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Guest bloggers: Carel IJsselmuiden (COHRED, University of KwaZulu-Natal, South Africa); Bipasha Bhattacharya (COHRED, Co...
18/04/2022

Guest bloggers: Carel IJsselmuiden (COHRED, University of KwaZulu-Natal, South Africa); Bipasha Bhattacharya (COHRED, Corresponding Author, [email protected]), Julia Vallauri and Eric Martin (Institut de recherche pour le développement, France).

In celebration of World Health Day on April 7, 2022, the team behind the Research Fairness Initiative have written a guest blog on their effort to increase fairness in research collaborations across the globe. Although this initiative was inspired by research in global public health, the framework can be applied to any and all research collaborations in order to allow contributors to consider fairness and equity in place in their collaborative projects.

Good health is crucially dependent on research. On good research, on research that is excellent, relevant, ethical and also timely, perhaps. Such research is rarely done by individuals, in isolation, as a garage-based effort – although it could be. In reality, excellent research requires more than individuals – it requires top institutions, supportive environments, financing, supportive legislation and international treaties, rewards and awards, translation opportunities to scalable innovations, and much more. To capture this complexity, we will use the term ‘research systems’ or ‘research and innovation systems’.

Low- and middle-income countries (LMICs) often lack many of these components that are essential to a high-performing research and innovation system. That is no surprise, given that many other sectors in LMICs also lack many of the components that make these sectors work better in high-income environments. In fact, so strong is the generalized perception of under-performing research and innovation systems in LMICs, that even the many current calls for and initiatives to achieve better ‘pandemic preparedness’ rarely mention the need for capable R&D systems in LMICs. And this is in spite of overwhelming evidence that those LMICs with high performing R&D capabilities are not only less affected by ‘vaccine inequity’ but also delivered the largest contributions towards vaccinating the populations of other LMICs.

Capable research, development and innovation systems are a basic requirement for LMICs. They should invest themselves, and the ‘global community’ should support this. This may sound simple, but when looking at the dance floor of international research collaborations, the movements do not recognizably add up to a tango. The mostly divergent, project-based efforts driven by prescriptive (high-income country) funders rather than by national (LMIC) priorities, without clear links to financing the scaling of results, and without systematic efforts to improve tango skills, dancing shoes or the ball-room itself, are more conducive to sore toes, falls, profanities and disappointment than to achieving gradual increases in performance, outputs and outcomes.

‘Development agencies’ and philanthropies supporting LMICs rarely recognize the importance of developing research capabilities in LMICs as a essential to success, especially sustainable success. This applies particularly in global health research as the COVID-19 pandemic demonstrates so clearly.

Given this conundrum – i.e. the need for long-term support for the complex research systems in LMICs combined with the absence of any serious, targeted and long-term funding for this and the often low or absent own investments by LMICs themselves – we believe that the evidence is growing that a continuing effort to improve the fairness and equitability of research partnerships is both essential and catalytic. To go back to the dance floor – would the tango not be immensely more productive and enjoyable if all partners have access to similar skills, training, music or shoes – even if on loan for a while?

The Research Fairness Initiative (RFI) (https://rfi.cohred.org)

The RFI is a direct response to the need for a pragmatic instrument to improve how research and innovation partnerships with low- and middle-income countries (LMICs) can be improved continuously. The RFI is unique. It can generate the transparency and systematic institutional learning required to improve how organisations engage in and manage research and innovation collaborations in a fair and equitable manner for greater impact. While its priority focus was on collaborations between institutions in high and those in low- and middle-income countries, it is clear that the RFI is also appropriate to collaborations between high-income countries.

The RFI elevates research partnerships from ad hoc arrangements between individual researchers to key performance areas for all main actors in research and innovation, in particular:

• Research and Academic institutions
• Government Departments responsible for research and innovation
• National Research and Innovation Agencies
• Research Funders
• Private Sector organizations with a major research and innovation portfolio
• International organizations, large non-profits, and others.

Equitable and fair research collaborations are crucially important to enable LMICs to develop the excellence and sustainability of their research institutions and systems. At this time, the RFI provides the only pragmatic, systematic and global approach to improve the way research collaborations are done – even between high-income institutions themselves.

The RFI has been co-designed through wide and extensive global consultations. Its process can be viewed here : https://rfi.cohred.org/rfi-history/. Its continued improvement is done with all organisations using or supporting the RFI.

The RFI ‘System’ consists of two complementary components:

1. RFI Reporting – biennial institutional self-assessments. The RFI framework of questions and indicators provides a pragmatic tool for institutional self-assessment of the policies and practices used to promote fairness and equitability in their research collaborations. Its focus is forward: ‘How to improve policies and practices in the next 2 years’. Responding to the questions in the RFI Framework often provides a first opportunity for organisations to strategically and systematically assess their own partnership policies, practices and expectations.
2. The RFI Global Learning Platform aggregates and analyses the information provided by institutions in their RFI Reports. Once fully developed, it will provide both real-time and special reports to enhance the evidence base the world of research needs to improve research partnerships and, where possible, reach global agreements on standards or benchmarks.

