Generalized additive mixed models for disentangling long-term trends, local anomalies, and seasonality in fruit tree phenology

On the phenology of soil organisms: Current knowledge and future steps

Phenology is the study of timing of periodic activities in biological life cycles. It describes an inherent component of ecosystem dynamics, and shifts in biological activity have been increasingly recognized as an indicator of global change. Although phenology is mainly studied above the ground, major ecosystem processes, such as decomposition, mineralization, and nutrient cycling, are soil-dependent. Therefore, the phenology of soil organisms is a crucial, but understudied, aspect of terrestrial ecosystem functioning. We performed a systematic review of 96 studies, which reported 228 phenological observations, to evaluate the current knowledge of soil microbial and animal phenology. Despite the increasing number of soil phenology reports, most research is still concentrated in a few countries (centered in the Northern Hemisphere) and taxa (microbiota), with significant gaps in the most diverse regions of the globe (i.e., tropics) and important taxa (e.g., ants, termites, and earthworms). Moreover, biotic predictors (e.g., biodiversity and species interactions) have rarely been considered as possible drivers of soil organisms' phenology. We present recommendations for future soil phenology research based on an evaluation of the reported geographical, taxonomic, and methodologic trends that bias current soil phenology research. First, we highlight papers that depict good soil phenology practice, either regarding the research foci, methodological approaches, or results reporting. Then, we discuss the gaps, challenges, and opportunities for future research. Overall, we advocate that focusing both on highly diverse ecosystems and key soil organisms, while testing for the direct and indirect effects of biodiversity loss and climatic stressors, could increase our knowledge of soil functioning and enhance the accuracy of predictions depicting the effects of global change on terrestrial ecosystem functioning as a whole.

Analyzing Temporal Trends of Urban Evaporation Using Generalized Additive Models

This study aimed to gain new insights into urban hydrological balance (in particular, the evaporation from paved surfaces). Hourly evaporation data were obtained simultaneously from two high-resolution weighable lysimeters. These lysimeters are covered in two pavement sealing types commonly used for sidewalks in Berlin, namely cobble-stones and concrete slabs. A paired experiment in field conditions is designed to determine the mechanism by which these two types of soil sealing affect the evaporation rate under the same climatic conditions. A generalized additive model (GAM) is applied to explain how the climatic conditions interact with soil sealing and to evaluate the variation of evaporation rate according to pavement type. Moreover, taking the advantage of the fact that the experimental design is paired, the study fits a new GAM where the response variable is the difference between the evaporation rate from the two lysimeters and its explanatory variables are the climatic conditions. As a result, under the same climatic conditions, cobble-stones are more prone to increasing the evaporation rate than concrete slabs when the precipitation accumulated over 10 h, solar radiation, and wind speed increases. On the other hand, concrete slabs are more inclined to increase the evaporation rate than cobblestones when the relative humidity increases. GAM represents a robust modeling approach for comparing different sealing types in order to understand how they alter the hydrological balance.

Climate sensitivity of understory trees differs from overstory trees in temperate mesic forests

The response of understory trees to climate variability is key to understanding current and future forest dynamics. However, analyses of climatic effects on tree growth have primarily focused on the upper canopy, leaving understory dynamics unresolved. We analyzed differences in climate sensitivity based on canopy position of four common tree species (Acer rubrum, Fagus grandifolia, Quercus rubra, and Tsuga canadensis) using growth information from 1,084 trees across eight sites in the northeastern United States. Effects of canopy position on climate response varied, but were significant and often nonlinear, for all four species. Compared to overstory trees, understory trees showed stronger reductions in growth at high temperatures and varied shifts in precipitation response. This contradicts the prevailing assumption that climate responses, particularly to temperature, of understory trees are buffered by the overstory. Forest growth trajectories are uncertain in compositionally and structurally complex forests, and future demography and regeneration dynamics may be misinferred if not all canopy levels are represented in future forecasts.

Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders Are Associated With Alterations in Mental Health Search Behavior in the United States

BACKGROUND

The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people's everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health.

OBJECTIVE

The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale.

METHODS

In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation.

RESULTS

Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety.

CONCLUSIONS

These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.

Long-term trends in fruit production in a tropical forest at Ngogo, Kibale National Park, Uganda

Fruit production in tropical forests varies considerably in space and time, with important implications for frugivorous consumers. Characterizing temporal variation in forest productivity is thus critical for understanding adaptations of tropical forest frugivores, yet long-term phenology data from the tropics, in particular from African forests, are still scarce. Similarly, as the abiotic factors driving phenology in the tropics are predicted to change with a warming climate, studies documenting the relationship between climatic variables and fruit production are increasingly important. Here we present data from 19 years of monitoring the phenology of 20 tree species at Ngogo in Kibale National Park, Uganda. Our aims were to characterize short- and long-term trends in productivity and to understand the abiotic factors driving temporal variability in fruit production. Short-term (month-to-month) variability in fruiting was relatively low at Ngogo, and overall fruit production increased significantly through the first half of the study. Among the abiotic variables we expected to influence phenology patterns (including rainfall, solar irradiance, and average temperature), only average temperature was a significant predictor of monthly fruit production. We discuss these findings as they relate to the resource base of the frugivorous vertebrate community inhabiting Ngogo.

