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opencc-by-4.0Jun 2020View details →

Habitat suitability predictions for a boreal forest indicator species, the northern goshawk (Accipiter gentilis), in Central Finland

This repository contains files that show optimal sites in Central Finland for the northern goshawk (Accipiter gentilis, hereafter goshawk), an indicator species of boreal forests with conservation values. The optimal sites were derived from the habitat suitability model outputs included in the following publication:   Björklund Heidia, Parkkinen Anssib, Hakkari Tomic, Heikkinen Risto K.d, Virkkala Raimod, Lensu Anssib (2020): Predicting valuable forest habitats using an indicator species for biodiversity. Biological Conservation, <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .    a Finnish Museum of Natural History Luomus, P.O. Box 17, FI-00014 University of Helsinki, Finland b University of Jyvaskyla, Department of Biological and Environmental Science, P.O. Box 35, FI-40014 University of Jyvaskyla, Finland c Centre for Economic Development, Transport and the Environment Central Finland, P.O. Box 250, FI-40101 Jyväskylä, Finland d Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland   The files are ArcGIS compatible shape files which indicate the spatial location of the 160 m × 160 m grid cells which include forest stands projected to be either highly suitable or suitable as a nesting site for the goshawk in Central Finland. The habitat suitability models and values were developed across the study area using Maxent software. The files show those 160-m grid cells from the study area which were included in one of the following two categories: (i) cells deemed as the most optimal (with high probability of suitable conditions) for goshawk nesting with suitability index values in Maxent outputs varying between 0.92–1.00 (‘best’ goshawk squares), and (ii) cells deemed as ‘good’ goshawk squares (with Maxent suitability index values of ≥ 0.69 and < 0.92). The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)).  Summarization of the key settings and elements of the study are provided below. A detailed treatment can now be found in the article published in Biological Conservation (Björklund et al.) for which the link is the following: <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .   Summary of the study Intensive commercial use of boreal forests is an accelerating threat to forest biodiversity, highlighting the development of cost-effective tools to detect the locations valuable for conservation. We applied species distribution models (SDMs) in our study area, Central Finland, to locate the optimal nesting sites for the goshawk, an indicator bird species for biodiversity hotspots in mature boreal forests. The optimal sites (here, 160 x 160 m grid squares) for the goshawk were determined using the Maxent software. Optimal squares for the goshawk had forests with considerably high volumes of Norway spruce (Picea abies, hereafter spruce) covering only 3.4% of the boreal landscape, and they were located mostly outside protected areas. Many of the squares with optimal nesting forests appeared to be under threat due to recently intensified logging operations. Half of the squares were logged to some extent and 10% were already lost or notably deteriorated due to logging after 2015 for which our models were calibrated. Threats to biodiversity of mature boreal spruce forests are likely to accelerate with increasing logging pressures. Thus, there is an urgent need to secure the continuous supply of mature spruce forests in the landscape by developing a denser network of protected areas and applying measures that aid in sparing large entities of mature forest on privately-owned land. Our modelled optimal squares can be used for selection of potential areas with biodiversity values in conservation prioritization. The study species The goshawk is a raptor species which prefers mature forests for nesting in Europe. Old forests dominated by spruce are considered as important for the breeding success of the species particularly in northern latitudes. Thus, intensive forest management can impair the breeding possibilities of the goshawk, and changes in forest landscapes are likely to contribute to the decline of the species. For example, in Finland, the goshawk is classified as nearly threatened species. In our study, we used the goshawk as an indicator species to model the spatial locations of boreal forest with much potential for including biodiversity values. The indicator