Multiple Sources and Forms of Nitrogen Sustain Year-Round Kelp Growth on the Inner Continental Shelf of the Santa Barbara Channel

Forests of the giant kelp Macrocystis pyrifera found on coastal rocky reefs lack the large reservoirs for nutrient storage found in many terrestrial environments. Supporting their high year-round growth rates requires a continuous supply of nitrogen. Complementary timing of nutrient supply associated with the physical processes that deliver nitrate to reefs largely achieves this goal, but modeling studies indicate that the magnitude of nitrate delivery is inadequate to support the measured nitrogen demand of kelp forests during summer. Ammonium, from sediment efflux and excretion by reef consumers, likely fills the deficit. Together, the varied sources of inorganic nitrogen supplied to kelp forests support their high growth rates throughout the year. Kelp compensates for diminished nitrogen supply during summer by decreasing tissue nitrogen content, resulting in a doubling of kelp C:N ratios

Data Paper. Data Paper

<h2>File List</h2><blockquote> <p>Ascii text, comma delimited. No compression schemes were used. Empty fields are denoted by -99999. </p> <p><a href="M_pyrifera_net_primary_production_and_growth.txt">M_pyrifera_net_primary_production_and_growth.txt</a></p> <p><a href="M_pyrifera_standing_crop_plant_density_and_loss_rates.txt">M_pyrifera_standing_crop_plant_density_and_loss_rates.txt</a></p> <p><a href="Census_of_fronds_on_marked_plants.txt">Census_of_fronds_on_marked_plants.txt</a></p> </blockquote><h2>Description</h2><blockquote> <p>Marine macroalgae are believed to be among the most productive autotrophs in the world. However, relatively little information exists about spatial and temporal variation in net primary production (NPP) by these organisms. The data presented here are being collected to investigate patterns and causes of variation in NPP by the giant kelp, <i>Macrocystis pyrifera,</i> which is believed to be one of the fastest growing autotrophs on earth. The standing crop and loss rates of <i>M. pyrifera</i> are measured monthly in permanent plots at three sites in the Santa Barbara Channel, USA. Collection of these data began in June 2002 and is ongoing. Seasonal estimates of NPP and growth rate are made by combining the field data with a model of kelp dynamics. The purpose of this Data Paper is to make available a time series of <i>M. pyrifera</i> NPP, growth, and standing crop that is appropriate for examining seasonal and interannual patterns across multiple sites. Data on plant density in each plot and censuses of fronds on tagged plants at each site are also made available here. NPP, mass-specific growth rate, and standing crop are presented in four different metrics (wet mass, dry mass, carbon mass, and nitrogen mass) to facilitate comparisons with previous studies of <i>M. pyrifera</i> and with NPP measured in other ecosystems. Analyses of these data reveal seasonal cycles in growth and standing crop as well as substantial differences in <i>M. pyrifera </i>NPP among sites and years.</p> <p><i>Key words</i>: <i>giant kelp; growth rate;</i> Macrocystis pyrifera<i>; marine algae; net primary production; standing crop</i>.</p> </blockquote

Appendix C. Figures showing mean species richness as a function of time span and area for each data set.

Figures showing mean species richness as a function of time span and area for each data set

Appendix A. The scales of equivalence equation.

The scales of equivalence equation

Appendix B. A comparison of AIC values for model selection.

A comparison of AIC values for model selection

A comparison of the species– time relationship across ecosystems and taxonomic groups

The species Á/time relationship (STR) describes how the species richness of a community increases with the time span over which the community is observed. This pattern has numerous implications for both theory and conservation in much the same way as the species Á/area relationship (SAR). However, the STR has received much less attention and to date only a handful of papers have been published on the pattern. Here we gather together 984 community time-series, representing 15 study areas and nine taxonomic groups, and evaluate their STRs in order to assess the generality of the STR, its consistency across ecosystems and taxonomic groups, its functional form, and its relationship to local species richness. In general, STRs were surprisingly similar across major taxonomic groups and ecosystem types. STRs tended to be well fit by both power and logarithmic functions, and power function exponents typically ranged between 0.2 and 0.4. Communities with high richness tended to have lower STR exponents, suggesting that factors increasing richness may simultaneously decrease turnover in ecological systems. Our results suggest that the STR is as fundamental an ecological pattern as the SAR, and raise questions about the general processes underlying this pattern. They also highlight the dynamic nature of most species assemblages, and the need to incorporate time scale in both basic and applied research on species richness patterns

A comparison of the species– time relationship across ecosystems and taxonomic groups

The species Á/time relationship (STR) describes how the species richness of a community increases with the time span over which the community is observed. This pattern has numerous implications for both theory and conservation in much the same way as the species Á/area relationship (SAR). However, the STR has received much less attention and to date only a handful of papers have been published on the pattern. Here we gather together 984 community time-series, representing 15 study areas and nine taxonomic groups, and evaluate their STRs in order to assess the generality of the STR, its consistency across ecosystems and taxonomic groups, its functional form, and its relationship to local species richness. In general, STRs were surprisingly similar across major taxonomic groups and ecosystem types. STRs tended to be well fit by both power and logarithmic functions, and power function exponents typically ranged between 0.2 and 0.4. Communities with high richness tended to have lower STR exponents, suggesting that factors increasing richness may simultaneously decrease turnover in ecological systems. Our results suggest that the STR is as fundamental an ecological pattern as the SAR, and raise questions about the general processes underlying this pattern. They also highlight the dynamic nature of most species assemblages, and the need to incorporate time scale in both basic and applied research on species richness patterns

Long-term ecological research and the COVID-19 anthropause: A window to understanding social-ecological disturbance

https://kent-islandora.s3.us-east-2.amazonaws.com/node/17226/87203-thumbnail.jpgThe period of disrupted human activity caused by the COVID-19 pandemic, coined the “anthropause,” altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long-term social-ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic-related delays in data availability and ecological response lags, we detail three case studies that show how long-term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long-term data in separating disturbance impacts from natural variation and long-term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.</p