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.
A quick post to highlight a new publication in this weeks issue of Current Biology. Edwards et al. went for another piece on the land-sharing/land-sparing debate and presented a very nice case study. Land-sharing is often defined as combining “sustainable” agricultural production with higher biodiversity outcomes often at the tradeoff of harvesting less and loss of natural habitats. Land-sparing on the other hand attempts to prevent remaining natural habitat from being used by humans, but instead intensify production and increase yield from other areas, thus reducing their potential for wildlife-friendly farming. They combined field work from the Choco-andres region (Taxonomic focus: Birds) with simulation models to investigate which strategy might benefit biodiversity the most. Contrary to many other previous publications they focused on phylogenetic richness (PD) rather than “species richness”. Based on landscape simulation models they could show that PD decreases steadily with greater distance to forests, which is interesting because it demonstrates that land-sharing strategies might only be successful, if sufficient amounts of natural habitat are in close proximity, that can act as source habitat for dispersing species.
According to their analysis some species seem to benefit more from land-sparing strategies than others. Specific evolutionary traits thus might be ether beneficial or detrimental for surviving in intensive human land use such as agriculture. They conclude that land-sharing might be of limited benefit without the simultaneous protection of nearby blocks of natural habitat, which can only be achieved with a co-occurring land-sharing strategy.
There are many interesting things to calculate in relation to landscape ecology and its statistical metrics. However many (if not the majority) of the published toolsets are not reproducible, their algorithm code not published or open-source. Obviously this makes the easy implementation of underlying algorithms even harder for independent developers (scientists) if you don’t have the time to reproduce their work (not to mention the danger of making stupid mistakes, we are all human).
I recently found this new article in Methods in Ecology and Evolution by Etherington et al., who didn’t really present any novel techniques or methods, but instead provided a new python library that is capable of calculating Neutral Landscape Models (NLMs). NLMs are often used as nullmodel counterpart to real remote-sensing derived maps (land-cover or altitude) to test the effect of landscape structure or heterogeneity on a species (-community). Many NLM algorithms are based on cluster techniques, cellular automata or calculating randomly distributed numbers in a given 2d space. There have been critical and considerate voices stating that existing NLMS are often misused and better null-models are needed for specific hypothesis, such as a species perception of landscape structures. Nevertheless NLMs are still actively used and new papers published with it.
The new library, called NLMpy, is open source and published under the MIT licence. Thus I can easily use and integrate into QGIS and its processing framework. Their NLMpy library only depends on numpy and scipy and thus doesn’t add any other dependency to your python setup, if you already are able to run LecoS in your QGIS installation. The NLM functions are visible in the new LecoS 1.9.6 version, but only if you have NLMpy installed and it is available in your python path. Otherwise they won’t show up! Please don’t ask me here how to install additional python libraries on your machine, but rather consult google or some of the Q&A sites. I installed it following the instructions on this page.
After you have installed it and upgraded your LecoS version within QGIS, you should be able to spot a new processing group and a number of new algorithms. Here are some screenshots that show the new algorithms and two NLMs that I calculated. The first one is based on a Midpoint displacement algorithm and could be for instance used to test against an altitude raster layer (need to reclassify to real altitude values first). The second one is aimed at emulating a random classified land-cover map. Here I first calculated a proportional NLM using a random cluster nearest-neighbour algorithm. Second I used the libraries reclassify function (“Classify proportional Raster”) to convert the proportional values (range 0-1) into a relative number of landcover classes with exactly 6 different land-cover classes. Both null model look rather realistic, don’t they 😉
This is a quick and dirty implementation, so there could occur some errors. You should use a meter-based projection as extent (such as UTM) as negative values (common in degree-based projections like WGS84 latitude-longitude) sometimes result in strange error messages. You also have to change the CRS of the generated result to the one of your project manually, otherwise you likely won’t see the result. Instead of the number of rows and columns as in the original implementation, the functions in LecoS are based on a selected extent and the desired output cellsize.
For more complex modelling tasks I would suggest that you use the library directly. To give you a good start Etherington et al. also appended some example code and data in their article´s supporting information. Furthermore a few days ago they even had a spatial MEE blog post with some youtube video demonstrations how to use their library. So it shouldn’t be that hard even for python beginners. Or you could just use the processing interface within LecoS.
In any way, if you use the library in your research, I guess the authors would really appreciate it if you could cite them 🙂
- Etherington, T. R., Holland, E. P., O’Sullivan, D. (2014), NLMpy: a python software package for the creation of neutral landscape models within a general numerical framework. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12308
In addition I also temporarily removed LecoS ability to calculate the mean patch distance metric due to some unknown errors in the calculation. I’m kinda stuck here and anyone who can spot the (maybe obvious) bug gets a virtual hug from me!
Happy new year!
Following current papers and newly proposed methods is always exciting. Especially when someone proposes a new technique for conservation planning, which
includes the habitat fragmentation caused by roads. The decrease of road-less areas in Europe certainly has an impact on a wide range of species (Selva et al. 2011), especially if they inhabit large home-ranges and frequently move between for instance forest patches. To give an example: Conservation NGOs in Germany are currently celebrating the return of wolves in east German forests. While this is certainly a good thing it requires a lot of future management actions (possible compensation of farmers, education of the public, …) and furthermore the high fragmentation of east German landscapes might also alter wolf behavior and migration. Many other animals are also affected by fast moving traffic (see the picture of the poor frog) and therefore i am certain that roads will become more and more important in future conservation area prioritizations and landscape planing.
In their recent paper Freudenberger et al (2013) shed light on the current fragmentation of an east German state and introduce a new landscape metric called the spatial road disturbance index (SPROADI). The SPROADI is an aggregated index, which integrates the (1) traffic intensity on the habitat intersecting road, (2) density and distribution of roads in a landscape and (3) overall fragmentation caused by roads. Mathematically the new index is just a weighted sum of the three equally weighted subindices, which were previously categorized using available quantitative data. While this is certainly not perfect (data maybe biased and thus has to be normalized beforehand, correlations with subindices) i think that due to its simplicity and easy understanding it might become a handy index for landscape planners and biologists conducting impact assessments for roads. Although i am not entirely convinced by the mathematics behind this new index (next rainy sunday i will put some thoughts behind it) i recommend interested people to give their paper a try.
- Freudenberger et al. “Spatial road disturbance index (SPROADI) for conservation planning: a novel landscape index, demonstrated for the State of Brandenburg, Germany”. Landscape Ecology (2013): 1-17. DOI: http://dx.doi.org/10.1007/s10980-013-9887-8
Selva, Nuria, et al. “Roadless and low-traffic areas as conservation targets in Europe.” Environmental management 48.5 (2011): 865-877.