Drought severity has pervasive impacts on honey production via direct impacts on water resources and nectar availability. The current mega-drought in Chile has impacts on water resources and forest vigor, particularly in the Mediterranean and Temperate regions where honey production is concentrated. While honey production plays an important role in the local rural economy and providing pollination services to other agricultural activities, studies of the long-term impacts of the mega-drought on honey production are scarce. Here, we evaluate the impact of climate variability on historical changes in honey production in the Mediterranean (32°S–37°S) and Temperate (37°S–41°S) regions of Chile, using annual honey production records of beekeepers together with national records of honey exports. We also used questionnaires and interviews to evaluate beekeeper perceptions regarding the effects of climate change on honey production and adaptation practices in both regions. Results indicated a declining trend in honey production and exports in the last decade, largely related to changes in precipitation and temperature in both regions. Declines in honey production affected 82% of beekeepers, 80% of whom had employed adaptive measures, and 74% considered that these measures were effective. The drier, warmer Mediterranean region showed more severe declines in precipitation and honey production, which beekeepers reported as a main contributing factor to transhumance from the Mediterranean to the Temperate region. This is the first study to show the effects of drought on honey production in Chile, providing a foundation for future climate change adaptation strategies within apiculture.

Isabella Aguilera-Betti, Ariel A. Muñoz, Daniel Stahle, Gino Figueroa, Fernando Duarte, Álvaro González-Reyes, Duncan Christie, Antonio Lara, Mauro E. González, Paul R. Sheppard, David Sauchyn, Andrés Moreira-Muñoz, Isadora Toledo-Guerrero, Matías Olea, Pablo Apaz, and Alfonso Fernandez (2017) The First Millennium-Age Araucaria Araucana in Patagonia. Tree-Ring Research: Vol. 73, No. 1, pp. 53-56. https://doi.org/10.3959/1536-1098-73.1.53

The South American Altiplano is one of the largest semiarid high‐altitude plateaus in the world. Within the Altiplano, peatlands known as “bofedales” are important components of regional hydrology and provide key water resources and ecosystem services to Andean communities. Warming temperatures, changes in hydroclimate, and shifting atmospheric circulation patterns all affect peatland dynamics and hydrology. It is therefore urgent to better understand the relationships between climate variability and the spatiotemporal variations in peatland productivity across the Altiplano. Here, we explore climate influences on peatland vegetation using 31 years of Landsat data. We focus specifically on the bofedal network in the western Altiplano, the driest sector of the plateau, and use the satellite‐derived Normalized Difference Vegetation Index (NDVI) as an indicator of productivity. We develop temporally and spatially continuous NDVI products at multiple scales in order to evaluate relationships with climate variables over the past three decades. We demonstrate that cumulative precipitation and snow persistence over the prior 2 years are strongly associated with growing season productivity. A step change in peatland productivity between 2013–2015 drives an increasing trend in NDVI and is likely a response to consecutive years of anomalously high snow accumulation and rainfall. Early summer minimum temperatures emerge as a secondary influence on productivity. Understanding large‐scale productivity dynamics and characterizing the response of bofedales to climate variability over the last three decades provides a baseline to monitor the responses of Andean peatlands to climate change.

The high Andean peatlands, locally known as “bofedales”, are a unique type of wetland distributed across the high-elevation South American Altiplano plateau. This extensive peatland network stores significant amounts of carbon, regulates local and regional hydrological cycles, supports habitats for a variety of plant and animal species, and has provided critical water and forage resources for the livestock of the indigenous Aymara communities for thousands of years. Nevertheless, little is known about the productivity dynamics of the high Andean peatlands, particularly in the drier western Altiplano region bordering the Atacama desert. Here, we provide the first digital peatland inventory and multiscale productivity assessment for the entire western Altiplano (63,705 km²) using 31 years of Landsat data (about 9000 scenes) and a non-parametric approach for estimating phenological metrics. We identified 5665 peatland units, covering an area of 510 km², and evaluated the spatiotemporal productivity patterns at the regional, peatland polygon, and individual pixel scales. The regional assessment shows that the peatland areas and peatlands with higher productivity are concentrated towards the northern part of our study region, which is consistent with the Altiplano north–south aridity gradient. Regional patterns further reveal that the last seven years (2011–2017) have been the most productive period over the past three decades. While individual pixels show contrasting patterns of reductions and gains in local productivity during the most recent time period, most of the study area has experienced increases in annual productivity, supporting the regional results. Our novel database can be used not only to explore future research questions related to the social, biological, and hydrological influences on peatland productivity patterns, but also to provide technical support for the sustainable development of livestock practices and conservation and water management policy in the Altiplano region.

Monitoring vegetation disturbances using long remote sensing time series is crucial to support environmental management, biodiversity conservation, and adaptation strategies to climate change from global to local scales. However, it is difficult to assess whether a remotely detected vegetation disturbance is critical or not, since available operational remote sensing methods deliver only maps of the vegetation anomalies but not maps of how “uncommon” or “extreme” the detected anomalies are based on the available records of the reference period. In this technical note, we present a new release of the probabilistic method and its implementation, the npphen R package, designed to detect not only vegetation anomalies from remotely sensed vegetation indices, but also to quantify the position of the anomalous observations within the historical frequency distribution of the phenological annual records. This version of the R package includes two new key functions to detect and map extreme vegetation anomalies: ExtremeAnom and ExtremeAnoMap. The npphen package allows remote sensing users to detect vegetation changes for a wide range of ecosystems, taking advantage of the flexibility of kernel density estimations to account for any shape of annual phenology and its variability through time. It provides a uniform statistical framework to study all types of vegetation dynamics, contributing to global monitoring efforts such as the GEO-BON Essential Biodiversity Variables.