Special Issue "Spatial Ecology"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (28 February 2017).

Special Issue Editor

Guest Editor
Prof. Duccio Rocchini Website E-Mail
Center Agriculture Food Environment, University of Trento, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
Centre for Integrative Biology, University of Trento, Via Sommarive, 14, 38123 Povo (TN), Italy
Department of Biodiversity and Molecular Ecology, Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
Interests: biodiversity estimate; ecological informatics; remote sensing; species distribution modelling

Special Issue Information

Dear Colleagues,

The need for a spatial view in Ecology is a fact. Dealing with ecological changes over space and time represents a long-lasting theme, now faced with challenging techniques and modelling approaches. To this end, robustly dealing with spatial problems is crucial to understand processes that shape ecosystem patterns.

This Special Issue is devoted to all aspects of spatial science related to Ecology.

Dr Duccio Rocchini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ž   biodiversity estimate
  • ž   climate change
  • ž   geographic information systems,
  • ž   geostatistics
  • ž   landscape patterns
  • ž   Landscape Ecology
  • ž   Marcoecology
  • ž   remote sensing
  • ž   species distribution modelling
  • ž   Volunteering Geographic Information

Published Papers (11 papers)

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Editorial

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Open AccessEditorial
Space-Ruled Ecological Processes: Introduction to the Special Issue on Spatial Ecology
ISPRS Int. J. Geo-Inf. 2018, 7(1), 11; https://doi.org/10.3390/ijgi7010011 - 02 Jan 2018
Abstract
This special issue explores most of the scientific issues related to spatial ecology and its integration with geographical information at different spatial and temporal scales.[...] Full article
(This article belongs to the Special Issue Spatial Ecology)

