scholarly journals Vulnerable, but still poorly known, marine ecosystems: how to make distribution models more relevant and impactful for conservation and management of VMEs?

2022 ◽  
Author(s):  
Charley Gros ◽  
Jan Jansen ◽  
Piers Dunstan ◽  
Dirk C Welsford ◽  
Nicole Hill
Keyword(s):  
Deep Sea ◽  
Sustainable Use ◽  
Marine Ecosystems ◽  
Environmental Data ◽  
Management Decision ◽  
Value Assessment ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Modelling Studies ◽  
Mapping Vulnerability

Human activity puts our oceans under multiple stresses, whose impacts are already significantly affecting biodiversity and physicochemical properties. Consequently, there is an increased international focus on the conservation and sustainable use of oceans, including the protection of fragile benthic biodiversity hotspots in the deep sea, identified as vulnerable marine ecosystems (VMEs). International VME risk assessment and conservation efforts are hampered because we largely do not know where VMEs are located. VME distribution modelling has increasingly been recommended to extend our knowledge beyond sparse observations. Nevertheless, the adoption of VME distribution models in spatial management planning and conservation remains limited. This work critically reviews VME distribution modelling studies, and recommends promising avenues to make VME models more relevant and impactful for policy and management decision making. First, there is an important interplay between the type of VME data used to build models and how the generated maps can be used in making management decisions, which is often ignored by model-builders. We encourage scientists towards founding their models on: (i) specific and quantitative definitions of what constitute a VME, (ii) site conservation value assessment in relation to VME multi-taxon spatial predictions, and (iii) explicitly mapping vulnerability. Along with the recent increase in both deep-sea biological and environmental data quality and quantity, these modelling recommendations can lead towards more cohesive summaries of VME’s spatial distributions and their relative vulnerability, which should facilitate a more effective protection of these ecosystems, as has been mandated by numerous international agreements.

Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA

2013 ◽  
Vol 22 (2) ◽  
pp. 174 ◽  
Author(s):  
Avi Bar Massada ◽  
Alexandra D. Syphard ◽  
Susan I. Stewart ◽  
Volker C. Radeloff
Keyword(s):  
Random Forests ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Environmental Data ◽  
Distribution Model ◽  
National Forest ◽  
Similar Species ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Manistee National Forest

Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar species-distribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution model types: one parametric, statistical model (Generalised Linear Models, GLM) and two machine-learning algorithms (Random Forests and Maximum Entropy, Maxent). We parameterised the models using 16 years of ignitions data and environmental data for the Huron–Manistee National Forest in Michigan, USA. Random Forests and Maxent had slightly better prediction accuracies than did GLM, but model fit was similar for all three. Variables related to human population and development were the best predictors of wildfire ignition locations in all models (although variable rankings differed slightly), along with elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models. We thus suggest that when accurate predictions are desired, the outcomes of different model types should be compared, or alternatively combined, to produce ensemble predictions.

scholarly journals Definition and detection of vulnerable marine ecosystems on the high seas: problems with the “move-on” rule

2010 ◽  
Vol 68 (2) ◽  
pp. 254-264 ◽  
Author(s):  
Peter J. Auster ◽  
Kristina Gjerde ◽  
Eric Heupel ◽  
Les Watling ◽  
Anthony Grehan ◽  
...  
Keyword(s):  
Deep Sea ◽  
Large Scale ◽  
Sustainable Use ◽  
Marine Ecosystems ◽  
Fishing Gear ◽  
Precautionary Approach ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
National Jurisdiction ◽  
High Seas

Abstract Auster, P. J., Gjerde, K., Heupel, E., Watling, L., Grehan, A., and Rogers, A. D. 2011. Definition and detection of vulnerable marine ecosystems on the high seas: problems with the “move-on” rule. – ICES Journal of Marine Science, 68: 254–264. Fishing in the deep sea in areas beyond national jurisdiction has produced multiple problems related to management for conservation and sustainable use. Based on a growing concern, the United Nations has called on States to prevent significant adverse impacts to vulnerable marine ecosystems (VMEs) in the deep sea. Although Food and Agriculture Organization (FAO) guidelines for management were produced through an international consultative process, implementing criteria for designation of VMEs and recognition of such areas when encountered by fishing gear have been problematic. Here we discuss assumptions used to identify VMEs and current requirements related to unforeseen encounters with fishing gear that do not meet technological or ecological realities. A more precautionary approach is needed, given the uncertainties about the location of VMEs and their resilience, such as greatly reducing the threshold for an encounter, implementation of large-scale permanent closed areas, and prohibition of bottom-contact fishing.

Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

2021 ◽  
pp. 1-24
Author(s):  
Charlène Guillaumot ◽  
Bruno Danis ◽  
Thomas Saucède
Keyword(s):  
Southern Ocean ◽  
Species Distribution ◽  
Model Performance ◽  
Species Distribution Modelling ◽  
Projection Area ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Influence Model ◽  
Modelling Studies

Abstract Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties in conducting biological experiments, limit the performance of species distribution models. In this review, we discuss some issues that may influence model performance when preparing datasets and calibrating models, namely the selection and quality of environmental descriptors, the spatial and temporal biases that may affect the quality of occurrence data, the choice of modelling algorithms and the spatial scale and limits of the projection area. We stress the importance of evaluating and communicating model uncertainties, and the most common evaluation metrics are reviewed and discussed accordingly. Based on a selection of case studies on SO benthic invertebrates, we highlight important cautions to take and pitfalls to avoid when modelling the distribution of SO species, and we provide some guidelines along with potential methods and original solutions that can be used for improving model performance.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 48
Author(s):  
Margaret F.J. Dolan ◽  
Rebecca E. Ross ◽  
Jon Albretsen ◽  
Jofrid Skarðhamar ◽  
Genoveva Gonzalez-Mirelis ◽  
...  
Keyword(s):  
Barents Sea ◽  
Predictor Variable ◽  
Environmental Data ◽  
Spatial Uncertainty ◽  
Benthic Habitat ◽  
Habitat Distribution ◽  
Distribution Models ◽  
Entire Study ◽  
Oceanographic Data ◽  
Uncertainty Analyses

The use of habitat distribution models (HDMs) has become common in benthic habitat mapping for combining limited seabed observations with full-coverage environmental data to produce classified maps showing predicted habitat distribution for an entire study area. However, relatively few HDMs include oceanographic predictors, or present spatial validity or uncertainty analyses to support the classified predictions. Without reference studies it can be challenging to assess which type of oceanographic model data should be used, or developed, for this purpose. In this study, we compare biotope maps built using predictor variable suites from three different oceanographic models with differing levels of detail on near-bottom conditions. These results are compared with a baseline model without oceanographic predictors. We use associated spatial validity and uncertainty analyses to assess which oceanographic data may be best suited to biotope mapping. Our results show how spatial validity and uncertainty metrics capture differences between HDM outputs which are otherwise not apparent from standard non-spatial accuracy assessments or the classified maps themselves. We conclude that biotope HDMs incorporating high-resolution, preferably bottom-optimised, oceanography data can best minimise spatial uncertainty and maximise spatial validity. Furthermore, our results suggest that incorporating coarser oceanographic data may lead to more uncertainty than omitting such data.

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

scholarly journals Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change

2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Climatic Conditions ◽  
First Century ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Major Barrier ◽  
Methodological Choices ◽  
Biodiversity And Ecosystem Function

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.

scholarly journals Implementing new northernmost records to modelling the distribution of Hypsiboas caingua (Anura: Hylidae) in South America

2014 ◽  
Vol 74 (4) ◽  
pp. 854-860 ◽  
Author(s):  
M Delatorre ◽  
TS Vasconcelos ◽  
NL Cunha ◽  
FI Martins ◽  
FH Oda ◽  
...  
Keyword(s):  
South America ◽  
Geographic Distribution ◽  
Scientific Literature ◽  
Climatic Data ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Field Surveys ◽  
Linear Distance ◽  
New Localities ◽  
Evolutionary Aspects

New northwestern records of the striped treefrog Hypsiboas caingua (Carrizo, 1991 “1990”) are presented for Brazil, extending its known geographic distribution approximately 115 km northward (linear distance) of the previous northernmost record of the species. In all new localities, individuals of H. caingua were only found in calling activity under temperatures below 18°C. The species appears to be associated with Atlantic Forest, although it had already been recorded in Cerrado. Although the scientific literature data suggests that H. caingua presents discontinuous geographical distribution, three methods of distribution modelling based on climatic data show that the species' distribution is continuous. The gap in the distribution may be related to the lack of field surveys in the regions that connect these populations, especially in colder periods. Nevertheless, factors other than climate might also be responsible for the current distribution of H. caingua, since the species was absent in well-surveyed sites situated between the areas of modeled distribution. Therefore, further studies on natural history, populations' genetic structure, and geographic distribution models implementing factors other than climate will be important to elucidate some ecological and evolutionary aspects of the distribution of H. caingua in South America.

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