I know that it has been a while since I posted anything here. The daily responsibilities and effort required for my PhD program are taking quite a toll on the time I have available for other non-phd matters (for instance curating this blog). I apologize for this and hope to post some more tutorials and discussion post in the future. However at the moment my personal research reserved 105% of my available time. But the scientific blogosphere is generally in a bit of a crisis I heard.
Anyway, today I just want to quickly share the exciting news that my MSc thesis I conducted at the Center for Macroecology, Evolution and Climate has passed scientific peer review and is now in early view in Animal Conservation. I am quite proud of this work as it represents the first lead-author paper I managed to publish that involved primary research and data collection.
Short breakdown: During my masters and also now in my PhD I am extensively working with the PREDICTS database, which is a global project aiming at collating local biodiversity estimates in different land-use systems across the entire world. The idea for this work came as I realized that many of the categories in the PREDICTS database are affected by some level of subjectivity. Local factors – such as specific land-use forms, vegetation conditions and species assemblage composition – could alter general responses of biodiversity to land use that have been generalized across larger scales. Thus the simple idea was to compare ‘PREDICTS-style’ model predictions with independent biodiversity estimates raised at the same local scale. But see abstract and paper below.
Jung et al (2016) – Local factors mediate the response of biodiversity to land use on two African mountains
Land-use change is the single biggest driver of biodiversity loss in the tropics. Biodiversity models can be useful tools to inform policymakers and conservationists of the likely response of species to anthropogenic pressures, including land-use change. However, such models generalize biodiversity responses across wide areas and many taxa, potentially missing important characteristics of particular sites or clades. Comparisons of biodiversity models with independently collected field data can help us understand the local factors that mediate broad-scale responses. We collected independent bird occurrence and abundance data along two elevational transects in Mount Kilimanjaro, Tanzania and the Taita Hills, Kenya. We estimated the local response to land use and compared our estimates with modelled local responses based on a large database of many different taxa across Africa. To identify the local factors mediating responses to land use, we compared environmental and species assemblage information between sites in the independent and African-wide datasets. Bird species richness and abundance responses to land use in the independent data followed similar trends as suggested by the African-wide biodiversity model, however the land-use classification was too coarse to capture fully the variability introduced by local agricultural management practices. A comparison of assemblage characteristics showed that the sites on Kilimanjaro and the Taita Hills had higher proportions of forest specialists in croplands compared to the Africa-wide average. Local human population density, forest cover and vegetation greenness also differed significantly between the independent and Africa-wide datasets. Biodiversity models including those variables performed better, particularly in croplands, but still could not accurately predict the magnitude of local species responses to most land uses, probably because local features of the land management are still missed. Overall, our study demonstrates that local factors mediate biodiversity responses to land use and cautions against applying biodiversity models to local contexts without prior knowledge of which factors are locally relevant.
Since quite some time ecological models have tried to incorporate both continuous and discrete characteristics of species into their models. Newbold et al. (2013) demonstrated that functional traits affect the response of tropical bird species towards land-use intensity. Tropical forest specialist birds seem to decrease globally in probability of presence and abundance in more intensively used forests. This patterns extends to many taxonomic groups and the worldwide decline of “specialist species” has been noted before by Clavel et al. (2011).
But how to acquire such data on habitat specialization? Ether you assemble your own exhaustive trait database or you query information from some of the openly available data sources. One could for instance be the IUCN redlist, which not only has expert-validated data on a species current threat status, but also on population size and also on a species habitat preference. Here IUCN follows its own habitat classification scheme ( http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3 ). The curious ecologist and conservationist should keep in mind however, that not all species are currently assessed by IUCN.
There are already a lot of scripts available on the net from which you can get inspiration on how to query the IUCN redlist (Kay Cichini from the biobucket explored this already in 2012 ). Even better: Someone actually compiled a whole r-package called letsR full of web-scraping functions to access the IUCN redlist. Here is some example code for Perrin’s Bushshrike, a tropical bird quite common in central Africa
# Install package install.packages(letsR) library(letsR) # Perrin's or Four-colored Bushshrike latin name name <- 'Telophorus viridis' # Query IUCN status lets.iucn(name) #>Species Family Status Criteria Population Description_Year #>Telophorus viridis MALACONOTIDAE LC Stable 1817 #>Country #>Angola, Congo, The Democratic Republic of the Congo, Gabon, Zambia # Or you can query habitat information lets.iucn.ha(name) #>Species Forest Savanna Shrubland Grassland Wetlands Rocky areas Caves and Subterranean Habitats #>Telophorus viridis 1 1 1 0 0 0 0 #> Desert Marine Neritic Marine Oceanic Marine Deep Ocean Floor Marine Intertidal Marine Coastal/Supratidal #> 0 0 0 0 0 0 #> Artificial/Terrestrial Artificial/Aquatic Introduced Vegetation Other Unknown #> 1 0 0 0 0
letsR also has other methods to work with the spatial data that IUCN provides ( http://www.iucnredlist.org/technical-documents/spatial-data ), so definitely take a look. It works by querying the IUCN redlist api for the species id (http://api.iucnredlist.org/go/Telophorus-viridis). Sadly the habitat function does only return the information if a species is known to occur in a given habitat, but not if it is of major importance for a particular species (so if for instance a Species is known to be a “forest-specialist” ). Telophorus viridis for instance also occurs in savannah and occasionally artificial habitats like gardens ( http://www.iucnredlist.org/details/classify/22707695/0 ).