species status of the goshawk is based on earlier studies showing the close association of the goshawk with various taxa of mature spruce forest, as well as the reported declines of both the goshawk and associated species due to loggings. Developing Maxent models for the goshawk The location data on occupied nests of the goshawk gathered in spring and summer 2015 and 2016 in Central Finland – as a part of the Finnish Common Birds of Prey Monitoring – were related to a set of environmental predictor variables using a maximum entropy method, Maxent software, which is considered particularly useful for modelling presence-only data (such as our goshawk nest site data). In our case, the data on forest stand and tree characteristics were related using Maxent to the known nesting sites to predict suitable conditions for the species across the Central Finland. The forest data used in the modelling were extracted from the multi-source national forest inventory (MS-NFI) data sources governed by the Natural Resources Institute Finland. The MS-NFI data used in our modelling are based on field data of the 11th and 12th NFIs from 2009 to 2016 and satellite images from 2015 and 2016. Prior modelling, Pearson correlations were calculated between the continuous environmental variables at the nest sites. Of the highly (|r| ≥ 0.7) correlated variables, we chose those variables which are known to be important for the goshawk, which are useful for generalization in other areas, or whose impact was of specific interest. Our final selected set of predictor variables included one class variable, site fertility class, and nine continuous variables: growing stock volume of the spruce, pine, birches and other hardwood, canopy cover, canopy cover of broad-leaved trees, saw timber of other broad-leaved trees than birches, pulpwood volume of the birches, and the biomass of the stem residual of the spruce. The original MS-NFI data recorded at the resolution of 16 × 16 m were resampled to the resolution of 160 × 160 m for the Maxent models, to represent one potential nesting forest stand. The accuracy of Maxent models were assessed with cross-validation and associated averaged AUC-values. The relative importance of the variables was measured by variable contribution and model deterioration measures provided by Maxent. The cloglog-transformed output index values ranging from 0 to 1 described the relative suitability of the 160-m squares to goshawk nesting. Based on the index values, the squares were classified as ‘optimal’ (with index values of 0.69–1.00), ‘typical’ (0.46– <0.69) and ‘poor’ (<0.46). In addition, we divided optimal squares into ‘best’ goshawk squares (index values of 0.92–1.00 corresponding to a high probability of suitable conditions), and ‘good’ goshawk squares (index values ≥ 0.69 and < 0.92). Maxent model outputs Spruce volume was the most important variable in defining habitat suitability for goshawk nesting, but hardwood cover, other hardwood logs and site fertility class contributed also to some extent to habitat suitability. In Maxent outputs, the set of 160-m squares deemed as optimal for goshawk nesting included 6 895 (cover 0.9% of the study area) best goshawk squares and 19 421 (cover 2.5%) good goshawk squares. The projected best and good goshawk squares were mostly located in unprotected areas: 95.0% of the best and 96.0% of the good goshawk squares occurred completely outside protected areas. For further details concerning the data and the model outputs, see the referred article Björklund et al. (2020). State of the optimal goshawk squares In total, 11% of best and over 9% of good goshawk squares were severely altered due to recent harvesting, typically clear-cutting, of the forests during the time period between 2015 and 2019. Altogether, some level of logging occurred in 3 062 (44%) of best goshawk and 9 846 (51%) of good goshawk squares during the recent years. However, many of the squares still included enough unlogged area for the goshawk in 2019. In our article, we conclude that while most of the optimal squares for the goshawk were still preserved in 2019, they are under risk as they are mainly situated outside protected area network. This stresses the importance of conserving biodiversity with complementary measures in privately-owned managed forests. In conclusion, a denser network with more PAs for forest-dwelling species should be secured in areas with intensive forestry, e.g. in southern Finland where PAs currently cover a smaller proportion of land compared to northern Finland.