For full certification, organisations have to publish their RFI reports on their own, corporate websites AND enable a comment function for readers.

Once complete, the RFI website will republish these reports and encourage further comments that will remain anonymous to the organisation. In this way, the RFI System should become a global platform for learning and action.

18/04/2022

The report was jointly issued by the Hurun Research Institute and the wealth management platform, Yi Tsai. It looked into the number of wealthy families in China across 100 geographical regions.

Contemporary surveysContemporary field surveys were conducted similarly to the historical ones performed by ENV. We used...
16/04/2022

Contemporary surveys
Contemporary field surveys were conducted similarly to the historical ones performed by ENV. We used the same field protocols with a few modifications. The surveys were shortened to start 30 minutes before sunrise to aid visibility and to ensure that birds were attending leks. In our first few surveys, birds at many leks did not become active until around 45 minutes before sunrise, with males continuing to arrive over the next few minutes. Flush counts were not conducted as we did not want to disturb the grouse, and in turn it reduced our time spent at each lek and allowed us to cover a larger geographic area. We began our surveys on March 26th in 2018 and March 25th in 2019 and continued until June 1st for both years. We had two teams, each of which surveyed each block once, for a total of two surveys per block. We surveyed both historical (18 in 2018 and 14 in 2019) and new survey blocks (10 in 2018 and 22 in 2019) for a total of 57 blocks surveyed. Surveys initially covered as many accessible roads as possible within each block; the second set of surveys was used to resurvey identified leks to confirm occupancy and counts.

We conducted driving surveys stopping every 1.6 km to look and listen for lekking grouse. The low cooing calls males make on the lek can be heard from up to four km [22], with the farthest lek heard during our field seasons being approximately 2.6 km away from a stop point. We also continued to look for birds between stops. At every stop, we recorded the location (GPS point), any signs of sharp-tailed grouse (leks, calls, or birds observed), and the surrounding habitat (native grassland, tame grassland, or cropland). When a lek was located we observed it for a minimum of ten minutes to ensure all birds were counted. In addition, we also recorded the behaviour of the grouse (dancing, calling, or inactive) and the habitat surrounding the lek.

Other lek location information
Additional lek locations were supplied by the Saskatchewan Conservation Data Centre (CDC; 1995–2016) and the Saskatchewan Ministry of Environment’s Co-operative Wildlife Management Survey App (CWMS; 2017–2019). These additional tools allowed citizen scientists and environmental contractors to report lek locations to us across our study area. The CDC quality assures and controls observational data using NatureServe protocols [23]. To ensure the quality of the CWMS leks, all records we used were limited to those made in April and May mornings and where dancing behaviour was recorded. By adding additional lek locations from these programs we acquired a better geographic representation of lek locations (i.e., lek habitats and surrounding landscape composition) across our large study area for training the model. In addition to the datasets that we used for model training purposes, we also gathered independent data from eBird on sharp-tailed grouse leks in Saskatchewan. eBird data was filtered to match datasets used above: observations had to occur in the last five years (2017–2021), ≥ 3 grouse were observed, and observations were limited to April and May.

Habitat suitability index
We used MaxEnt to create the Habitat Suitability Index for sharp-tailed grouse in Saskatchewan. This approach is typically competitive or outperforms other modeling methods for predictive accuracy when using presence-only data [24, 25]. Our data partially consisted of standardized presence and absence surveys (i.e., historical and contemporary surveys); however, incorporation of the citizen-science presence-only data necessitated the use of this type of analysis. MaxEnt requires two primary pieces of data to create the habitat model: known occurrence data for leks, and rasterized maps of environmental predictor variables (habitat features; see below). Due to potential changes in land use over time, we limited use of historical lek data from the ENV surveys to the period since 1990. This decision was made to produce the best possible balance between retaining lek data, and matching the years represented by the environmental predictor layers (see below). Field data from 2018–2019, as well as lek occurrence information from the CDC (1995–2016) and CWMS app (2017–2019) were also used. We only included leks in our analyses if there were three or more males displaying (dancing, calling, territorial displays, or mating; [26]). As lek locations were used for the presence data in the model, the model is more correctly an index of lek occurrence and not necessarily reflective of sharp-tailed grouse habitat throughout the yearly life cycle (e.g., brood-rearing, wintering habitats). Although sharp-tailed grouse occupy all of Saskatchewan, we limited the background (geographical range) for MaxEnt modeling to the prairie ecoregion of Saskatchewan (240,967 km2) given that this was where all of our lek location data came from. The Maxent algorithm compares locations of where a species is present to 10,000 random samples of available habitat within the geographical range we defined above.