Research

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Open AccessArticle
Development and Comparison of Species Distribution Models for Forest Inventories
ISPRS Int. J. Geo-Inf. 2017, 6(6), 176; https://doi.org/10.3390/ijgi6060176 - 16 Jun 2017
Cited by 8
Abstract
A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our case presence/absence data) according environmental variables. During [...] Read more.
A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our case presence/absence data) according environmental variables. During the process we have developed maximum entropy (MaxEnt), classification and regression trees (CART), multivariate adaptive regression splines (MARS), showing the statistical basis of each model and, at the same time, we have developed a specific additive model to compare and validate their capability. To compare different results, the area under the receiver operating characteristic (ROC) function (AUC) was used. Every AUC value obtained with those models is significant and all of the models could be useful to represent the distribution of each species. Moreover, the additive model with thin plate splines gave the best results. The worst capability was obtained with MARS. This model’s performance was below average for several species. The additive model developed obtained better results because it allowed for changes and calibrations. In this case we were aware of all of the processes that occurred during the modeling. By contrast, models obtained using specific software, in general, perform like “hermetic machines”, because it could sometimes be impossible to understand the stages that led to the final results. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models
ISPRS Int. J. Geo-Inf. 2017, 6(3), 66; https://doi.org/10.3390/ijgi6030066 - 28 Feb 2017
Cited by 5
Abstract
Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and [...] Read more.
Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN) models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor) were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya’s bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI) variables were used to model their ecological niches using Maximum Entropy (MaxEnt). Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models’ performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055) indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the ‘actual’ habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Effect of the Long-Term Mean and the Temporal Stability of Water-Energy Dynamics on China’s Terrestrial Species Richness
ISPRS Int. J. Geo-Inf. 2017, 6(3), 58; https://doi.org/10.3390/ijgi6030058 - 24 Feb 2017
Cited by 1
Abstract
Water-energy dynamics broadly regulate species richness gradients but are being altered by climate change and anthropogenic activities; however, the current methods used to quantify this phenomenon overlook the non-linear dynamics of climatic time-series data. To analyze the gradient of species richness in China [...] Read more.
Water-energy dynamics broadly regulate species richness gradients but are being altered by climate change and anthropogenic activities; however, the current methods used to quantify this phenomenon overlook the non-linear dynamics of climatic time-series data. To analyze the gradient of species richness in China using water-energy dynamics, this study used linear regression to examine how species richness is related to (1) the long-term mean of evapotranspiration (ET) and potential evapotranspiration (PET) and (2) the temporal stability of ET and PET. ET and PET were used to represent the water-energy dynamics of the terrestrial area. Changes in water-energy dynamics over the 14-year period (2000 to 2013) were also analyzed. The long-term mean of ET was strong and positively ( R 2 ( 0.40 ~ 0.67 ) , p < 0.05 ) correlated with the species richness gradients. Regions in which changes in land cover have occurred over the 14-year period (2000 to 2013) were detected from long-term trends. The high level of species richness in all groups (birds, mammals, and amphibians) was associated with relatively high ET, determinism (i.e., predictability), and entropy (i.e., complexity). ET, rather than PET or temporal stability measures, was an effective proxy of species richness in regions of China that had moderate energy (PET > 1000 mm/year), especially for amphibians. In addition, predictions of species richness were improved by incorporating information on the temporal stability of ET with long-term means. Amphibians are more sensitive to the long-term ET mean than other groups due to their unique physiological requirements and evolutionary processes. Our results confirmed that ET and PET were strongly and significantly correlated with climatic and anthropogenic induced changes, providing useful information for conservation planning. Therefore, climate management based on changes to water-energy dynamics via land management practices, including reforestation, should be considered when planning methods to conserve natural resources to protect biodiversity. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessCommunication
sgdm: An R Package for Performing Sparse Generalized Dissimilarity Modelling with Tools for gdm
ISPRS Int. J. Geo-Inf. 2017, 6(1), 23; https://doi.org/10.3390/ijgi6010023 - 19 Jan 2017
Cited by 6
Abstract
Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed to deal with high dimensional datasets, such [...] Read more.
Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed to deal with high dimensional datasets, such as time series or hyperspectral remote sensing data. In this manuscript we present sgdm, an R package for performing sparse generalized dissimilarity modelling (SGDM). The package includes some general tools that add functionality to both generalized dissimilarity modelling and sparse generalized dissimilarity modelling. It also includes an exemplary dataset that allows for the application of SGDM for mapping the spatial patterns of tree communities in a region of natural vegetation in the Brazilian Cerrado. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Integrating Multiple Spatial Datasets to Assess Protected Areas: Lessons Learnt from the Digital Observatory for Protected Areas (DOPA)
ISPRS Int. J. Geo-Inf. 2016, 5(12), 242; https://doi.org/10.3390/ijgi5120242 - 15 Dec 2016
Cited by 6
Abstract
The Digital Observatory for Protected Areas (DOPA) has been developed to support the European Union’s efforts in strengthening our capacity to mobilize and use biodiversity data so that they are readily accessible to policymakers, managers, researchers and other users. Assessing protected areas for [...] Read more.
The Digital Observatory for Protected Areas (DOPA) has been developed to support the European Union’s efforts in strengthening our capacity to mobilize and use biodiversity data so that they are readily accessible to policymakers, managers, researchers and other users. Assessing protected areas for biodiversity conservation at national, regional and international scales implies that methods and tools are in place to evaluate characteristics such as the protected areas’ connectivity, their species assemblages (including the presence of threatened species), the uniqueness of their ecosystems, and the threats these areas are exposed to. Typical requirements for such analyses are data on protected areas, information on species distributions and threat status, and information on ecosystem distributions. By integrating all these global data consistently in metrics and indicators, the DOPA provides the means to allow end-users to evaluate protected areas individually but also to compare protected areas at the country and ecoregion level to, for example, identify potential priorities for further conservation research, action and funding. Since the metrics and indicators are available through web services, the DOPA further allows end-users to develop their own applications without requiring management of large databases and processing capacities. In addition to examples illustrating how the DOPA can be used as an aid to decision making, we discuss the lessons learnt in the development of this global biodiversity information system, and outline planned future developments for further supporting conservation strategies. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Methodology for Evaluating the Quality of Ecosystem Maps: A Case Study in the Andes