opencc-by-4.0Dec 2021View details →

Code and data accompanying Palmeirim et al. (2022) Emergent properties of species-habitat networks in an insular forest landscape. Science Advances

Dataset containing species distribution in insular forest fragments at Balbina and full R code for analyses and figures. For deatails, please see the original publication: "Emergent properties of species-habitat networks in an insular forest landscape". Ana Filipa Palmeirim, Carine Emer, Maíra Benchimol, Danielle Storck-Tonon, Anderson S. Bueno, Carlos A. Peres. Science Advances (2022). 10.1126/sciadv.abm0397.

opencc-by-4.0Sep 2022View details →

Data from: Accounting for predator species identity reveals variable relationships between nest predation rate and habitat in a temperate forest songbird

Abstract Nest predation is the primary cause of nest failure in most ground-nesting bird species. Investigations of relationships between nest predation rate and habitat usually pool different predator species. However, such relationships likely depend on the specific predator involved, partly because habitat requirements vary among predator species. Pooling may therefore impair our ability to identify conservation-relevant relationships between nest predation rate and habitat. We investigated predator-specific nest predation rates in the forest-dependent, ground-nesting wood warbler Phylloscopus sibilatrix in relation to forest area and forest edge complexity at two spatial scales, and to the composition of the adjacent habitat matrix. We used camera traps at 559 nests to identify nest predators in five study regions across Europe. When analysing predation data pooled across predator species, nest predation rate was positively related to forest area at the local scale (1,000 m around nest), and higher where proportion of grassland in the adjacent habitat matrix was high but arable land low. Analyses by each predator species revealed variable relationships between nest predation rates and habitat. At the local scale, nest predation by most predators was higher where forest area was large. At the landscape scale (10,000 m around nest), nest predation by buzzards Buteo buteo was high where forest area was small. Predation by pine martens Martes martes was high where edge complexity at the landscape scale was high. Predation by badgers Meles meles was high where the matrix had much grassland but little arable land. Our results suggest that relationships between nest predation rates and habitat can depend on the predator species involved and may differ from analyses disregarding predator identity. Predator-specific nest predation rates, and their relationships to habitat at different spatial scales, should be considered when assessing the impact of habitat change on avian nesting success.

opencc-by-4.0Apr 2023View details →

Dataset (81 forest parcels) supplementing the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)"

The dataset about 81 small-scale private forest parcels contains the answer variables and predictors used in the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)".

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American Goshawk habitat data from nest stands and random points within the Minidoka Ranger District, Sawtooth National Forest, USA

This data supported analysis of American Goshawk (Astur atricapillus) nest stand habitat and was collected within the Minidoka Ranger District of the Sawtooth National Forest in southern Idaho and northern Utah from 2017-2020. The central goal of this research was to develop management tools that demonstrate the utility of conducting analyses at multiple spatial scales as well as using both parametric and machine learning approaches. The stand-level dataset includes variables collected by hand in the field at nest stands and paired random forested sites 300 meters away. It also includes some terrain variables based on remote sensing data. Variables included in the stand-level data table include nest, distance to edge, distance to road, distance to water, division, dominant tree species, canopy closure, Stand Density Index (SDI), Trees per hectare, elevation, slope, Topographic Position Index (TPI), northness, eastness, Diameter at Breast Height (DBH), DBH variance, tree height, tree height variance, and crown depth. We recommend that the stand-level data be used to identify relevant variables and their thresholds for forest managers due to its high resolution. The forest-wide dataset includes only variables collected using various remote sensing datasets at nests and random forested points throghout the Minidoka Ranger District of the Sawtooth National Forest. Variables included in the forest-wide data table include nest, canopy closure, elevation, slope, TPI, northness, eastness, distance to road, distance to water, distance to edge, tree height, and crown depth. We recommend that the forest-wide data be used to identify areas of high suitability for goshawk occupancy across the study area along with sites that could become suitable habitat with management intervention. Latitude and longitude data, while used in our analyses, are excluded from the data tables to protect breeding goshawks from disturbance.

openCC (other)Nov 2024View details →

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Data from: Habitat selection in transformed landscapes and the role of forest remnants and shade coffee in the conservation of resident birds