So I just programmed my own function to assess if forest habitat is of major importance to a given species. It takes a IUCN species id as input and returns ether “Forest-specialist”, if forest habitat is of major importance to a species, “Forest-associated” if a species is just known to occur in forest or “Other Habitats” if a species does not occur in forests at all. The function works be cleverly querying the IUCN redlist and breaking up the HTML structure at given intervals that indicate a new habitat type.
Find the function on gist.github (Strangely WordPress doesn’t include them as they promised)
How does it work? You first enter the species IUCN redlist id. It is in the url after you have queried a given species name. Alternatively you could also download the whole IUCN classification table and match your species name against it 😉 Find it here. Then simply execute the function with the code.
name = 'Telophorus viridis' data <- read.csv('all.csv') # This returns the species id data$Red.List.Species.ID[which(data$Scientific.Name==name)] #> 22707695 # Then simply run my function isForestSpecialist(22707695) #> 'Forest-specialist'
The start of this year was marked by the publication of two new global datasets for environmental analysis. My impression is that both of those datasets will be of increasing importance in ecological analysis in the future (even though their value for conservation biology has been actively criticized, see Tropek et al. 2014). Thus there is a need to assess the accuracy of their forest loss detection over time and if they are consistent.
The first dataset is the already famous Global Forest Map published by Hansen et al. (2013) in Science end of last year. The temporal span of their dataset goes back from the year 2000 up to the year 2012 and by using only Landsat data in a temporal time-series analysis they got a pretty decent high-resolution land-cover product. Although the resolution of the Hansen dataset is great (30m global average coming from Landsat) Hansen et al. decided to only publish the year 2000 baseline with the forest cover. They provide us with aggregated loss, gain and loss per year layers though, but nevertheless the user has no option to reproduce a similar product for the year 2012.
The other dataset is the combined published result from a 4 year long monitoring by the japanese satellite ALOS-PALSAR. They decided to release a global forest cover map at a 50m spatial resolution, which in contrast to Hansen can be acquired for the whole time-frame of the ALOS-PALSAR mission. It thus has a temporal coverage of the whole globe from the year 2007 until 2010. The data can be acquired on their homepage after getting an account. The ALOS PALSAR data has a nice temporal span and can be downloaded for multiple years, thus in theory allowing to make temporal comparisons and predictions about future land-use trends. However I am a bit concerned about the accuracy of their classifications as I have found multiple errors already in the area I am working in.
Because I am interested in using the ALOS PALSAR dataset in my analysis (how often do you get a nice spatial-temporal dataset of forest cover) I made a comparison between the forest loss detected in my area of interest for both datasets. It should be noted that is a comparison between different satellite sensors as well and not only by classification algorithms. So we are not comparing products from the same data source.
So what is the plan for our comparison:
- We downloaded the whole ALOS PALSAR layers for all years covered of the area around Kilimanjaro in northern Tanzania (N00, E035). We then extracted only the forest cover (Value == 1) and calculate the difference between years to acquire the forest loss for the year 2008,2009 and 2010 respectively.
- From the Google Engine app we downloaded the “loss per year” dataset and cropped it to our area of interest. Furthermore we are only interested in the aggregated Forest loss in the years 2008, 2009 and 2010 which we have available in the ALOS PALSAR dataset. We furthermore resampled the Hansen dataset up to 50m to match up with the ALOS PALSAR resolution.
I haven’t found a fancy way to display this simple comparison, so here comes just the result table. As predicted (if you look at it visually),the ALOS PALSAR algorithm overshots the amount of forest loss a lot.
|Hansen Forest Loss cells||262||304||529|
|ALOS PALSAR Forest Loss cells||26995||24970||16297|
|Equal cells in both||17||30||131|
So which one is right? I personally trust Hansens data a lot more. Especially because I found them to be pretty consistent in my area of study. For me the ALOS PALSAR data is not useable yet until the authors have figured out ways to improve their classification. It can be concluded that users should not forget that those Forest Cover products are ultimately just the result of a big un-supervised algorithm who doesn’t discriminate between right and wrong. Without validation and careful consideration of the observer you might end up having wrong results.
M. Shimada, O. Isoguchi, T. Tadono, and K. Isono, “PALSAR Radiometric and Geometric Calibration,” IEEE Trans. GRS, vol. 47, no. 12, pp.3915-3932, Dec 2009.
M. Shimada and T. Otaki, “Generating Continent-scale High-quality SAR Mosaic Datasets: Application to PALSAR Data for Global Monitoring,” IEEE JSTARS Special Issue on Kyoto and Carbon Initiative, vol. 3, Issue 4, 2010, pp.637-656.
