Seasonality, niche management, and vertical migration in landscapes of relief

John, Christian; Post, Eric

doi:10.32942/osf.io/e72pk

Cited by 4 publications

(4 citation statements)

References 101 publications

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“…As a result, some components of the realized niche may easily differ (niche switching) (Fig. 1, bottom-right panel) depending on the period of the year considered, while other could remain conservated (niche tracking), given the complex biotic and abiotic interactions in different geographical areas (John and Post 2021). For many species, variation in environmental variables may be especially relevant during the breeding season, during which factors like temperature may become critical for successful reproduction (e.g.…”

Section: Migrationmentioning

confidence: 99%

The importance of including phenology when modelling species ecological niche

Ponti

Sannolo

2022

Ecography

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Species distribution models have grown in complexity by incorporating fine-scale variables, including data on microclimate, physiology and species interactions. Recent studies have acknowledged the importance of the spatial scale by including higher resolution maps and more complex climatic variables. However, models rarely consider the consequences of including data related to time. Indeed, species phenology -and potential shifts in phenology due, for example, to climate change -is potentially one of the most neglected aspects of ecological modelling. We present a literature review of relevant phenological aspects at different temporal scales and across several taxa. Such elements should be considered to define better the environmental niche and project present, future and past distribution models. We considered the available studies on plants, insects, reptiles, birds and mammals to evaluate how they dealt with the phenology of the investigated species, as well as the phenology of other resources and interacting species, to infer present, past and future projections. Here we focus on four main phenological aspects that, if not considered, may easily bias any projection, namely: 1) phenology can be accompanied by a shift in distribution within the year (e.g. migratory species); 2) activity may be restricted to a portion of the year (e.g. most ectotherms from temperate climates); 3) survival and reproduction success may depend on the synchrony with other species phenology (e.g. plants-pollinators interactions); 4) changes in climatic conditions can lead to shifts in phenology (e.g. anticipated or delayed blooms or changes in migration timing). In this review, we show how neglecting such factors may quickly lead to project a biased distribution. Finally, we provide a guide on evaluating whether the case study may be affected by such factors and what actions may improve the models.

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Section: Migrationmentioning

confidence: 99%

The importance of including phenology when modelling species ecological niche

Ponti

Sannolo

2022

Ecography

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“…These patterns align with spatial trends discussed previously, with cluster 1 dives having peaks in density in the SPG where primary productivity is low, further supporting that foraging behavior in this area is more correlated with variables of vertical movement and water column structure (Bailey, Fossette, et al, 2012). EP leatherbacks using different dive depths with varied environmental variables suggests that they may be utilizing adaptive feeding behavior or “niche switching,” whereby different vertical niche space is used along the horizontal, latitudinal migration to compensate for spatiotemporal variation in resources (John & Post, 2021).…”

Section: Discussionmentioning

confidence: 99%

Clustering and classification of vertical movement profiles for ecological inference of behavior

et al. 2023

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Vertical movements can expose individuals to rapid changes in physical and trophic environments—for aquatic fauna, dive profiles from biotelemetry data can be used to quantify and categorize vertical movements. Inferences on classes of vertical movement profiles typically rely on subjective summaries of parameters or statistical clustering techniques that utilize Euclidean matching of vertical movement profiles with vertical observation points. These approaches are prone to subjectivity, error, and bias. We used machine learning approaches on a large dataset of vertical time series (N = 28,217 dives) for 31 post‐nesting leatherback turtles (Dermochelys coriacea). We applied dynamic time warp (DTW) clustering to group vertical movement (dive) time series by their metrics (depth and duration) into an optimal number of clusters. We then identified environmental covariates associated with each cluster using a generalized additive mixed‐effects model (GAMM). A convolutional neural network (CNN) model, trained on standard dive shape types from the literature, was used to classify dives within each DTW cluster by their shape. Two clusters were identified with the DTW approach—these varied in their spatial and temporal distributions, with dependence on environmental covariates, sea surface temperature, bathymetry, sea surface height anomaly, and time‐lagged surface chlorophyll a concentrations. CNN classification accuracy of the five standard dive profiles was 95%. Subsequent analyses revealed that the two clusters differed in their composition of standard dive shapes, with each cluster dominated by shapes indicative of distinct behaviors (pelagic foraging and exploration, respectively). The use of these two machine learning approaches allowed for discrete behaviors to be identified from vertical time series data, first by clustering vertical movements by their movement metrics (DTW) and second by classifying dive profiles within each cluster by their shapes (CNN). Statistical inference for the identified clusters found distinct relationships with environmental covariates, supporting hypotheses of vertical niche switching and vertically structured foraging behavior. This approach could be similarly applied to the time series of other animals utilizing the vertical dimension in their movements, including aerial, arboreal, and other aquatic species, to efficiently identify different movement behaviors and inform habitat models.

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“…Migration of ungulates can occur across large distances (e.g. mean of ∼40 km and max of 196 km for moose; Mauer 1998) or shorter-distance elevational gradients (John and Post 2021), with winter ranges usually at lower elevations below tree-line where snow is typically shallower. Travel between seasonal ranges can be especially dangerous.…”

Section: Introductionmentioning

confidence: 99%

Human and animal movements combine with snow to increase moose-vehicle collisions in winter

Cunningham

Liston

Reinking

et al. 2022

Environ. Res. Lett.

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Wildlife-vehicle collisions imperil humans, wildlife, and property. Collisions with moose (Alces alces) are especially consequential and there are indications they may increase during severe winters. We tested hypotheses regarding the influence of moose movements and vehicular traffic patterns on collision risk. We first modeled daily snow depth and accumulation across 5.6 million km2 of the North American Arctic-Boreal region. Next, we analyzed the movements and road use of 113 GPS-collared moose in response to snow depth. Finally, we examined the influence of these snow properties on vehicular traffic and 7680 moose-vehicle collisions. As winter progressed and the snowpack deepened in each study area, GPS-collared moose migrated to lower elevations, leading them into areas with shallower snow but higher road densities. This elevational migration corresponded with a higher probability of road-use by moose (by up to ten-fold) in winter than in summer. Corresponding to these patterns, moose-vehicle collisions were 2.4–5.7 times more frequent from December through February (compared to early summer). Collision risk was highest when and where snow depth was less than 120 cm, indicating that migration into areas with shallower snow increased collision risk in those areas. Most (82%) moose-vehicle collisions occurred after dark. This pattern was strongest during winter, when nighttime traffic volumes were eight times higher than summer due to longer nights. Overall, our findings suggest that concurrent seasonal changes in human and wildlife behavior increase the frequency of moose-vehicle collisions during winter. Snow depth influences collisions primarily through its impacts on moose movement, while strong seasonal changes in daylight hours cause an increase in nighttime traffic that further contributes to risk. This information may help predict times and places where risk of moose-vehicle collisions are highest and to develop seasonally dynamic mitigation strategies.

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Seasonality, niche management, and vertical migration in landscapes of relief

Cited by 4 publications

References 101 publications

The importance of including phenology when modelling species ecological niche

The importance of including phenology when modelling species ecological niche

Clustering and classification of vertical movement profiles for ecological inference of behavior

Human and animal movements combine with snow to increase moose-vehicle collisions in winter

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