1. Biodiversity conservation in transformed landscapes is becoming increasingly important. However, most assessments of the value of modified habitats rely heavily on species presence and/or abundance, masking ecological processes such as habitat selection and phenomena like ecological traps, which may render species persistence uncertain. High species richness has been documented in tropical agroforestry systems but comparisons with native habitat remnants generally lack detailed information on species demography and habitat use. 2. We generated a multi-species, multi-measure framework to evaluate the role of habitat selection in the adaptation of species to transformed landscapes, and demonstrate that its use could affect how we value the contribution different land uses make to biodiversity conservation. 3. We analyzed seven years of capture-mark-recapture and observation data for twelve species of resident birds present in native forest remnants and shade coffee plantations in a mega-diverse region. We assessed whether species behaved adaptively by evaluating the correlation between measures of habitat preference (occurrence, abundance, fidelity, inter-seasonal variance and age) and performance (body condition, muscle, primary molt, breeding and juveniles) in forest and coffee, and generated hypotheses about their role in species persistence. 4. We documented adaptive habitat selection for seven species, non-ideal selection for four, and maladaptive selection for one. While many species showed equal-preference and/or equal performance in many traits, in general we found more evidence for birds preferring and/or performing better in forest than coffee, although relationships between our indicators and population adaptation need to be studied further before our proposed framework can be applied to more species and landscapes. 5. While shade coffee can act as a biodiversity-friendly matrix providing complementary or supplementary habitat to a wide range of resident bird species, protecting remnants of native vegetation is still of paramount importance for biodiversity conservation in agricultural landscapes. 28-Aug-2019

opencc-zeroDec 2019View details →

opencc-zeroJul 2016View details →

Data and modeling results for publication: Landscape genetics indicate recently increased habitat fragmentation in African forest-associated chafers

<ul> <li>DNA sequences: cox1 and ITS1 alignments</li> <li>spatial records (in hypervolume archive)</li> <li>spatial principal component 1-3 used for hypervolume models (in hypervolume archive)</li> <li>Present and past species distribution models (SDMs): <ul> <li>biomod2 ensemble SDMs <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li>biomod2 SDMs for single PMIP3 models <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li>hypervolume SDMs</li> </ul> </li> <li>landscape connectivity models <ul> <li>circuitscape (for F0, F1, and F2)</li> <li>least cost corridors and paths (for F0, F1, and F2)</li> </ul> </li> </ul>

opencc-by-4.0Feb 2011View details →

Fig. 1 in Natural-Licks Use By Orangutans And Conservation Of Their Habitats In Bornean Tropical Production Forest

Fig. 1. Location maps of Deramakot Forest Reserve in Sabah, Malaysian Borneo (D1 to D4: natural-licks).

opencc-by-4.0Dec 2021View details →

Fig. 6. Swamp forest habitat where P in Surveys of Afrotemperate forests yields two new freshwater crabs (Decapoda: Potamonautidae: Potamonautes MacLeay, 1838) from South Africa

Fig. 6. Swamp forest habitat where P. mariepskoppie sp. nov., was collected at the Blyde Canyon Nature Reserve, below the Mariepskop Mountains, Mpumalanga Province, South Africa.

Fig. 5 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 5. Average habitat suitability index –HSI– in the upper and lower reach of the riacho São Pedro, for the three fish species.

Fig. 6 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 6. Weighted usable area (WUA) in the upper and lower reach of the riacho São Pedro, for the three fish species.

Fig. 4 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 4. Habitat suitability curves for depth (m), mean water column velocity (m/s) and type of substrate for the three fish species in the riacho São Pedro. Substrate types are; 1- large boulder, 2- boulder, 3- cobble, 4- gravel, 5- fine gravel, 6- sand and 7- vegetation.

Fig. 3 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 3. Frequencies of habitat availability by classes of depth (m), mean velocity (m/s) and type of substrate (1- large boulder; 2- boulder; 3- cobble; 4- gravel; 5- fine gravel; 6- sand and 7- vegetation), obtained by direct survey in the riacho São Pedro.

Fig. 1 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 1. Map of the rio Guandu basin, with indication of the studied reach in riacho São Pedro. Solid dash = small substation of CEDAE (Water and Sewer Treatment Works of Rio de Janeiro State). WTP: Water Treatment Plant.

Fig. 2 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 2. Isometric view of the cross-sections in the two target reaches of the riacho São Pedro. Grey color indicates water; solid line indicates the contour of the cross-section (in wet areas and banks).