Shimada, M.; Isoguchi, O.; Motooka, T.; Shiraishi, T.; Mukaida, A.; Okumura, H.; Otaki, T.; Itoh, T., “Generation of 10m resolution PALSAR and JERS-SAR mosaic and forest/non-forest maps for forest carbon tracking,” Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International , vol., no., pp.3510,3513, 24-29 July 2011
Another interesting project closely related to PREDICTS is the BIOFRAG Project, which tries to construct a global database of research papers dealing with Forest Fragmentation and its impacts on Biodiversity taxa. One final goal of the BIOFRAG project is the development of a new fragmentation index using watersheds delineation algorithm and fragment descriptors in order to characterize Fragment traits. I am very interested in seeing the final outcome of this approach and maybe I even find the time to implement their algorithm in LecoS for QGIS as soon as it is released. Their database paper, lead authored by Marion Pfeifer, was just released to the public as open-access paper. You can read it in full here.
If you consider of contributing data then more information can be found on the BIOFRAG blog and all researchers involved with forest fragmentation research should consider contributing to them and also to PREDICTS (see here) if you haven’t already done so. And as usual: If you were studying in Africa, then please get in touch with me! I will contact you as soon as I return from my Fieldwork in Kenya and Tanzania at the end of May.
And here are some news from my current field work that is part of my Thesis. After spending some quiet, but exiting days in Nairobi (maybe later more about that) I finally arrived in Wundanyi, Taita Hills, where a substantial part of my work will be conducted along the CHIESA transect. Suited in the coastel area in proximity to Mombasa the Taita Hills are renown for their extraordinary bird diversity and endemic species and as such are considered to be part of the Eastern Arc Mountains Diversity hotspot. The Taita hills encompass a variety of different land-use forms, but the majority of them surely are tropical homegardens as most of the “Taita” people are subsistence farmers growing crops in the highly fertile soil of the mountain slopes. Besides homegardens there are riverine forests in the valleys, shrubland vegetation in the lower altitudes, exotic tree plantations and of course the remaining indigenous forests remaining on the Taita hills mountain tops. Every last forest part is known well and was traditionally protected by the locals as part of their culture. However in the later centuries the remaining forest area became more and more scarcer and even during my visits in some of the forest fragments with the highest biodiversity value (Chawia, Ngangao) I saw frequent signs of fuelwood and timber extraction. Clearly a lack of funding for biodiversity protection seems to be the problem, but also an economic perspective and opportunities such as ecotourism might enhance locals perception if and how these last forest parts should be protected.
My work in the Taita hills is all about birds. Specifically I am conducting avian diversity and abundance assessments along an altitudinal transect encompassing a variety of different land-use systems. Although avian assessments have been conducted in Taita many times before, they were often restricted to the forest fragments and for instance didn’t look at the bird diversity in homegardens in different altitudes. The resulting data will just be used for my thesis as validation dataset, but I am hoping that it has maybe some value on its own as well. Initial results show that especially the homegarden in Taita support quite a high diversity of birds, which is even similar to levels in the remaining forest fragments (although the community is somewhat different and biotic homogenization is likely on-going).
It can be quite challenging to conduct avian research in tropical human-dominated landscapes. Not only do you have to arrange for transport to the specific transect areas and lodging (in my case provided by the University of Helsinki Research station in Wundanyi), but also account for the frequent interruption by children and farmers asking what you are doing. Furthermore it is not an easy task to count birds in for instance a maize or sugarcane plantation due to the limited accessibility and my intention not to damage the farmers crops. Most of the farmers however happily provide access to their land and are very interested in what kind of research this “Mzungu” is doing on their farm. From my own experience here I can tell that the Taita people are very kind and it is a pleasure to work with them on their land. They are very respectful and even walking around late at night or very early in the morning seems to be no problem here (in contrast to for instance Nairobi or Mombasa).
In the end my sampling goes on quite well and much better than I expected. Although it is technically raining season and long heavy rains can be expected every day, the mornings were exceptionally dry and weather was mostly favourable for ornithological research. Generally this time of the year in East Africa is especially interesting for bird assessments as many local bird species are in their breeding plumage and nesting, but also because European migrants are often still around or on their way back to Europe (for instance I saw and heard an European Willow Warbler some days ago). Lets see what else the next weeks will have for be in terms of avian diversity.
Just some moments ago (on the 15th of November 2013, so not yet in Europe 😀 ) a new very interesting paper came out in Science presenting a new High-resolution dataset of global forest loss in the last decade. It is written by the authors of already existing widely used datasets, such as GLCF and CARPE, in corporation with Google.
“In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year.”
Not surprisingly only the tropical rain-forest was found to posses a negative trend in the last decade. Most likely because of shifting agriculture, clash-and-burn practices and large scale deforestations due to monoculture cultivation of Palmoil, Soy and Jatropha. Quite impressively they used over 654,178 Landsat images in a time-series analysis to in characterize forest extent and change in the time from 2000 to 2012. Google apparently helped out with the computation (cloud-computing). And the best thing is: You can view the data
online via Google.
At the bottom you can find a Data download link, which makes the whole thing very interesting for follow-up research! Check it out!
EDIT: Apparently download is not yet available for the public. Will keep you updated!