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1. Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

   https://doi.org/10.3390/rs13112074  

   Reisinger, Ryan R. ; Friedlaender, Ari S. ; Zerbini, Alexandre N. ; Palacios, Daniel M. ; Andrews-Goff, Virginia ; Dalla Rosa, Luciano ; Double, Mike ; Findlay, Ken ; Garrigue, Claire ; How, Jason ; et al ( June 2021 , Remote Sensing)

   Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input featuresmore »for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.« less
2. The future distribution of wetland birds breeding in Europe validated against observed changes in distribution

   https://doi.org/10.1088/1748-9326/ac4ebe  

   Soultan, Alaaeldin ; Pavón-Jordán, Diego ; Bradter, Ute ; Sandercock, Brett K ; Hochachka, Wesley M ; Johnston, Alison ; Brommer, Jon ; Gaget, Elie ; Keller, Verena ; Knaus, Peter ; et al ( February 2022 , Environmental Research Letters)

   Abstract Wetland bird species have been declining in population size worldwide as climate warming and land-use change affect their suitable habitats. We used species distribution models (SDMs) to predict changes in range dynamics for 64 non-passerine wetland birds breeding in Europe, including range size, position of centroid, and margins. We fitted the SDMs with data collected for the first European Breeding Bird Atlas and climate and land-use data to predict distributional changes over a century (the 1970s–2070s). The predicted annual changes were then compared to observed annual changes in range size and range centroid over a time period of 30 years using data from the second European Breeding Bird Atlas. Our models successfully predicted ca. 75% of the 64 bird species to contract their breeding range in the future, while the remaining species (mostly southerly breeding species) were predicted to expand their breeding ranges northward. The northern margins of southerly species and southern margins of northerly species, both, predicted to shift northward. Predicted changes in range size and shifts in range centroids were broadly positively associated with the observed changes, although some species deviated markedly from the predictions. The predicted average shift in core distributions was ca. 5 km yrmore »−1 towards the north (5% northeast, 45% north, and 40% northwest), compared to a slower observed average shift of ca. 3.9 km yr −1 . Predicted changes in range centroids were generally larger than observed changes, which suggests that bird distribution changes may lag behind environmental changes leading to ‘climate debt’. We suggest that predictions of SDMs should be viewed as qualitative rather than quantitative outcomes, indicating that care should be taken concerning single species. Still, our results highlight the urgent need for management actions such as wetland creation and restoration to improve wetland birds’ resilience to the expected environmental changes in the future.« less
3. Predicting species distributions and community composition using satellite remote sensing predictors

   https://doi.org/10.1038/s41598-021-96047-7  

   Pinto-Ledezma, Jesús N. ; Cavender-Bares, Jeannine ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m 2 ). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km 2 ). Our results provide insights formore »advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.« less
4. Vulnerability to climate change of a microendemic lizard species from the central Andes

   https://doi.org/10.1038/s41598-021-91058-w  

   Laspiur, A. ; Santos, J. C. ; Medina, S. M. ; Pizarro, J. E. ; Sanabria, E. A. ; Sinervo, B. ; Ibargüengoytía, N. R. ( December 2021 , Scientific Reports)

   Abstract Given the rapid loss of biodiversity as consequence of climate change, greater knowledge of ecophysiological and natural history traits are crucial to determine which environmental factors induce stress and drive the decline of threatened species. Liolaemus montanezi (Liolaemidae), a xeric-adapted lizard occurring only in a small geographic range in west-central Argentina, constitutes an excellent model for studies on the threats of climate change on such microendemic species. We describe field data on activity patterns, use of microhabitat, behavioral thermoregulation, and physiology to produce species distribution models (SDMs) based on climate and ecophysiological data. Liolaemus montanezi inhabits a thermally harsh environment which remarkably impacts their activity and thermoregulation. The species shows a daily bimodal pattern of activity and mostly occupies shaded microenvironments. Although the individuals thermoregulate at body temperatures below their thermal preference they avoid high-temperature microenvironments probably to avoid overheating. The population currently persists because of the important role of the habitat physiognomy and not because of niche tracking, seemingly prevented by major rivers that form boundaries of their geographic range. We found evidence of habitat opportunities in the current range and adjacent areas that will likely remain suitable to the year 2070, reinforcing the relevance of the rivermore »floodplain for the species’ avoidance of extinction.« less
5. Predictable Changes in Eelgrass Microbiomes with Increasing Wasting Disease Prevalence across 23° Latitude in the Northeastern Pacific

   https://doi.org/10.1128/msystems.00224-22  

   Beatty, Deanna S. ; Aoki, Lillian R. ; Rappazzo, Brendan ; Bergman, Chelsea ; Domke, Lia K. ; Duffy, J. Emmett ; Dubois, Katie ; Eckert, Ginny L. ; Gomes, Carla ; Graham, Olivia J. ; et al ( August 2022 , mSystems)

   Raina, Jean-Baptiste (Ed.)

   ABSTRACT Predicting outcomes of marine disease outbreaks presents a challenge in the face of both global and local stressors. Host-associated microbiomes may play important roles in disease dynamics but remain understudied in marine ecosystems. Host–pathogen–microbiome interactions can vary across host ranges, gradients of disease, and temperature; studying these relationships may aid our ability to forecast disease dynamics. Eelgrass, Zostera marina , is impacted by outbreaks of wasting disease caused by the opportunistic pathogen Labyrinthula zosterae . We investigated how Z. marina phyllosphere microbial communities vary with rising wasting disease lesion prevalence and severity relative to plant and meadow characteristics like shoot density, longest leaf length, and temperature across 23° latitude in the Northeastern Pacific. We detected effects of geography (11%) and smaller, but distinct, effects of temperature (30-day max sea surface temperature, 4%) and disease (lesion prevalence, 3%) on microbiome composition. Declines in alpha diversity on asymptomatic tissue occurred with rising wasting disease prevalence within meadows. However, no change in microbiome variability (dispersion) was detected between asymptomatic and symptomatic tissues. Further, we identified members of Cellvibrionaceae, Colwelliaceae, and Granulosicoccaceae on asymptomatic tissue that are predictive of wasting disease prevalence across the geographic range (3,100 kilometers). Functional roles of Colwelliaceae andmore »Granulosicoccaceae are not known. Cellvibrionaceae, degraders of plant cellulose, were also enriched in lesions and adjacent green tissue relative to nonlesioned leaves. Cellvibrionaceae may play important roles in disease progression by degrading host tissues or overwhelming plant immune responses. Thus, inclusion of microbiomes in wasting disease studies may improve our ability to understand variable rates of infection, disease progression, and plant survival. IMPORTANCE The roles of marine microbiomes in disease remain poorly understood due, in part, to the challenging nature of sampling at appropriate spatiotemporal scales and across natural gradients of disease throughout host ranges. This is especially true for marine vascular plants like eelgrass ( Zostera marina ) that are vital for ecosystem function and biodiversity but are susceptible to rapid decline and die-off from pathogens like eukaryotic slime-mold Labyrinthula zosterae (wasting disease). We link bacterial members of phyllosphere tissues to the prevalence of wasting disease across the broadest geographic range to date for a marine plant microbiome-disease study (3,100 km). We identify Cellvibrionaceae, plant cell wall degraders, enriched (up to 61% relative abundance) within lesion tissue, which suggests this group may be playing important roles in disease progression. These findings suggest inclusion of microbiomes in marine disease studies will improve our ability to predict ecological outcomes of infection across variable landscapes spanning thousands of kilometers.« less