ISPRS Int. J. Geo-Inf. 2016, 5(8), 144; https://doi.org/10.3390/ijgi5080144 - 15 Aug 2016
Cited by 2
Abstract
Uncertainty in thematic maps has been tested mainly in maps with discrete or fuzzy classifications based on spectral data. However, many ecosystem maps in tropical countries consist of discrete polygons containing information on various ecosystem properties such as vegetation cover, soil, climate, geomorphology [...] Read more.
Uncertainty in thematic maps has been tested mainly in maps with discrete or fuzzy classifications based on spectral data. However, many ecosystem maps in tropical countries consist of discrete polygons containing information on various ecosystem properties such as vegetation cover, soil, climate, geomorphology and biodiversity. The combination of these properties into one class leads to error. We propose a probability-based sampling design with two domains, multiple stages, and stratification with selection of primary sampling units (PSUs) proportional to the richness of strata present. Validation is undertaken through field visits and fine resolution remote sensing data. A pilot site in the center of the Colombian Andes was chosen to validate a government official ecosystem map. Twenty primary sampling units (PSUs) of 10 × 15 km were selected, and the final numbers of final sampling units (FSUs) were 76 for the terrestrial domain and 46 for the aquatic domain. Our results showed a confidence level of 95%, with the accuracy in the terrestrial domain varying between 51.8% and 64.3% and in the aquatic domain varying between 75% and 92%. Governments need to account for uncertainty since they rely on the quality of these maps to make decisions and guide policies. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Improved Biogeography-Based Optimization Based on Affinity Propagation
ISPRS Int. J. Geo-Inf. 2016, 5(8), 129; https://doi.org/10.3390/ijgi5080129 - 23 Jul 2016
Cited by 3
Abstract
To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the [...] Read more.
To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operation of the BBO algorithm. In this work, the MBBO algorithm was applied to IEEE Congress on Evolutionary Computation (CEC) 2015 benchmarks optimization problems to conduct analytic comparison with the first three winners of the CEC 2015 competition. The results show that the MBBO algorithm enhances the exploration, exploitation, convergence speed and solution accuracy and can emerge as the best solution-providing algorithm among the competing algorithms. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Exploring the Relationship between Remotely-Sensed Spectral Variables and Attributes of Tropical Forest Vegetation under the Influence of Local Forest Institutions
ISPRS Int. J. Geo-Inf. 2016, 5(7), 117; https://doi.org/10.3390/ijgi5070117 - 14 Jul 2016
Cited by 2
Abstract
Conservation of forests outside protected areas is essential for maintaining forest connectivity, which largely depends on the effectiveness of local institutions. In this study, we use Landsat data to explore the relationship between vegetation structure and forest management institutions, in order to assess [...] Read more.
Conservation of forests outside protected areas is essential for maintaining forest connectivity, which largely depends on the effectiveness of local institutions. In this study, we use Landsat data to explore the relationship between vegetation structure and forest management institutions, in order to assess the efficacy of local institutions in management of forests outside protected areas. These forests form part of an important tiger corridor in Eastern Maharashtra, India. We assessed forest condition using 450 randomly placed 10 m radius circular plots in forest patches of villages with and without local institutions, to understand the impact of these institutions on forest vegetation. Tree density and species richness were significantly different between villages with and without local forest institutions, but there was no difference in tree biomass. We also found a significant difference in the relationship between tree density and NDVI between villages with and without local forest institutions. However, the relationship between species richness and NDVI did not differ significantly. The methods proposed by this study evaluate the status of forest management in a forest corridor using remotely sensed data and could be effectively used to identify the extent of vegetation health and management status. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Mapping Historical Data: Recovering a Forgotten Floristic and Vegetation Database for Biodiversity Monitoring
ISPRS Int. J. Geo-Inf. 2016, 5(7), 100; https://doi.org/10.3390/ijgi5070100 - 23 Jun 2016
Cited by 5
Abstract
Multitemporal biodiversity data on a forest ecosystem can provide useful information about the evolution of biodiversity in a territory. The present study describes the recovery of an archive used to determine the main Schmid’s vegetation belts in Trento Province, Italy. The archive covers [...] Read more.
Multitemporal biodiversity data on a forest ecosystem can provide useful information about the evolution of biodiversity in a territory. The present study describes the recovery of an archive used to determine the main Schmid’s vegetation belts in Trento Province, Italy. The archive covers 20 years, from the 1970s to the 1990s. During the FORCING project (an Italian acronym for Cingoli Forestali, i.e., forest belts), a comprehensive process of database recovering was executed, and missing data were digitized from historical maps, preserving paper-based maps and documents. All of the maps of 16 forest districts, and the related 8000 detected transects, have been georeferenced to make the whole database spatially explicit and to evaluate the possibility of performing comparative samplings on up-to-date datasets. The floristic raw data (approximately 200,000 specific identifications, including frequency indices) still retain an important and irreplaceable information value. The data can now be browsed via a web-GIS. We provide here a set of examples of the use of this type of data, and we highlight the potential and the limits of the specific dataset and of the historical database, in general. Full article
(This article belongs to the Special Issue Spatial Ecology)
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Open AccessArticle
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2496-2518; https://doi.org/10.3390/ijgi4042496 - 16 Nov 2015
Cited by 9
Abstract
Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data “contaminated” with varying [...] Read more.
Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data “contaminated” with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species’ niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling. Full article
(This article belongs to the Special Issue Spatial Ecology)
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