Environmental predictor variables
Two types of environmental predictor layers were created for use in our model: proportions of various landcovers and a terrain roughness index. All the layers had a pixel size of 1 km x 1 km. This resolution was chosen so that our results could be incorporated and matched to current large-scale mapping and management efforts in the province of Saskatchewan and because some lek locations had only a quarter-section identification rather than an exact GPS location. The proportion of landcover layers were created using 2010 land use maps created by Agriculture and Agri-Food Canada [27], which was the most recently available and reliable landcover layer for the province of Saskatchewan. These land use layers have a 30-m resolution, so it should be noted that smaller features, such as power poles and smaller patches of habitat, are not mapped by this product. In ArcMap 10.6 (ESRI), we converted this raster layer to vector format, a 1-km x 1-km grid was then overlaid on the resulting shapefile, and the proportions (0–1) of each landcover type (Fig 1) within each 1- x 1-km pixel were calculated using the Tabulate Intersection tool (Fig 1). We also calculated the proportion of each landcover type within a 5 x 5-km square with the lek at its center (i.e., roughly equivalent to the 2-km radius breeding complex [11]; Fig 1). Grasslands are a landcover dominated by graminoids and forbs. The amount of grassland in the landscape is inversely correlated with cropland and so we selected grassland to be included in our modelling rather than both dominant landcovers. Shrub landcover is typically woody vegetation ≤2-m tall and the tree landcover represents areas with vegetation ≥2-m tall. The tree landcover is typically limited to river valleys and some small forested areas, representing 5% human disturbance. In a review of the effect of anthropogenic structures on various grouse species, lek site persistence was the portion of grouse life history most affected by anthropogenic structures [6]. More recent studies have also shown an avoidance by prairie grouse of developed areas [43]. Sharp-tailed grouse show high lek fidelity and as such may not ever fully abandon lek sites; however, their populations may decline over time at disturbed sites due to lower recruitment [51]. Alternatively, anthropogenic features may aid sharp-tailed grouse nest success by excluding potential predators displaced by the development [52]. Further research on specific anthropogenic features and their effects on sharp-tailed grouse lek occurrence is still needed.

There are several habitat features that could help to refine our modelling of sharp-tailed grouse lek occurrence. For example, we did not include vegetation height in our models even though it is typically important in many sharp-tailed grouse habitat suitability models [20, 53, 54]. Unfortunately, acquiring information on vegetation height is impractical at the province-wide scale and can be highly variable over time depending on land use and weather conditions. Finer-resolution environmental predictor layers would have also allowed us to also assess smaller habitat patches (e.g., shrub patches that might be smaller than the initial 30-m resolution of our landcover layer) and features (e.g., small roads, wells, powerlines) that have been demonstrated to be important in previous sharp-tailed grouse habitat selection studies [52]. This could explain why shrub cover, which is a key component of several sharp-tailed grouse habitat models [20, 55, 56] was less important in our models. Many of the shrub patches with lower height and density do not appear in the landcover layers we used and to our knowledge there are no other suitable shrub mapping products available in Saskatchewan.

Management implications
The sharp-tailed grouse is a vital game bird in many jurisdictions, and ensuring that the necessary habitat is available to support it is integral to ensuring sustainable hunting opportunities [8, 57]. To do this effectively, wildlife managers must have access to tools that identify preferred and high quality grouse habitats [57]. Our lek occurrence model highlights the importance of maintaining large diverse grasslands for management of sharp-tailed grouse. In addition, within these large and intact grassland landscapes it is essential to maintain other various natural features. An area managed for sharp-tailed grouse and concurrent human use according to our model would need to retain a high proportion of grasslands (approximately 0.80), while limiting the amount of anthropogenic influences to less than 5% of this landscape. Using this HSI model, wildlife managers will be able to better predict sharp-tailed grouse habitat use and target conservation efforts to areas with high potential for the occurrence of breeding complexes, thereby more effectively managing the species at a large scale. Given their economic and cultural value [8], our research confirms that sharp-tailed grouse could be useful as an umbrella species for protecting northern grasslands and other species of conservation concern [58, 59] due to their concurrent need for relatively large patches of intact grasslands. However, future research should examine how habitat change could lead to changes in lek persistence or disappearance, and how the landscape features we identified as necessary for lek occurrence are related to habitat quality (survival, reproductive success) outside of the lekking season.

Acknowledgments
We would like to thank numerous field assistants for their work. While most of our surveys were road based, we would like to thank the numerous landowners that allowed us access to their land and for accommodations during the field season. We would also like to thank Mark Brigham, Ryan Brook, and two anonymous reviewers that provided helpful comments on earlier drafts of our manuscript.

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