Distribution patterns of east Australian humpback whales (Megaptera novaeangliae) in Hervey Bay, Queensland: a historical perspective

Tourism activities are expanding in both terrestrial and marine environments, which can have detrimental effects on the target species. Balancing the amount of disturbance a population of animals receives against the educational value of tourism requires localised research and adaptive management. This study examined the distribution of humpback whales within Hervey Bay, Queensland, using data spanning 2004–16, just before the implementation of a commercial ‘swim-with-whales’ program. Spatial and temporal patterns of humpback whale calves were of particular interest given that they are more vulnerable to human-related disturbances than other group types. We found that humpback whales displayed a distinct spatial segregation in Hervey Bay based on pod composition. Most whales displayed a residency time of two to three days, with females having a somewhat shorter residency time than males. These findings suggest that humpback whales in Hervey Bay not only display temporal segregation dependent on maturation and reproductive status, but fine-scale spatial distribution based on pod composition. Understanding habitat preference and patterns of habitat use of humpback whales in Hervey Bay is critical for effective management of the newly sanctioned swim-with-whale tourism in Hervey Bay and the sustained recovery of humpback whales in this region.

Time-varying predatory behavior is primary predictor of fine-scale movement of wildland-urban cougars

BACKGROUND

While many species have suffered from the detrimental impacts of increasing human population growth, some species, such as cougars (Puma concolor), have been observed using human-modified landscapes. However, human-modified habitat can be a source of both increased risk and increased food availability, particularly for large carnivores. Assessing preferential use of the landscape is important for managing wildlife and can be particularly useful in transitional habitats, such as at the wildland-urban interface. Preferential use is often evaluated using resource selection functions (RSFs), which are focused on quantifying habitat preference using either a temporally static framework or researcher-defined temporal delineations. Many applications of RSFs do not incorporate time-varying landscape availability or temporally-varying behavior, which may mask conflict and avoidance behavior.

METHODS

Contemporary approaches to incorporate landscape availability into the assessment of habitat selection include spatio-temporal point process models, step selection functions, and continuous-time Markov chain (CTMC) models; in contrast with the other methods, the CTMC model allows for explicit inference on animal movement in continuous-time. We used a hierarchical version of the CTMC framework to model speed and directionality of fine-scale movement by a population of cougars inhabiting the Front Range of Colorado, U.S.A., an area exhibiting rapid population growth and increased recreational use, as a function of individual variation and time-varying responses to landscape covariates.

RESULTS

We found evidence for individual- and daily temporal-variability in cougar response to landscape characteristics. Distance to nearest kill site emerged as the most important driver of movement at a population-level. We also detected seasonal differences in average response to elevation, heat loading, and distance to roads. Motility was also a function of amount of development, with cougars moving faster in developed areas than in undeveloped areas.

CONCLUSIONS

The time-varying framework allowed us to detect temporal variability that would be masked in a generalized linear model, and improved the within-sample predictive ability of the model. The high degree of individual variation suggests that, if agencies want to minimize human-wildlife conflict management options should be varied and flexible. However, due to the effect of recursive behavior on cougar movement, likely related to the location and timing of potential kill-sites, kill-site identification tools may be useful for identifying areas of potential conflict.

Surface fire drives short-term changes in the vegetative phenology of woody species in a Brazilian savanna

We evaluated the effects of fire on the vegetative phenological behavior (crown foliage cover, sprouting, mature and young leaves) of woody species at two sites in the Brazilian savanna, one of which had been accidentally burned. We used generalized additive mixed models to test the hypothesis that: 1) fire damages total foliage cover, thus leading to changes in vegetative phenological patterns. As this hypothesis was corroborated, we also tested whether 2) the damage caused by fire to the total crown foliage cover and mature leaves is greater in evergreen than in deciduous species, and 3) the negative effects of fire on vegetative phenology persist after the first fire-free year. The first two hypotheses were corroborated, but the third was not. Fire effects on total crown foliage cover and mature leaves were greatest during the first three months following the fire, and were significantly greater in evergreen species. For shoots and young leaves, the greatest differences found between three and seven months post-fire. On the other hand, no differences were observed in phenological events between burned and unburned sites in the second year post-fire, indicating that marked effects of the fire were only observed over a short period. Our results showed immediate negative effects on the vegetative phenophases, but also that these effects are transient, and cannot be discerned after the first fire-free year.

Detecting long-term growth trends using tree rings: a critical evaluation of methods

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability analysis. Finally, we recommend SCI and RCS, as these methods showed highest reliability to detect long-term growth trends.