Modelling Maritime Pine (Pinus pinaster Aiton) Spatial Distribution and Productivity in Portugal: Tools for Forest Management

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.

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Using Unsupervised Artificial Neural Networks to Detect Sibling Species: A case in Myxomycetes

Biodiversity Information Science and Standards ◽

10.3897/biss.3.37255 ◽

2019 ◽

Vol 3 ◽

Author(s):

Francisco Pando ◽

Ignacio Heredia ◽

Lara Lloret

Keyword(s):

Sibling Species ◽

Species Distribution ◽

Environmental Data ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Distribution Models ◽

Global Biodiversity Information Facility ◽

Environmental Feature ◽

Feature Vectors ◽

Ecological Preferences

Species distribution modelling (SDM) --i.e. the prediction of species potential geographic distributions based on correlations between known presence records and the environmental conditions at occurrence localities-- is one of the most freqently cited developments in recent years in the realm of biodiversity studies (Web of Science 2019). The reasons for the explosion in SDM studies reside in: a) the pressure to know species distributions (under both present and future climate change scenarios) with precision to satisfy scientific as well as societal objectives; b) the impossibility of knowing every occurrence of all but the most conspicuous and/or special interest species; c) the availability of primary occurrence and environmental data in unprecedented amounts, thanks to initiatives like the Global Biodiversity Information Facility (GBIF); and d) the development of algorithms and software, along with the computing power, which allow inference of species distribution models from the available data. The standard methods to produce such models are based on environmental feature vectors, and some well-established algorithms such as distance-based machine learning, regression or a combination of these (Tsoar et al. 2007). In this presentation, we explore deep learning techniques (LeCun et al. 2015), particularly how those developed in recent years could contribute to the study of species distribution (see also Botella et al. 2018; Deneu et al. 2018). In this contribution we aim to identify sibling species on the basis of their ecological preferences. In this exercise, we prepare an image-based environmental representation space using an unsupervised classification approach, instead of a set of environmental feature vectors. For our case study, we chose a well-known cosmopolitan myxomycete (i.e., mycetozoan) species: Hemitrichia serpula (Scop.) Rostaf., whose ecological preferences --involving several biomes-- may suggest that it could comprise several sibling species.

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Aquatic and semi aquatic Hemiptera community of Sonebeel, the largest wetland of Assam, northeastern India

Journal of Threatened Taxa ◽

10.11609/jott.3440.10.13.12792-12799 ◽

2018 ◽

Vol 10 (13) ◽

pp. 12792-12799

Author(s):

Anupama Saha ◽

Susmita Gupta

Keyword(s):

Species Richness ◽

Pest Control ◽

Relative Abundance ◽

Species Distribution ◽

Diversity Indices ◽

Environmental Data ◽

Northeastern India ◽

Ecological Roles

Aquatic and semiaquatic Hemiptera bugs play significant ecological roles, and they are important indicators and pest control agents. Little information is currently available concerning its populations in southern Assam. This study assessed hemipterans in four sites of Sonebeel, the largest wetland in Assam (3458.12 ha at full storage level), situated in Karimganj District. The major inflow and outflow of the wetland are the rivers Singla and Kachua, respectively (the Kachua drains into the Kushiyara River). Samples were trapped with pond nets and were seasonally recorded. This study recorded a total of 28 species of aquatic and semiaquatic hemipterans belonging to 20 genera under nine families. Population, geographical and environmental data (e.g., rainfall) were used to assess the relative abundance of species, species richness and different diversity indices, and species distribution.

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

Remote Sensing ◽

10.3390/rs13081495 ◽

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.

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Valorisation of non-timber by-products from maritime pine (Pinus pinaster, Ait) for particleboard production

Industrial Crops and Products ◽

10.1016/j.indcrop.2021.113581 ◽

2021 ◽

Vol 168 ◽

pp. 113581

Author(s):

J. Santos ◽

J. Pereira ◽

N. Ferreira ◽

N. Paiva ◽

J. Ferra ◽

...

Keyword(s):

Pinus Pinaster ◽

Maritime Pine ◽

Pinus Pinaster Ait ◽

By Products

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Very high resolution environmental predictors in species distribution models

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133313512667 ◽

2013 ◽

Vol 38 (1) ◽

pp. 79-96 ◽

Cited By ~ 63

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.

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Does phylogeographical structure relate to climatic niche divergence? A test using maritime pine (Pinus pinaster Ait.)

Global Ecology and Biogeography ◽

10.1111/geb.12369 ◽

2015 ◽

Vol 24 (11) ◽

pp. 1302-1313 ◽

Cited By ~ 22

Author(s):

M. J. Serra-Varela ◽

D. Grivet ◽

L. Vincenot ◽

O. Broennimann ◽

J. Gonzalo-Jiménez ◽

...

Keyword(s):

Pinus Pinaster ◽

Maritime Pine ◽

Climatic Niche ◽

Niche Divergence ◽

Pinus Pinaster Ait

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Climate, ticks and disease

10.1079/9781789249637.0000 ◽

2021 ◽

Keyword(s):

Climate Change ◽

Spatial Distribution ◽

Disease Ecology ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Expert Opinions ◽

The World ◽

Host Pathogen ◽

The Future ◽

Impacts Of Climate Change

Abstract This book is a collection of 77 expert opinions arranged in three sections. Section 1 on "Climate" sets the scene, including predictions of future climate change, how climate change affects ecosystems, and how to model projections of the spatial distribution of ticks and tick-borne infections under different climate change scenarios. Section 2 on "Ticks" focuses on ticks (although tick-borne pathogens creep in) and whether or not changes in climate affect the tick biosphere, from physiology to ecology. Section 3 on "Disease" focuses on the tick-host-pathogen biosphere, ranging from the triangle of tick-host-pathogen molecular interactions to disease ecology in various regions and ecosystems of the world. Each of these three sections ends with a synopsis that aims to give a brief overview of all the expert opinions within the section. The book concludes with Section 4 (Final Synopsis and Future Predictions). This synopsis attempts to summarize evidence provided by the experts of tangible impacts of climate change on ticks and tick-borne infections. In constructing their expert opinions, contributors give their views on what the future might hold. The final synopsis provides a snapshot of their expert thoughts on the future.

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