High N Storage but Low N Recovery After Long-Term N-Fertilization in a Subtropical Cunninghamia lanceolata Plantation Ecosystem: A 14-Year Case Study.

Forests are among the most important N pools of all terrestrial ecosystems. Elevated atmospheric N deposition in recent decades has led to increased interest in the influences of N application on forest N cycles. However, accurate assessments of N storage in forest ecosystems remain elusive. We used a 14-year experiment of a Chinese fir [Cunninghamia lanceolata (Lamb.) Hook] plantation to explore how long-term N fertilization affected N storage and recovery rates. Our study plots were located in a field that had been continuously fertilized over 14 years (2004-2017) with urea at rates of 0 (N0, control), 60 (N60, low-N), 120 (N120, medium-N), and 240 (N240, high-N) kg N hm-2a-1. Data were collected that included N content and biomass in the understory, litter, and various plant organs (i.e., leaves, branches, stems, roots, and bark), as well as soil N content and density at different depths. Results showed that the total ecosystem N storage in the N-fertilized plots was 1.1-1.4 times higher than that in the control plots. About 12.36% of the total ecosystem N was stored in vegetation (plant organs, litter, and understory) and 87.64% was stored in soil (0-60 cm). Plant organs, litter, and soil had higher N storage than the understory layer. Significantly higher plant N uptake was found in the medium-N (1.2 times) and high-N (1.4 times) treatments relative to the control. The N recovery rate of the understory layer in the N-fertilized treatments was negative and less than that in the control. Application of long-term N fertilizer to this stand led to a low N recovery rate (average 11.39%) and high loss of N (average 91.86%), which indicate low N use efficiency in the Chinese fir plantation ecosystem. Our findings further clarify the distribution of N in an important terrestrial ecosystem and improve our understanding of regional N cycles.

C:N:P stoichiometry in terrestrial ecosystems in China.

Ecological stoichiometry is an efficient tool for exploring the balance and cycling of coupled elements (e.g., carbon [C], nitrogen [N], and phosphorus [P]). Therefore, C:N:P ratios are essential input parameters in most ecological models of productivity or C cycling. However, previous C:N:P ratios estimated using the species arithmetic means exhibit high uncertainty when used as direct model parameters. In this study, we comprehensively calculated C:N:P ratios from organs to ecosystems for 66 typical natural ecosystems in China (e.g., forests, grasslands, and deserts) using the community biomass-weighted mean (CWM), with the consistently measured element data of 3229 site-species combination. The C:N:P ratios were 427:19:1, 885:13:1, 9549:33:1, and 797:18:1 in the leaves, branches, trunks, and roots of terrestrial ecosystems, respectively. Furthermore, the ratios were 91:4:1, 919:17:1, 1121:25:1, and 55:4:1 in ecosystems, plant communities, litter, and soils, respectively. Significant differences were observed in C:N:P ratios among different ecosystem types and biomes, with generally higher ratios in forests. Moreover, the latitudinal patterns of C:N ratios exhibited no obvious trends, whereas both C:P and N:P ratios decreased significantly with increasing latitude, especially in forests. Environmental conditions explained 15.4-86.6% of the spatial variation of C:N:P ratios from organs to ecosystems. In summary, this study systematically demonstrates the variations in biome-scale C:N:P stoichiometry in terrestrial ecosystems, as well as their influencing factors, using the CWM. More importantly, this study provides a systematic dataset of C:N:P ratios from plot to biome scale that can be used to improve relevant ecological models.

Arctic terrestrial biodiversity status and trends: A synopsis of science supporting the CBMP State of Arctic Terrestrial Biodiversity Report.

This review provides a synopsis of the main findings of individual papers in the special issue Terrestrial Biodiversity in a Rapidly Changing Arctic. The special issue was developed to inform the State of the Arctic Terrestrial Biodiversity Report developed by the Circumpolar Biodiversity Monitoring Program (CBMP) of the Conservation of Arctic Flora and Fauna (CAFF), Arctic Council working group. Salient points about the status and trends of Arctic biodiversity and biodiversity monitoring are organized by taxonomic groups: (1) vegetation, (2) invertebrates, (3) mammals, and (4) birds. This is followed by a discussion about commonalities across the collection of papers, for example, that heterogeneity was a predominant pattern of change particularly when assessing global trends for Arctic terrestrial biodiversity. Finally, the need for a comprehensive, integrated, ecosystem-based monitoring program, coupled with targeted research projects deciphering causal patterns, is discussed.

Criteria-based ranking (CBR): A comprehensive process for selecting and prioritizing monitoring indicators.

Resources allocated to natural resource management often fluctuate, requiring the types and numbers of parameters used in monitoring programs (e.g., indicators of ecosystem health) to be frequently reassessed. Conventional approaches to selecting monitoring indicators are often biased and non-inclusive. A new Criteria-based Ranking (CBR) process for selecting and/or prioritizing indicators was tested in the Muskoka River Watershed (Ontario, Canada). The CBR process is based on two environmental assessment tools, Simple Weighted and Leopold matrices. It incorporates environmental components and criteria for assessing each indicator, which generate a score per indicator. The process tested in this study was concluded to be an effective way to prioritize and/or select environmental monitoring indicators. A different set of indicators emerged when a common set of criteria was used to assess monitoring indicators. Benefits of the CBR process include: •Standardization of indicator selection process with less bias and lower cost (e.g., time and human resources).•Indicators that are representative of the community and more relevant for decision-making (e.g., more resilient to socio-political change).•Adaptability: (1) to other goals, e.g., selecting from a list of Valued Ecosystem Components (VECs), and (2) to any context through localized scoring criteria. Easily integrated into existing practice.