial patterns, allowing us to start disentangling the possible drivers of plant species-environment relationships.

Using codispersion analysis to characterize spatial patterns in species co-occurrences.

Visualizing and quantifying spatial patterns of co-occurrence (i.e., of two or more species, or of species and underlying environmental variables) can suggest hypotheses about processes that structure species assemblages and their relevant spatial scales. Statistical models of spatial co-occurrence generally assume that underlying spatial processes are isotropic and stationary, but many ecologically realistic spatial processes are anisotropic and non-stationary. Here, we introduce codispersion analysis to ecologists and use it to detect and quantify anisotropic and nonstationary patterns and their relevant spatial scales in bivariate co-occurrence data. Simulated data illustrated that codispersion analysis can accurately characterize complex spatial patterns. Analysis of co-occurrence of common tree species growing in a 35-ha plot revealed both positive and negative codispersion between different species; positive codispersion values reflected positive correlation in species abundance (aggregation), whereas negative codispersion values reflected negative correlation in species abundance (segregation). Comparisons of observed patterns with those simulated using two different null models showed that the codispersion of most species pairs differed significantly from random expectation. We conclude that codispersion analysis can be a useful exploratory tool to guide ecologists interested in modeling spatial processes.

Can ecosystem-scale translocations mitigate the impact of climate change on terrestrial biodiversity? Promises, pitfalls, and possibilities: Ecosystem-scale translocations.

Because ecological interactions are the first components of the ecosystem to be impacted by climate change, future forms of threatened-species and ecosystem management should aim at conserving complete, functioning communities rather than single charismatic species. A possible way forward is the deployment of ecosystem-scale translocation (EST), where above- and below-ground elements of a functioning terrestrial ecosystem (including vegetation and topsoil) are carefully collected and moved together. Small-scale attempts at such practice have been made for the purpose of ecological restoration. By moving larger subsets of functioning ecosystems from climatically unstable regions to more stable ones, EST could provide a practical means to conserve mature and complex ecosystems threatened by climate change. However, there are a number of challenges associated with EST in the context of climate change mitigation, in particular the choice of donor and receptor sites. With the aim of fostering discussion and debate about the EST concept, we  1) outline the possible promises and pitfalls of EST in mitigating the impact of climate change on terrestrial biodiversity and 2) use a GIS-based approach to illustrate how  potential source and receptor sites, where EST could be trialed and evaluated globally, could be identified.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand's southern beech treelines.

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments.

Fine-scale spatial patterns in bacterial community composition and function within freshwater ponds.

The extent to which non-host-associated bacterial communities exhibit small-scale biogeographic patterns in their distribution remains unclear. Our investigation of biogeography in bacterial community composition and function compared samples collected across a smaller spatial scale than most previous studies conducted in freshwater. Using a grid-based sampling design, we abstracted 100+ samples located between 3.5 and 60 m apart within each of three alpine ponds. For every sample, variability in bacterial community composition was monitored using a DNA-fingerprinting methodology (automated ribosomal intergenic spacer analysis) whereas differences in bacterial community function (that is, carbon substrate utilisation patterns) were recorded from Biolog Ecoplates. The exact spatial position and dominant physicochemical conditions (for example, pH and temperature) were simultaneously recorded for each sample location. We assessed spatial differences in bacterial community composition and function within each pond and found that, on average, community composition or function differed significantly when comparing samples located >20 m apart within any pond. Variance partitioning revealed that purely spatial variation accounted for more of the observed variability in both bacterial community composition and function (range: 24-38% and 17-39%) than the combination of purely environmental variation and spatially structured environmental variation (range: 17-32% and 15-20%). Clear spatial patterns in bacterial community composition, but not function were observed within ponds. We therefore suggest that some of the observed variation in bacterial community composition is functionally 'redundant'. We confirm that distinct bacterial communities are present across unexpectedly small spatial scales suggesting that populations separated by distances of >20 m may be dispersal limited, even within the highly continuous environment of lentic water.